Methods and systems for planning, predicting, and monitoring therapies for pulmonary diseases

Abstract

Methods for planning, predictive modeling, and monitoring therapies for pulmonary diseases are provided. In some embodiments, a method for planning a treatment for a patient having a pulmonary disease includes receiving patient data including computed tomography (CT) data of a lung of the patient. The method can include generating a set of lung metrics by inputting the patient data into a first machine learning algorithm. The method can also include predicting a response of the patient to treatment for the pulmonary disease by inputting the set of lung metrics into a second machine learning algorithm. The method can further include evaluating whether the patient is a candidate for the treatment for the pulmonary disease, based on the predicted response. The method can further include evaluating whether the patient has benefited following treatment and whether additional treatment is warranted.

Description

METHODS AND SYSTEMS FOR PLANNING, PREDICTING, AND MONITORING THERAPIES FOR PULMONARY DISEASESCROSS-REFERENCE TO RELATED APPLICATION(S)[00011 This application claims the benefit of priority to U.S. Patent Application No. 63 / 667,471, filed July 3, 2024, and U.S. Patent Application No. 63 / 807,495, filed May 16, 2025, each of which is incorporated herein by reference in its entirety.|0002] This application is related to International Application No. PCT / US2023 / 086498, filed December 29, 2023, which is incorporated by reference herein in its entirety.TECHNICAL FIELD

[0093] The present technology7generally relates to treatment planning, and in particular, to methods and sy stems for planning, predicting, and monitoring therapies for pulmonary diseases.BACKGROUND[0004| Chronic obstructive pulmonary disorder (COPD) is a disease of impaired lung function. Symptoms of COPD include coughing, wheezing, shortness of breath, and chest tightness. Cigarette smoking is the leading cause of COPD, but long-term exposure to other lung irritants (e.g., air pollution, chemical fumes, dust) may also cause or contribute to COPD. In most cases, COPD is a progressive disease that worsens over the course of many years. Accordingly, many people have COPD, but are unaware of its progression. COPD is currently a major cause of death and disability in the United States. Severe COPD may prevent a patient from performing even basic activities such as walking, climbing stairs, or bathing. Unfortunately, there is no known cure for COPD. Nor are there known medical techniques capable of reversing the pulmonary damage associated with COPD. Conventional approaches to treating COPD are associated with serious complications, have limited effectiveness, are only suitable for a small percentage of COPD patients, and / or have other significant disadvantages. Given the prevalence of the disease and the inadequacy of conventional treatments, there is a great need for innovation in this field.SUMMARY[0005| The subject technology is illustrated, for example, according to various aspects described below, including with reference to FIGS. 1-28. Various examples of aspects of the subject technology are described as numbered clauses (Al, A2, A3, etc.) for convenience. These are provided as examples and do not limit the subject technology.Al . A method for planning a treatment for a patient having a pulmonary disease, the method comprising: receiving patient data including computed tomography (CT) data of a lung of the patient; generating a set of lung metrics by inputting the patient data into a first machine learning algorithm, wherein the set of lung metrics represents a state of the lung of the patient; predicting a response of the patient to a treatment for the pulmonary disease by inputting the set of lung metrics into a second machine learning algorithm; and evaluating whether the patient is a candidate for the treatment for the pulmonary disease, based on the predicted response.A2. The method of clause Al, wherein the patient data comprises one or more of the following: questionnaire information, medical record information, magnetic resonance imaging (MRI) data, single-photon emission computed tomography (SPECT) data, bronchoscopy data, ventilation-perfusion data, pulmonary function test data, chest radiography data, fluoroscopy data, photographs, or sensor data.A3. The method of clause Al or A2, wherein the CT data comprises expiratory CT data.A4. The method of clause A3, wherein the CT data comprises inspiratory CT data.A5. The method of any one of clauses A1-A4, wherein the patient data comprises data obtained at a plurality of different time points.A6. The method of any one of clauses A1-A5, wherein the set of lung metrics correlates to whether the patient has at least one of chronic obstructive pulmonary disease(COPD), severe emphysema, or severe emphysema with hyperinflation.A7. The method of any one of clauses A1-A6, wherein the set of lung metrics characterizes one or more lung parameters, the one or more lung parameters comprising one or more of the following: forced expiratory' volume in 1 second, forced vital capacity, vital capacity, inspiratory capacity, inspiratory capacity / total lung capacity ratio, functional residual capacity, total lung capacity, diffusion capacity for carbon monoxide, residual volume, residual volume / total lung capacity ratio, closing volume, lobar and / or segmental tissue destruction, lobar and / or segmental air trapping, lobar and / or segmental fissure status, extent of lobar and / or segmental fissure completion, lobar and / or segmental ventilation, lung function, homogeneity / heterogeneity of lobar and / or segmental emphysema, emphysema ty pe, locations of diseased portions of the lung, lobar volume, segmental volume, segmental locations, diaphragm shape, tissue density, opacity, proximity of diseased portions to anatomical structures, proximity of diseased portions to other medical devices, lumen inner diameter of bronchial sections, air flow mapping, collapsed airways, airway pressure, airwaycompliance, pleural pressure, airway diameter to wall thickness, airway wall deformation, vascular perfusion, parenchyma density, bullae, information that localizes disease in the lung, obstruction score, mucus score, or degree of epithelialization.A8. The method of any one of clauses A1-A7, wherein the set of lung metrics comprises at least one disease score characterizing severity of pulmonary7disease in the patient.A9. The method of clause A8, wherein the at least one disease score represents a predictor of patient response to the treatment of the pulmonary disease.A10. The method of clause A8 or A9, wherein the set of lung metrics comprises multiple disease scores each corresponding to a respective lobar, segmental, or sub-segmental region of the lung of the patient.Al l. The method of clause A8 or A9, wherein the set of lung metrics comprises a single disease score based on multiple local disease scores each corresponding to a respective lobar, segmental, or sub-segmental region of the lung of the patient.A12. The method of clause All, wherein the single disease score is an average of the multiple local disease scores.A13. The method of any one of clauses A8-A12, wherein the at least one disease score represents an extent of at least one of air trapping or hyperinflation in the lung of the patient.A14. The method of any one of clauses A7-A13, wherein the set of lung metrics characterizes a change in at least one of the one or more lung parameters over a plurality of time points.A15. The method of clause A14, wherein the plurality of time points comprise two or more of the following: before endobronchial implant therapy, after endobronchial implant therapy, before administration of a bronchodilator, after administration of a bronchodilator, before exercise, or during exercise.A16. The method of any one of clauses A1-A15, wherein the predicted response comprises a prediction of one or more of the following: forced expiratory volume in 1 second, forced vital capacity, vital capacity, inspiratory capacity, inspiratory capacity / total lung capacity' ratio, functional residual capacity, total lung capacity, diffusion capacity for carbon monoxide, residual volume, residual volume / total lung capacity ratio, segmental volume, mMRC score, SGRQ score or a subset thereof, CAT score or a subset thereof. 6- minute walk test results, cycle ergometry results, cardiopulmonary exercise testing (CPET) results, patient health metrics, patient exercise metrics, patient visit metrics, number of required implant removals, time to reintervention, durability of treatment, quality' of life score, body mass index, comorbidities, drug regimen, length of hospitalization, healthcare utilization, or cost.A17. The method of any one of clauses A1-A16, wherein the treatment comprises an airway treatment for COPD.Al 8. The method of clause Al 7, wherein the airway treatment comprises a pharmacological treatment.A19. The method of clause A17 or A18, wherein the airway treatment comprises an interventional treatment.A20. The method of clause Al 9, wherein the interventional treatment comprises one or more of the following: vapor therapy, administration of a sealant, transbronchial fenestration, placement of an endobronchial coil, placement of an endobronchial valve, or placement of a minimal endobronchial reinforcement implant.A21. The method of clause A20, wherein the interventional treatment comprises the placement of the minimal endobronchial reinforcement implant.A22. The method of any one of clauses A1-A21, further comprising generating a plan for the treatment, if the patient is a candidate for the treatment with the pulmonary disease.A23. The method of clause A22, wherein the plan is generated by inputting one or more of the predicted response or the set of lung metrics into a third machine learning algorithm.A24. The method of clause A22 or A23, wherein the treatment comprises placement of at least one minimal endobronchial reinforcement implant, and the plan comprises one or more of the following: implant placement location, number of implants, implant size, implant A pe, pathway to a target location, or localized treatment solutions.A25. The method of clause A24, wherein the implant placement location is based at least in part on one of more of the following: location of dynamic airway collapse as determined from expiratory CT data, severity of disease in a peripheral region of the lung, location of a pleural wall of the patient, or location of lobar, segmental, and / or sub-segmental airways.A26. The method of any one of clauses A1-A25, further comprising generating a report comprising a summary of one or more of the following: at least a portion of the set of lung metrics, the predicted response, the evaluation of whether the patient is a candidate forthe treatment, or the generated plan for the treatment.A27. The method of any one of clauses A1-A26, further comprising updating one or more of the first machine learning algorithm or the second machine learning algorithm based on historical or repository patient data.A28. The method of clause A27, wherein the historical or repository patient data comprises data of patients having GOLD III COPD, data of patients having GOLD IV COPD, or a combination thereof.A29. The method of clause A27 or A28, wherein the historical or repository patient data comprises data of patients treated with one or more of the following: a minimal endobronchial reinforcement implant, an endobronchial valve, an endobronchial coil, or vapor therapy.A30. The method of any one of clauses A27-A29, wherein the historical or repository patient data comprises data of the patient from an earlier time point.A31. A system comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of clauses A1-A30.A32. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of clauses A1-A30.A33. A method for evaluating a treatment outcome of a patient, the method comprising: receiving patient data including computed tomography (CT) data of a lung of the patient after placement of an endobronchial implant in the lung; generating a set of status metrics by inputting the patient data into a first machinelearning algorithm, wherein the set of status metrics includes: a set of lung metrics representing a state of the lung of the patient after the placement of the endobronchial implant, and a set of implant metrics representing a state of the endobronchial implant after placement in the lung; determining a response of the patient to the endobronchial implant by inputting the set of status metrics into a second machine learning algorithm; and predicting an outcome of the patient after the placement of the endobronchial implant by inputting one or more of the set of status metrics or the determined response into a third machine learning algorithm.A34. The method of clause A33, wherein the patient data comprises one or more of the following: questionnaire information, medical record information, magnetic resonance imaging (MRI) data, single-photon emission computed tomography (SPECT) data, bronchoscopy data, ventilation-perfusion data, pulmonary function test data, chest radiography data, fluoroscopy data, photographs, or sensor data.A35. The method of clause A33 or A34, wherein the CT data comprises expiratory CT data.A36. The method of any one of clauses A33-A35, wherein the CT data comprises inspiratory CT data.A37. The method of any one of clauses A33-A36, wherein the patient data comprises data obtained at a plurality of different time points.A38. The method of any one of clauses A33-A37, wherein the set of lung metrics characterizes one or more lung parameters, the one or more lung parameters characterizing any of the following: forced expiratory volume in 1 second, forced vital capacity7, vital capacity, inspiratory capacity, inspiratory capacity / total lung capacity ratio, functional residual capacity, total lung capacity, diffusion capacity for carbon monoxide, residual volume, residual volume / total lung capacity ratio, closing volume, lobar and / or segmental tissue destruction, lobar and / or segmental air trapping, lobar and / or segmental fissure status,extent of lobar and / or segmental fissure completion, lobar and / or segmental ventilation, lung function, homogeneity / heterogeneity of lobar and / or segmental emphysema, emphysema ty pe, location of diseased portions of the lung, lobar volume, segmental volume, segment locations, diaphragm shape, tissue density, opacity, proximity' of diseased portions to anatomical structures, proximity of disease portions to other medical devices, lumen inner diameter of bronchial sections, air flow mapping, collapsed airways, airway pressure, airway compliance, pleural pressure, airway diameter to wall thickness, airway wall deformation, vascular perfusion, parenchyma density, bullae, information that localizes disease in the lung, obstruction score, mucus score, degree of epithelialization, granulation tissue, implant- induced airway deformation, or airway tissue invagination into a lumen of the implant.A39. The method of any one of clauses A33-A38, wherein the set of lung metrics comprises a disease score characterizing severity of pulmonary disease in the patient.A40. The method of clause A38 or A39. wherein the set of lung metrics characterizes a change in at least one of the one or more lung parameters over a plurality of time points.A41. The method of clause A40. wherein the plurality of time points comprise two or more of the following: before endobronchial implant therapy, after endobronchial implant therapy, before administration of a bronchodilator, after administration of a bronchodilator, before exercise, or during exercise.A42. The method of any one of clauses A33-A41, wherein the endobronchial implant comprises a minimal endobronchial reinforcement implant.A43. The method of any one of clauses A33-A42, wherein the set of implant metrics characterizes one or more of the following: implant location, distance between a distal end of the implant and pleura, implant length, implant diameter at any one or more locations along a length of the implant, implant cross-sectional profile at any one or more locations along a length of the implant, implant integrity, pitch of loops of an implant, angle of an implant loop profile relative to a longitudinal axis of the implant, implant position relative to one or more additional implants, movement of the implant between inspiration andexpiration, occlusion of the implant, or implant dislodgment.A44. The method of any one of clauses A33-A43, further comprising generating and displaying a virtual bronchoscopy depicting a model incorporating one or more of at least a portion of the lung metrics or at least a portion of the implant metrics.A45. The method of any one of clauses A33-A44. wherein the determined response comprises a determination of one or more of the following: forced expiratory' volume in 1 second, forced vital capacity, vital capacity', inspiratory capacity', inspiratory capacity / total lung capacity ratio, functional residual capacity, total lung capacity, diffusion capacity for carbon monoxide, residual volume, residual volume / total lung capacity ratio, segmental volume, mMRC score, SGRQ score or a subset thereof, CAT score or a subset thereof, 6- minute walk test results, cycle ergometry results, cardiopulmonary' exercise testing (CPET) results, patient health metrics, patient exercise metrics, patient visit metrics, number of required implant removals, time to reintervention, durability of treatment, quality of life score, body mass index, comorbidities, drug regimen, length of hospitalization, healthcare utilization, or cost.A46. The method of any one of clauses A33-A45, wherein the predicted outcome comprises a prediction of a post-procedure issue after the placement of the endobronchial implant.A47. The method of clause A46. wherein the post-procedure issue comprises one or more of the following: copious mucus, excessive granulation tissue, excessive fibrosis, implant collapse, implant failure, implant migration, implant expectoration, inadequate lung function, pneumothorax, infection, pneumonia, or hospitalization.A48. The method of clause A46 or A47, further comprising determining an intervention to address the post-procedure issue.A49. The method of clause A48. wherein the determined intervention comprises one or more of the following: cleanup bronchoscopy, retrieval or removal of the endobronchial implant, repositioning of the endobronchial implant, replacement of theendobronchial implant, dilation of the endobronchial implant, placement of an additional endobronchial implant, or consultation with a healthcare professional.A50. The method of any one of clauses A33-A49, further comprising generating a report comprising a summan' of one or more of the following: at least a portion of the lung metrics, at least a portion of the implant metrics, the determined response of the patient to the endobronchial implant, the predicted outcome of the patient after the placement of the endobronchial implant, or the determined intervention to address a post-procedure issue.A51. The method of any one of clauses A33-A50, further comprising updating one or more of the first machine learning algorithm, the second machine learning algorithm, or the third machine learning algorithm based on historical or repository patient data.A52. The method of clause A51. wherein the historical or repository patient data comprises data of patients having GOLD III COPD, data of patients having GOLD IV COPD, or a combination thereof.A53. The method of clause A51 or A52. wherein the historical or repository patient data comprises data of patients treated with one or more of the following: a minimal endobronchial reinforcement implant, an endobronchial valve, an endobronchial coil, or vapor therapy.A54. The method of any one of clauses A51-A53, wherein the historical or repository patient data comprises data of the patient from an earlier time point.A55. The method of any one of clauses A33-A54, further comprising comparing the set of lung metrics to a set of second lung metrics determined from one or more of the following: image data of the lung before the placement of the endobronchial implant, image data of the lung at an earlier time point after the placement of the endobronchial implant, image data of the lung after placement of another endobronchial implant at a different location than a location of the endobronchial implant, or image data from other patients having COPD.A56. A system comprising: a processor; and a memoiy operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of clauses A33-A55.A57. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of clauses A33-A55.A58. A method for evaluating a patient having or suspected of having a pulmonary disease, the method comprising: receiving patient data including computed tomography (CT) data of a lung of the patient; and generating a pulmonary disease score for a region of interest of the lung by inputting the patient data into a machine learning algorithm, wherein the pulmonary disease score characterizes a severity of pulmonary disease in the region of interest of the lung of the patient, wherein the region of interest is a segmental region or a sub-segmental region of the lung.A59. The method of clause A58, wherein the machine learning algorithm evaluates voxel density in the CT data associated with the region of interest of the lung of the patient.A60. The method of clause A58 or A59, wherein the pulmonary disease score is based on multiple local disease scores each corresponding to a respective lobar, segmental, or sub-segmental region of the lung of the patient.A61. The method of clause A60, wherein the pulmonary disease score is an average of the multiple local disease scores.A62. The method of clause A58 or A59. wherein the pulmonary’ disease score is a first pulmonary disease score, wherein the method further comprises generating a plurality of pulmonary disease scores comprising the first pulmonary disease score, wherein each of the plurality’ of pulmonary disease scores corresponds to a respective lobar, segmental, or sub- segmental region of the lung of the patient.A63. The method of any one of clauses A58-A62. wherein the pulmonary disease score represents an extent of at least one of air trapping or hyperinflation in the lung of the patient.A64. The method of any one of clauses A58-A63. wherein the CT data comprises expiratory CT data.A65. The method of any one of clauses A58-A64. wherein the CT data comprises inspiratory CT data.A66. The method of any one of clauses A58-A65, wherein the CT data is generated prior to a treatment administered to the patient to treat the pulmonary disease.A67. The method of any one of clauses A58-A65, wherein the CT data is generated following a treatment administered to the patient to treat the pulmonary' disease.A68. The method of clause A66 or A67, wherein the treatment comprises placement of an endobronchial implant.A69. A system comprising: a processor; and a memory’ operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of clauses A58-A68.A70. A computed tomography (CT) scanner comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the CT scanner to perform operations comprising the method of any one of clauses A58-A68.A71. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computingsystem to perform operations comprising the method of any one of clauses A58-A68.A72. A method for normalizing quantitative computed tomography (CT) results for a patient, the method comprising: receiving first CT data for the patient, wherein the first CT data is generated under predetermined imaging conditions; and transforming the first CT data to second CT data by applying to the first CT data at least one correction factor associated with the predetermined imaging conditions.A73. The method of clause A72. wherein the at least one correction factor maps voxel density in the first CT data to a normalized voxel density.A74. The method of clause A72 or A73, wherein the at least one correction factor compensates for voxel density in the first CT data affected by one or more of the following: tube current, tube potential, pitch,A75. The method of any one of clauses A72-A74, wherein the at least one correction factor compensates for voxel density in the first CT data affected by at least one of slice thickness or slice interval.A76. The method of any one of clauses A72-A75, wherein the at least one correction factor compensates for voxel density in the first CT data affected by a reconstruction algorithm for determining sharpness or smoothness of image in an axial plane.A77. The method of any one of clauses A72-A76, wherein the first CT data is obtained from a CT scan provider having a provider-specific machine learning algorithm for reconstructing a CT image from CT data, wherein the at least one correction factor compensates for voxel density in the first CT data affected by the provider-specific machine learning algorithm.A78. The method of any one of clauses A72-A77, wherein the at least one correction factor compensates for voxel density in the first CT data affected by administration of a contrast agent in the patient before the first CT data is generated.A79. The method of any one of clauses A72-A78. wherein the second CT data is normalized with respect to CT scan parameters.A80. The method of any one of clauses A72-A79. wherein the first CT data is obtained during a pre-procedure phase prior to placement of an endobronchial implant in the patient.A81. The method of any one of clauses A72-A80. further comprising generating a set of lung metrics associated with the patient based on the second CT data.A82. The method of any one of clauses A72-A79, wherein the first CT data is obtained during a peri-procedure phase during placement of an endobronchial implant in the patient.A83. The method of any one of clauses A72-A79, wherein the first CT data is obtained during a post-procedure phase following placement of an endobronchial implant in the patient.A84. The method of clause A82 or A83, further comprising generating at least one of a set of lung metrics or a set of implant metrics associated with the patient based on the second CT data.A85. A system comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of clauses A72-A84.A86. A computed tomography (CT) scanner comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the CT scanner to perform operations comprising the method of any one of clauses A72-A84.A87. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of clauses A72-A84.A88. A method for planning a treatment for a patient having a pulmonary disease, the method comprising: receiving patient data including computed tomography (CT) data of a lung of the patient; generating a set of lung metrics by inputting the patient data into a first machine learning algorithm, wherein the set of lung metrics represents a state of the lung of the patient; identifying a potential target region in the lung for a treatment for the pulmonary disease, based at least in part on the generated lung metrics.A89. The method of clause A88. wherein the patient data comprises one or more of the following: questionnaire information, medical record information, magnetic resonance imaging (MRI) data, single-photon emission computed tomography (SPECT) data, bronchoscopy data, ventilation-perfusion data, pulmonary function test data, chest radiography data, fluoroscopy data, photographs, or sensor data.A90. The method of clause A88 or A89, wherein the CT data comprises expiratory CT data.A91. The method of any one of clauses A88-A90, wherein the CT data comprises inspiratory CT data.A92. The method of any one of clauses A88-A91, wherein the patient data comprises data obtained at a plurality of different time points.A93. The method of any one of clauses A88-A92, wherein the set of lung metrics correlates to whether the patient has at least one of chronic obstructive pulmonary disease (COPD), severe emphysema, or severe emphysema with hyperinflation.A94. The method of any one of clauses A88-A93. wherein the set of lung metrics characterizes one or more lung parameters, the one or more lung parameters comprising one or more of the following: forced expiratory volume in 1 second, forced vital capacity, vital capacity, inspiratory capacity, inspiratory capacity / lolal lung capacity ratio, functional residual capacity , total lung capacity, diffusion capacity for carbon monoxide, residual volume, residual volume / total lung capacity ratio, closing volume, lobar and / or segmental tissue destruction, lobar and / or segmental air trapping, lobar and / or segmental fissure status, extent of lobar and / or segmental fissure completion, lobar and / or segmental ventilation, lung function, homogeneity / heterogeneity of lobar and / or segmental emphysema, emphysema type, locations of diseased portions of the lung, lobar volume, segmental volume, segmental locations, diaphragm shape, tissue density, opacity, proximity of diseased portions to anatomical structures, proximity of diseased portions to other medical devices, lumen inner diameter of bronchial sections, air flow mapping, collapsed airways, airway pressure, airway compliance, pleural pressure, airway diameter to wall thickness, airway wall deformation, vascular perfusion, parenchyma density, bullae, information that localizes disease in the lung, obstruction score, mucus score, or degree of epitheliahzation.A95. The method of any one of clauses A88-A94. wherein the set of lung metrics comprises at least one disease score characterizing severity of pulmonary disease in the patient.A96. The method of clause A95, wherein the at least one disease score represents a predictor of patient response to the treatment of the pulmonary disease.A97. The method of clause A95 or A96, wherein the set of lung metrics comprises multiple disease scores each corresponding to a respective lobar, segmental, or sub-segmental region of the lung of the patient.A98. The method of clause A95 or A96, wherein the set of lung metrics comprises a single disease score based on multiple local disease scores each corresponding to a respective lobar, segmental, or sub-segmental region of the lung of the patient.A99. The method of clause A98, wherein the single disease score is an average ofthe multiple local disease scores.Al 00. The method of any one of clauses A95-A99, wherein the at least one disease score represents an extent of at least one of air trapping or hyperinflation in the lung of the patient.A101. The method of any one of clauses A94-A100, wherein the set of lung metrics characterizes a change in at least one of the one or more lung parameters over a plurality of time points.A 102. The method of clause A101, wherein the plurality of time points comprise two or more of the following: before endobronchial implant therapy, after endobronchial implant therapy, before administration of a bronchodilator, after administration of a bronchodilator, before exercise, or during exercise.Al 03. The method of any one of clauses A88-A102, wherein the treatment comprises an airway treatment for COPD.A104. The method of clause A103, wherein the airway treatment comprises a pharmacological treatment.A105. The method of clause A103 or A104, wherein the airway treatment comprises an interventional treatment.A106. The method of clause A105, wherein the interventional treatment comprises one or more of the following: vapor therapy, administration of a sealant, transbronchial fenestration, placement of an endobronchial coil, placement of an endobronchial valve, or placement of a minimal endobronchial reinforcement implant.A107. The method of clause A106, wherein the interventional treatment comprises the placement of the minimal endobronchial reinforcement implant.A108. The method of any one of clauses A88-A107, further comprising generating aplan for the treatment.Al 09. The method of clause Al 08, wherein the plan is generated by inputting the set of lung metrics into a second machine learning algorithm.A110. The method of clause Al 08 or Al 09, wherein the treatment comprises placement of at least one minimal endobronchial reinforcement implant, and the plan comprises one or more of the following: implant placement location, number of implants, implant size, implant type, pathway to a target location, or localized treatment solutions.Al l i. The method of clause Al 10, wherein the implant placement location is based at least in part on one of more of the following: location of dynamic airway collapse as determined from expiratory' CT data, severity of disease in a peripheral region of the lung, location of a pleural wall of the patient, or location of lobar, segmental, and / or sub-segmental airways.Al 12. A system comprising: a processor; and a memory' operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of clauses A88-A111.Al 13. A computed tomography (CT) scanner comprising: a processor; and a memory' operably coupled to the processor and storing instructions that, when executed by the processor, cause the CT scanner to perform operations comprising the method of any one of clauses A88-A111.Al 14. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of clauses A88-A111.Bl . A method for planning a treatment for a patient having a pulmonary disease, the method comprising: receiving patient data including computed tomography (CT) data of a lung of the patient; generating a set of one or more lung metrics by inputting the patient data into a patient characterization machine learning algorithm, wherein the set of one or more lung metrics represents a state of the lung of the patient; and mapping a potential pathway, identifying a target airway segment, or both, in the lung of the patient for placing an implant at a target location in the lung, by inputting one or more of the patient data or the set of one or more lung metrics into a treatment planning machine learning algorithm.B2. The method of clause Bl, wherein mapping the potential pathway in the lung comprises identifying a pathway from segmental bronchi in the lung to a pleural surface of the lung.B3. The method of clause Bl or B2, wherein the treatment planning machine algorithm is configured to maximize access of the implant to a region of the lung having trapped air.B4. The method of any one of clauses B1-B3, wherein the treatment planning machine algorithm is configured to maximize access to emphysematous tissue at a periphery' of the lung.B5. The method of any one of clauses B1-B4, wherein the treatment planning machine algorithm is configured to minimize tortuosity of the mapped pathway.B6. The method of any one of clauses B1-B5, wherein the treatment planning machine algorithm is configured to minimize risk of damage to blood vessels in the lung during delivery' and placement of the implant.B7. The method of any one of clauses B1-B6, wherein the CT data comprises expiratory CT data.B8. The method of any one of clauses B1-B7, wherein the CT data comprises inspiratory CT data.B9. The method of any one of clauses B1-B8, wherein the set of lung metrics characterizes one or more lung parameters, the one or more lung parameters comprising one or more of the following: forced expiratory volume in 1 second, forced vital capacity, vital capacity, inspiratory capacity, inspiratory capacity / total lung capacity ratio, functional residual capacity, total lung capacity', diffusion capacity' for carbon monoxide, residual volume, residual volume / total lung capacity ratio, closing volume, lobar and / or segmental tissue destruction, lobar and / or segmental air trapping, lobar and / or segmental fissure status, extent of lobar and / or segmental fissure completion, lobar and / or segmental ventilation, lung function, homogeneity / heterogeneity of lobar and / or segmental emphysema, emphysema ty pe, locations of diseased portions of the lung, lobar volume, segmental volume, segmental locations, diaphragm shape, tissue density, opacity, proximity of diseased portions to anatomical structures, proximity of diseased portions to other medical devices, lumen inner diameter of bronchial sections, air flow mapping, collapsed airways, airway pressure, airway compliance, airway resistance, airway elastance, pleural pressure, airway diameter to wall thickness, airway wall deformation, vascular perfusion, parenchyma density, bullae, information that localizes disease in the lung, obstruction score, mucus score, or degree of epithelialization.BIO. The method of clause B9, wherein the set of lung metrics characterizes one or more of airway resistance, airway compliance, airway elastance, or any combination thereof, at a segmental level of the lung, a lobar level of the lung, a lung level of the lung, or other regional level of the lung.Bl 1. The method of any one of clauses B1-B10, wherein the set of lung metrics comprises at least one disease score characterizing severity of pulmonary disease in the patient.Bl 2. The method of clause Bl 1, wherein the at least one disease score comprises a first disease score corresponding to severity of emphysema and a second disease score corresponding to a non-emphysema pulmonary disease.B13. The method of clause B12, wherein the non-emphysema pulmonary disease is one of small airways disease, bronchitis, asthma, pulmonary fibrosis, type 2 inflammation, or neutrophilic inflammation.Bl 4. The method of clause B12 or B13, wherein the treatment planning machine learning algorithm is configured to map a potential pathway, identify a target airway segment, or both, based at least in part on the first disease score.B15. The method of any one of clauses B12-B14, wherein the treatment planning machine learning algorithm is configured to map a potential pathway, identify a target airway segment, or both based at least in part on the second disease score, where the second disease score is utilized to exclude one or more pathways as a potential pathway, exclude an airway segment as a target airway segment, or both.Bl 6. The method of any one of clauses B12-B15, further comprising identifying one or more pharmacological treatment options for treatment of the pulmonary disease, by inputting one or more of the patient data or the set of one or more lung metrics into the treatment planning machine learning algorithm.B17. The method of clause B16, wherein the treatment planning machine learning algorithm is configured such that in response to the first disease score satisfying a first predetermined threshold and the second disease score satisfying a second predetermined threshold, the treatment planning machine learning algorithm provides an output indicating that both at least one pharmacological treatment and placement of an implant are warranted for treatment of the pulmonary' disease.B 18. The method of any one of clauses B 1-B 17, further comprising identifying the target location based at least in part on one of more of the following: location of dynamic airway collapse as determined from expiratory' CT data, severity' of disease in a peripheral region of the lung, location of a pleural wall of the patient, or location of lobar, segmental, and / or sub-segmental airways.Bl 9. The method of any one of clauses Bl -Bl 8, wherein the implant is a minimalendobronchial reinforcement implant.B20. A system comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of clauses Bl -Bl 9.B21. A computed tomography (CT) scanner comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the CT scanner to perform operations comprising the method of any one of clauses B1-B19.B22. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of clauses B1-B19.B23. A method for evaluating a patient having a pulmonary disease, the method comprising: receiving patient data including computed tomography (CT) data of a lung of the patient; and generating an identification of at least one type of pulmonary disease causing air trapping or obstruction in the lung of the patient, by inputting the patient data into a patient characterization machine learning algorithm.B24. The method of clause B23, wherein generating the identification of at least one type of pulmonary disease comprises identifying that air trapping or obstruction in the lung of the patient is caused by emphysema.B25. The method of clause B23 or B24, wherein generating the identification of at least one type of pulmonary disease comprises identifying that air trapping or obstruction in the lung of the patient is caused by small airway disease.B26. The method of any one of clauses B23-B25. wherein generating the identification of at least one type of pulmonary disease comprises identifying that obstruction in the lung of the patient is caused by chronic bronchitis.B27. The method of any one of clauses B23-B26. wherein generating the identification of at least one type of pulmonary disease comprises identifying that obstruction in the lung of the patient is caused by asthma.B28. The method of any one of clauses B23-B27, wherein generating the identification of at least one type of pulmonary disease comprises identifying that obstruction in the lung of the patient is caused by pulmonary fibrosis.B29. The method of any one of clauses B23-B28, further comprising generating at least one disease score characterizing severity of pulmonary’ disease in the patient.B30. The method of any one of clauses B23-B29, wherein the at least one disease score comprises a first disease score quantifying an amount of air trapping or obstruction in the lung of the patient caused by a first pulmonary disease.B31 . The method of clause B30, wherein the first disease score comprises a percentage of an obstructed airway region that is exhibiting at least one of air trapping or obstruction in the lung of the patient and is caused by the first pulmonary disease.B32. The method of clause B30 or B31, wherein the at least one disease score comprises a second disease score quantifying an amount of at least one of air trapping or obstruction in the lung of the patient caused by a second pulmonary disease.B33. The method of any one of clauses B23-B32. wherein the CT data comprises expiratory CT data.B34. The method of any one of clauses B23-B33, wherein the CT data comprises inspiratory CT data.B35. The method of any one of clauses B23-B34, further comprising generating a characterization of one or more lung parameters, the one or more lung parameters comprising one or more of the following: forced expiratory volume in 1 second, forced vital capacity, vital capacity, inspiratory capacity, inspiratory capacity / total lung capacity ratio, functional residual capacity , total lung capacity, diffusion capacity for carbon monoxide, residual volume, residual volume / total lung capacity ratio, closing volume, lobar and / or segmental tissue destruction, lobar and / or segmental air trapping, lobar and / or segmental fissure status, extent of lobar and / or segmental fissure completion, lobar and / or segmental ventilation, lung function, homogeneity / heterogeneity of lobar and / or segmental emphysema, emphysema type, locations of diseased portions of the lung, lobar volume, segmental volume, segmental locations, diaphragm shape, tissue density, opacity, proximity of diseased portions to anatomical structures, proximity of diseased portions to other medical devices, lumen inner diameter of bronchial sections, air flow mapping, collapsed airways, airway pressure, airway compliance, airway resistance, airway elastance, pleural pressure, airway diameter to wall thickness, airway wall deformation, vascular perfusion, parenchyma density, bullae, information that localizes disease in the lung, obstruction score, mucus score, or degree of epithelialization.B36. A system comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of clauses B23-B35.B37. A computed tomography (CT) scanner comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the CT scanner to perform operations comprising the method of any one of clauses B23-B35.B38. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of clauses B23-B35.B39. A method for evaluating a patient, the method comprising: receiving patient data including computed tomography (CT) data of a lung of the patient; generating a set of one or more lung metrics by inputting the patient data into a patient characterization machine learning algorithm, wherein the set of one or more lung metrics characterizes a shape of an airway of the lung of the patient at one or more timepoints within a respiratory cycle, including at least one of maximal inspiration, maximal expiration, tidal volume inspiration, or tidal volume expiration.B40. The method of clause B39. wherein the set of one or more lung metrics characterizes a shape of the airw ay at each of maximal inspiration, maximal expiration, tidal volume inspiration, and tidal volume expiration.B41. The method of clause B39 or B40, wherein the CT data comprises at least one of expiratory CT data or inspiratory CT data.B42. The method of B41, wherein the patient characterization machine learning algorithm is configured to utilize voxel density thresholding to identify the airway of the lung in the expiratory CT data or the inspiratory CT data.B43. The method of any one of clauses B39-B42, wherein the method further comprises estimating a relative change in lung volume betw een maximal inspiration and tidal volume inspiration.B44. The method of any one of clauses B39-B43, wherein the method further comprises estimating a relative change in lung volume betw een maximal expiration and tidal volume expiration.B45. The method of any one of clauses B39-B44, wherein the set of one or more lung metrics characterizes at least one of diameter, length, radius of curvature, or tortuosity of the airway of the lung.B46. The method of any one of clauses B39-B45, wherein the set of lung metrics characterizes one or more lung parameters, the one or more lung parameters comprising one or more of the following: forced expiratory volume in 1 second, forced vital capacity, vital capacity, inspiratory capacity, inspiratory capacity / lolal lung capacity ratio, functional residual capacity , total lung capacity, diffusion capacity for carbon monoxide, residual volume, residual volume / total lung capacity ratio, closing volume, lobar and / or segmental tissue destruction, lobar and / or segmental air trapping, lobar and / or segmental fissure status, extent of lobar and / or segmental fissure completion, lobar and / or segmental ventilation, lung function, homogeneity / heterogeneity of lobar and / or segmental emphysema, emphysema type, locations of diseased portions of the lung, lobar volume, segmental volume, segmental locations, diaphragm shape, tissue density, opacity, proximity of diseased portions to anatomical structures, proximity of diseased portions to other medical devices, lumen inner diameter of bronchial sections, air flow mapping, collapsed airways, airway pressure, airway compliance, airway resistance, airway elastance, pleural pressure, airway diameter to wall thickness, airway wall deformation, vascular perfusion, parenchyma density, bullae, information that localizes disease in the lung, obstruction score, mucus score, or degree of epithelialization.B47. The method of any one of clauses B39-B46. wherein the set of one or more lung metrics is a first set of one or more lung metrics characterizing a shape of an airway of the lung of the patient prior to placement of an endobronchial implant in the lung, wherein the method further comprises: generating a second set of one or more lung metrics that represent a predicted state of the lung after placement of the endobronchial implant in the lung, by inputting at least one of the first set of one or more lung metrics or patient data into the patient characterization machine learning algorithm; and generating a set of one or more implant metrics by inputting the second set of one or more lung metrics into an implant characterization machine learning algorithm, wherein the set of one or more implant metrics represent a predicted state of the endobronchial implant after placement in the lung.B48. The method of clause B47, wherein the second set of one or more lung metrics is based at least in part on one or more of: at least a portion of the first set of one or morelung metrics, a number of one or more endobronchial implants placed in the lung, or placement location of each of one or more endobronchial implants placed in the lung.B49. A system comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of clauses B39-B48.B50. A computed tomography (CT) scanner comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the CT scanner to perform operations comprising the method of any one of clauses B39-B48.B51. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of clauses B39-B48.B52. A method for evaluating a patient having an endobronchial implant placed in a lung of the patient, the method comprising: receiving patient data including computed tomography (CT) data of the lung of the patient; generating a set of one or more implant metrics by inputting the patient data into an implant characterization machine learning algorithm, wherein the set of one or more implant metrics represent a state of the endobronchial implant after placement in the lung, wherein the set of one or more implant metrics characterizes a shape of the implant at one or more timepoints within a respiratory cycle, including at least one of maximal inspiration, maximal expiration, tidal volume inspiration, or tidal volume expiration.B53. The method of clause B52, wherein the set of one or more implant metrics characterizes a shape of the implant at each of maximal inspiration, maximal expiration, tidalvolume inspiration, and tidal volume expiration.B54. The method of clause B52 or B53, wherein the CT data comprises at least one of expiratory CT data or inspiratory CT data.B55. The method of clause B54, wherein the implant characterization machine learning algorithm is configured to utilize voxel density thresholding to identify the implant in the expiratory CT data or the inspiratory CT data.B56. The method of any one of clauses B52-B55, wherein the set of one or more implant metrics characterizes one or more of the following: implant location, distance between a distal end of the implant and pleura, implant length, implant diameter at any one or more locations along a length of the implant, implant cross-sectional profile at any one or more locations along a length of the implant, implant integrity, pitch of loops of an implant, angle of an implant loop profile relative to a longitudinal axis of the implant, implant position relative to one or more additional implants, movement of the implant between inspiration and expiration, occlusion of the implant, or implant dislodgment.B57. The method of any one of clauses B52-B56, wherein the endobronchial implant comprises a minimal endobronchial reinforcement implant.B58. A system comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of clauses B52-B57.B59. A computed tomography (CT) scanner comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the CT scanner to perform operations comprising the method of any one of clauses B52-B57.B60. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of clauses B52-B57.B61. A method for evaluating a patient having a pulmonary disease, the method comprising: receiving patient data including computed tomography (CT) data of a lung of the patient; and generating at least one disease score for the patient by inputting the patient data into a patient characterization machine learning algorithm, wherein the at least one disease score characterizes severity of dynamic hyperinflation in the lung of the patient.B62. The method of clause B61, wherein the disease score is generated for the patient without the patient performing a cardiopulmonary exercise test.B63. The method of clause B61 or B62, wherein the CT data comprises inspiratory' CT data.B64. The method of any one of clauses B61-B63. wherein the CT data comprises expiratory CT data.B65. A system comprising: a processor; and a memory' operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of clauses B61-B64.B66. A computed tomography (CT) scanner comprising: a processor; and a memory' operably coupled to the processor and storing instructions that, when executed by the processor, cause the CT scanner to perform operations comprising the method of any one of clauses B61-B64.B67. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of clauses B61-B64.B68. A method for evaluating a patient, the patient comprising: receiving patient data including computed tomography (CT) data of a lung of the patient; generating a set of one or more initial lung metrics for the patient based on the patient data; generating a set of one or more corrected lung metrics corrected for variability in breathing effort, by applying one or more correction factors generated by a correction machine learning algorithm.B69. The method of clause B68, wherein generating the set of one or more initial lung metrics comprises inputting the patient data into a patient characterization machine learning algorithm.B70. The method of clause B68 or B69, wherein the set of one or more corrected lung metrics comprises at least one of total lung capacity or residual volume.B71. The method of any one of clauses B68-B70, wherein the one or more correction factors represent a difference in expiratory patient data due to variability in breathing effort by a patient in different body postures.B72. The method of clause B71, wherein the one or more correction factors represent a difference in expiratory' patient data due to variability in breathing effort by a patient during a pulmonary’ function test compared to during an expiratory CT scan.B73. The method of any one of clauses B68-B72, wherein the CT data comprises inspiratory CT data.B74. The method of any one of clauses B68-B73. wherein the method for evaluating the patient does not include receiving expiratory CT data from an expiratory CT scan.B75. The method of any one of clauses B68-B74. further comprising generating corrected expiratory CT data that corrects for vari ability in breathing effort, by inputting the set of one or more corrected lung metrics into a transformation machine learning algorithm.B76. The method of clause B75, wherein generating corrected expiratory CT data comprises generating a corrected expiratory CT scan that corrects for variability in breathing effort.B77. A system comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of clauses B68-B76.B78. A computed tomography (CT) scanner comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the CT scanner to perform operations comprising the method of any one of clauses B68-B76.B79. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of clauses B68-B76.B80. A method for planning a treatment for a patient having a pulmonary disease, the method comprising: receiving patient data including computed tomography (CT) data of a lung of the patient; determining a ventilation / perfusion (V / Q) profile of the lung based on the patient data; and identifying a target location for placing an endobronchial implant in the lung of the patient by inputting the V / Q profile into a treatment planning machine learning algorithm.B81. The method of clause B80. wherein the identified target location corresponds to a region of the lung exhibiting a threshold level of perfusion.B82. The method of clause B80 or B81, wherein the treatment planning machine learning algorithm is configured to identify a target location for maximizing efficacy of the endobronchial implant.B83. The method of any one of clauses B80-B82, wherein the CT data comprises expiratory CT data.B84. The method of any one of clauses B80-B83, wherein the CT data comprises inspiratory CT data.B85. The method of any one of clauses B80-B84. wherein the endobronchial implant is a minimal endobronchial reinforcement implant.B86. A system comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of clauses B80-B85.B87. A computed tomography (CT) scanner comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the CT scanner to perform operations comprising the method of any one of clauses B80-B85.B88. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of clauses B80-B85.B89. A method comprising: receiving a set of lung metrics characterizing lung state of a patient, the set of lung metrics comprising one or more of: at least one metric representative of amount of emphysema destruction in a first lung, at least one metric representative of amount of emphysema destruction in a second lung, or residual volume (RV), total lung capacity (TLC); and determining the patient is a candidate for treatment with an endobronchial reinforcement implant, in response to determining, based on the set of lung metrics:(i) the patient has at least one lung exhibiting heterogeneous emphysema; and(ii) the patient has a baseline RV / TLC metric that is equal to or above a first threshold, or a baseline RV that is equal to or above a second threshold, or both.B90. The method of clause B89, wherein determining the patient as a candidate comprises identifying the patient as a candidate in response to determining, based on the set of lung metrics, the patient has two lungs exhibiting heterogeneous emphysema.B91. The method of clause B89 or B90, wherein the first threshold is at least 0.55.B92. The method of any one of clauses B89-B91. wherein the first threshold is at least 0.65.B93. The method of any one of clauses B90-B92, wherein the second threshold is between about 150% and about 180%.B94. The method of any one of clauses B90-B93, wherein the second threshold is about 180%.B95. The method of any one of clauses B90-B94. wherein the set of lung metrics further comprises an emphysema score representative of percentage of emphysema destruction in each of at least one of the first or second lungs, and wherein identifying the patient as a candidate comprises identifying the patient as a candidate at least in part in response to determining that the emphysema score is above at least 25%.B96. The method of clause B89, wherein determining the patient as a candidate comprises identifying the patient as a candidate in response to determining, based on the set of lung metrics, the patient has a single lung exhibiting heterogeneous emphysema and a single lung exhibiting homogeneous emphysema and the second threshold is between about 175% and about 225%.B97. The method of clause B96. wherein the second threshold is about 200%.B98. The method of any one of clauses B89-B97, wherein the patient is considered to have at least one lung exhibiting heterogeneous emphysema if the at least one lung has an upper lobe exhibiting a first level of emphysema destruction and a lower lobe exhibiting a second level of emphysema destruction, the first and second levels of emphysema destruction differing by at least 15%.B99. The method of any one of clauses B89-B98. wherein the set of lung metrics further comprises at least one additional lung metric characterizing one or more of: mucus plugging, suspected pulmonary hypertension, dynamic hyperinflation, bronchial wall thickening, fibrosis, scarring, extent of disease in small airw ays, or extent of disease in large airways in the patient.Bl 00. The method of any one of clauses B89-B99, wherein the set of lung metrics is derived from one or more of computed tomography (CT) data, plethysmography, or spirometry.B 101 . The method of clause B99 or B 100, further comprising determining the patient is or is not a candidate for treatment with the endobronchial reinforcement implant, based on any one or more of the at least one additional lung metrics.B102. The method of any one of clauses B89-B101, further comprising, in response to determining the patient is a candidate for treatment with an endobronchial reinforcement implant, placing at least one endobronchial reinforcement implant in at least one lung of the patient.Bl 03. The method of clause Bl 02, wherein the endobronchial reinforcement implant comprises a minimal endobronchial reinforcement implant.Bl 04. A method comprising: receiving a set of lung metrics characterizing lung state of a patient, the set of lung metrics comprising an emphysema score representative of percentage of emphysema destruction in lungs of the patient; and determining the patient is a candidate for treatment with an endobronchial reinforcement implant, in response to determining that the emphysema score is above at least 25%.B105. The method of clause Bl 04, wherein the set of lung metrics further comprises at least one metric representative of amount of emphysema destruction in a first lung, and at least one metric representative of amount of emphysema destruction in a second lung, and wherein identifying the patient as a candidate comprises identifying the patient as a candidate in response to determining, based on the set of lung metrics, the patient has two lungs exhibiting heterogeneous emphysema.B 106. The method of clause B 104 or B 105. wherein the patient is considered to have at least one lung exhibiting heterogeneous emphysema if the at least one lung has an upper lobe exhibiting a first level of emphysema destruction and a lower lobe exhibiting a second level of emphysema destruction, the first and second levels of emphysema destruction differing by at least 15%.Bl 07. The method of any one of clauses Bl 04 -Bl 06, wherein the set of lung metrics further comprises at least one additional lung metric characterizing one or more of: mucus plugging, suspected pulmonary hypertension, dynamic hyperinflation, bronchial wall thickening, fibrosis, scarnng, extent of disease in small airways, extent of disease in large airways in the patient, airway resistance, airway compliance, or airway elastance.B108. The method of clause B107, wherein the set of additional lung metrics characterizes one or more of airway resistance, airway compliance, airway elastance, or anycombination thereof, at a segmental level of the lung, a lobar level of the lung, a lung level of the lung, or other regional level of the lung.B109. The method of clause B107 or B108, further comprising determining the patient is or is not a candidate for treatment with the endobronchial reinforcement implant, based on any one or more of the at least one additional lung metrics.Bl 10. The method of any one of clauses Bl 04 -Bl 09, further comprising, in response to identifying that the patient is a candidate for treatment with an endobronchial reinforcement implant, placing at least one endobronchial reinforcement implant in at least one lung of the patient.Bi l l. The method of clause Bl 10, wherein the endobronchial reinforcement implant comprises a minimal endobronchial reinforcement implant.B 112. A method for treating a patient, comprising: receiving computed tomography (CT) data of a lung of the patient; identifying, based on the CT data, one or more candidate airway segments in the lung, wherein each candidate airway segment has an emphysema score of at least 20%, the emphysema score being representative of a percentage of emphysema destruction in the candidate airway segment; calculating a volume of emphysema destruction in each candidate airway segment based on a respective volume metric of the candidate airw ay segment and the respective emphysema score for the candidate airway segment; determining one or more target airway segments in the lung based on the calculated volumes of emphysema destruction; and placing an endobronchial reinforcement implant in each of the one or more target airway segments.Bl 13. The method of clause Bl 12, wherein the CT data comprises at least one of inspiratory or expiratory CT data.Bl 14. The method of clause Bl 12 or Bl 13. wherein each candidate airway segment has a segmental diameter of between about 3mm and about 9 mm.Bl 15. The method of any one of clauses Bl 12-B114, wherein the volume metric comprises an air volume of the candidate airway segment.Bl 16. The method of any one of clauses Bl 12-B115, wherein the volume metric comprises a tissue volume of the candidate airway segment.Bl 17. The method any one of clauses Bl 12-B11 , wherein calculating a volume of emphysema destruction comprises adding and / or multiplying the volume metric and the emphysema score.Bl 18. The method of any one of clauses B112-B117, wherein calculating a volume of emphysema destruction comprises applying a weight factor to the volume metric, the emphysema score, or both.Bl 19. The method of clause Bl 18, wherein applying a weight factor comprises applying a first weight factor to the volume metric and applying a second weight factor to the emphysema score, wherein the first and second weight factors are different.B120. The method of any one of clauses Bl 12-B119, wherein determining one or more target airway segments comprises identifying, as a target airway segment, a candidate airway segment having the largest calculated volume of emphysema destruction out of the one or more candidate airw ay segments.B121 . The method of clause Bl 20, wherein determining one or more target airways comprises identifying, as two target airway segments, two candidate airway segments having the largest tw o calculated volumes of emphysema destruction out of the candidate airw ay segments.B122. The method of clause B121, wherein determining one or more target airw ays comprises identifying, as two target airway segments, three candidate airw ay segments having the largest three calculated volumes of emphysema destruction out of the candidate airway segments.B123. The method of any one of clauses Bl 12-B122, further comprising generating, from the CT data using a machine learning algorithm, a set of lung metrics characterizing one or more of airway resistance, airway compliance, airway elastance, or any combination thereof, at a segmental level of the lung, a lobar level of the lung, a lung level of the lung, or other regional level of the lung.B124. The method of clause B123, wherein determining one or more target airway segments is further based on the set of lung metrics.Bl 25. The method of any one of clauses Bl 12-B124, wherein placing an endobronchial reinforcement implant in each of the one or more target airway segments comprises placing one, two, or three endobronchial reinforcement implants in the lung.B126. The method of clause B125, wherein the lung is a first lung of the patient, the method further comprising: receiving computed tomography (CT) data of a second lung of the patient; identifying, based on the CT data, one or more candidate airway segments in the second lung, wherein each candidate airway segment has an emphysema score of at least 20%, the emphysema score being representative of a percentage of emphysema destruction in the candidate airway segment; calculating a volume of emphysema destruction in each candidate airway segment based on a respective volume metric of the candidate airway segment and the respective emphysema score for the candidate airway segment; determining one or more target airway segments in the second lung based on the calculated volumes of emphysema destruction; and placing an endobronchial reinforcement implant in each of the one or more target airway segments in the second lung.B127. The method of clause B126, comprising placing up to three endobronchial reinforcement implants in the first lung and placing up to three endobronchial reinforcement implants in the second lung.B128. The method of any one of clauses Bl 12-B127, wherein the endobronchial reinforcement implant comprises a minimal endobronchial reinforcement implant.B129. A system comprising: a processor; and a memoiy operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of clauses B89-B128.Bl 30. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of clauses B89-B128.B 131. A method for treating a patient, the method comprising: determining one or more target airway segments in at least one lung of the patient, wherein the patient is identified as having emphysema; and placing an endobronchial reinforcement implant in each of the one or more target airway segments.B 132. The method of clause B 131 , wherein the patient has at least one lung exhibiting heterogeneous emphysema.Bl 33. The method of clause Bl 32, wherein placing an endobronchial reinforcement implant comprises placing only a single endobronchial reinforcement implant in the at least one lung exhibiting heterogeneous emphysema.Bl 34. The method of clause Bl 32, wherein placing an endobronchial reinforcement implant comprises placing only two endobronchial reinforcement implants in the at least one lung exhibiting heterogeneous emphysema.Bl 35. The method of clause Bl 32, wherein placing an endobronchial reinforcement implant comprises placing only up to three endobronchial reinforcement implants in the at least one lung exhibiting heterogeneous emphysema.B 136. The method of clause B 131 , wherein the patient has at least one lung exhibiting homogeneous emphysema.B137. The method of clause B136, wherein placing an endobronchial reinforcement implant comprises placing at least one endobronchial reinforcement implant in each lobe of the lung exhibiting homogeneous emphysema.B138. The method of clause B136, wherein placing an endobronchial reinforcement implant comprises placing at least one endobronchial reinforcement implant in each of an upper lobe and a lower lobe of the at least one lung exhibiting homogeneous emphysema.B139. The method of clause B136, wherein placing an endobronchial reinforcement implant comprises placing at least three implants in the at least one lung exhibiting homogeneous emphysema.B140. The method of clause B139, wherein placing an endobronchial reinforcement implant comprises placing more than three implants in the at least one lung exhibiting homogeneous emphysema.B141 . The method of any one of clauses B136-B140, wherein placing an endobronchial reinforcement implant comprises placing up to a total of five implants in the at least one lung exhibiting homogeneous emphysema.B 142. The method of any one of clauses B 136-B 140, wherein placing an endobronchial reinforcement implant comprises placing up to a total of six implants in the at least one lung exhibiting homogeneous emphysema.B 143. The method of clause B 131 , wherein the patent has unilateral homogeneous emphysema, with a first lung exhibiting homogeneous emphysema and a second lung exhibiting heterogeneous emphysema.B144. The method of clause B143, wherein placing an endobronchial reinforcement implant comprises placing a first number of endobronchial reinforcement implants in the first lung exhibiting homogeneous emphysema and placing a second number of endobronchial reinforcement implants in the second lung exhibiting heterogeneous emphy sema, wherein the second number is greater than the first number.B 145. The method of clause B 131 , wherein the patent has bilateral homogeneous emphysema.B146. The method of clause B145, wherein placing an endobronchial reinforcement implant comprises placing at least two endobronchial reinforcement implants in a first lung of the patient, and placing at least two endobronchial reinforcement implants in a second lung of the patient.B147. The method of clause B145 or B 146, wherein the method comprises placing a total of at least six endobronchial reinforcement implants in the patient.B148. The method of clause B147, wherein the method comprises placing a total of at least eight endobronchial reinforcement implants in the patient.Bl 49. The method of clause Bl 48, wherein the method comprises placing a total of at least ten endobronchial reinforcement implants in the patient.B150. The method of any one of clauses B131-B149, wherein the endobronchial reinforcement implant is a minimal endobronchial reinforcement implant.Bl 51. A method for treating a patient, comprising: determining a plurality of target airway segments in a first lung and a second lung of the patient, wherein the patient is identified as having a pulmonary disease; during a first procedure at a first time, placing an endobronchial reinforcement implant in each of at least a first portion of the target airway segments; and during a second procedure at a second time, placing an endobronchial reinforcement implant in each of at least a second portion of the target airway segments.B152. The method of clause B151, wherein the first portion of the target airway segments is in the first lung of the patient, and the second portion of the target airway segments is in the second lung.B 153. The method of clause B 151 , wherein the first portion of the target airway segments comprises at least one airway segment in the first lung and at least one airway segment in the second lung, and wherein the second portion of the target airway segments comprises at least one airw ay segment in the first lung or at least one airway segment in the second lung.B154. The method of any one of clauses B151-B153, further comprising receiving a follow-up set of lung metrics characterizing lung state of the patient between the first time and the second time.Bl 55. The method of clause Bl 54, wherein determining a plurality of target airway segments comprises: determining the first portion of the target airway segments prior to the first time based at least in part on a baseline set of lung metrics characterizing lung state of the patient prior to the first time, and determining the second portion of the target airway segments between the first time and the second time based at least in part on the follow-up set of lung metrics.B156. The method of clause B155, wherein the second portion of the target airwaysegments is based on a comparison of the follow-up set of lung metrics and the baseline set of lung metrics.B157. The method of clause B155 or B156, further comprising: determining a number of endobronchial reinforcement implants to place in the patient at the first time based at least in part on the baseline set of lung metrics, and determining a number of endobronchial reinforcement implants to place in the patient at the second time based at least in part on the follow-up set of lung metrics.B158. The method of any one of clauses B151-B157, wherein the elapsed time between the first time and the second time is about one month, about three months, about six months, or about twelve months.B159. The method of any one of clauses B151-B158, wherein the elapsed time between the first time and the second time is determined at least in part based on health of the patient, progression of the pulmonary disease following the first time, or both.B160. The method of any one of clauses B151-B159, wherein the endobronchial reinforcement implant is a minimal endobronchial reinforcement implant.BRIEF DESCRIPTION OF THE DRAWINGS[00061 Many aspects of the present disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily to scale. Instead, emphasis is placed on clearly illustrating the principles of the present disclosure.

[0007] FIG. 1 is a schematic illustration of a bronchial tree of a human subject within a chest cavity of the subject.

[0008] FIG. 2 is a schematic illustration of a bronchial tree of a human subject in isolation.[0009} FIG. 3 is an enlarged view of a terminal portion of the bronchial tree shown in FIG. 2.

[0010] FIG. 4 is a table showing examples of dimensions and generation numbers of different portions of a bronchial tree of a human subject.

[0011] FIG. 5 is a diagram showing lung volumes during normal lung function.

[0012] FIG. 6 is a table showing airway wall composition at different portions of a bronchial tree of a human subject.

[0013] FIG. 7 is an anatomical illustration of airway wall composition at different portions of a bronchial tree of a human subject.

[0014] FIG. 8 is an anatomical illustration showing small airway narrowing in emphysematous lung tissue.[0015| FIG. 9 is an anatomical illustration showing alveolar wall damage in emphysematous lung tissue.

[0016] FIG. 10 is an anatomical illustration showing normal air ay patency during exhalation in healthy lung tissue.[0017| FIG. 11 is an anatomical illustration showing airway collapse during exhalation in emphysematous lung tissue.[00181 FIG. 12 is an anatomical illustration showing normal acinar.

[0019] FIG. 13 is an anatomical illustration showing centriacinar emphysema.|0020] FIG. 14 is an anatomical illustration showing panacinar emphysema.|0021] FIG. 15 is an anatomical illustration showing paraseptal emphysema.

[0022] FIG. 16 is a side view of an implant in accordance with at least some embodiments of the present technology.

[0023] FIG. 17 is a schematic end view of the implant shown in FIG. 1 .

[0024] FIG. 18 is a side view of a portion of an implant in accordance with at least some embodiments of the present technology within an airway.

[0025] FIG. 19 is a block diagram providing a general overview of a w orkflow- for the selection of patients for treatment, and planning and monitoring a treatment procedure, in accordance with embodiments of the present technology.

[0026] FIG. 20 is a flow diagram illustrating a method for planning a treatment for a patient, in accordance with embodiments of the present technology.

[0027] FIG. 21 is a flow diagram illustrating a method for evaluating a treatment outcome of a patient, in accordance with embodiments of the present technology.

[0028] FIG. 22 is a flow diagram illustrating a method for updating the software algorithms of FIGS. 20 and 21, in accordance with embodiments of the present technology.

[0029] FIGS. 23 A and 24B are flow diagrams illustrating examples of generating various lung metrics based on inspiratory CT scans and expiratory CT scans, respectively.

[0030] FIG. 24 is a flow diagram illustrating an example of assessing treatment effect and / or efficacy based on a CT scan, in accordance with embodiments of the present technology.

[0031] FIG. 25A is an anatomical illustration showing a coronal view of fissures in the right and left lungs. FIG. 25B is an anatomical illustration showing a sagittal view- of fissures in the right lung. FIG. 25C is an anatomical illustration showing a sagittal view of fissures in the left lung.[00321 FIG. 26 is a flow diagram illustrating a method for normalizing quantitative CT results, in accordance with embodiments of the present technology.[00331 FIG. 27 is a flow diagram illustrating a method for evaluating a patient having or suspected of having a pulmonary disease, in accordance with embodiments of the present technology.

[0034] FIG. 28 is a flow diagram illustrating a method for planning a treatment for a patient having a pulmonary disease, in accordance with embodiments of the present technology7.

[0035] FIG. 29A is a flow diagram illustrating a method for evaluating a patient having or suspected of having a pulmonary disease, in accordance with embodiments of the present technology. FIG. 29B is a flow diagram illustrating information flow in an example of the method illustrated in FIG. 29 A. FIG. 29C is a flow diagram illustrating an example process of training a correction machine learning algorithm used in the method illustrated in FIG. 29A.

[0036] FIG. 30 is a flow diagram illustrating a method for evaluating a patient having or suspected of having a pulmonary7disease, in accordance with embodiments of the present technology.

[0037] FIG. 31 is a flow diagram illustrating a method for evaluating a patient having or suspected of having a pulmonary disease, in accordance with embodiments of the present technology.

[0038] FIG. 32 is a flow diagram illustrating a method for evaluating a patient having or suspected of having a pulmonary disease, in accordance with embodiments of the present technology7.

[0039] FIG. 33 is a flow diagram illustrating a method for planning a treatment for a patient having a pulmonary7disease, in accordance with embodiments of the present technology.

[0040] FIG. 34 is a flow diagram illustrating a method for planning a treatment for a patient having a pulmonary disease, in accordance with embodiments of the present technology.

[0041] FIG. 35 is a flow diagram illustrating a method of evaluating a patient having an endobronchial implant placed in a lung of the patient, in accordance with embodiments of the present technology.[0042 | FIG. 36 is a flowchart schematic of an example method for patient selection and treatment with an endobronchial reinforcement implant, in accordance with the present technology.|0043] FIG. 37 is a flowchart schematic of an example method for target airway segment selection for placement of an endobronchial reinforcement implant, in accordance with the present technology7.

[0044] FIG. 38 is an illustrative schematic of an arrangement including a patient selection system and a target selection system.

[0045] FIG. 39 is a table including example patient data for potential use in selection a patient for treatment with an endobronchial reinforcement implant, in accordance with the present technology.

[0046] FIG. 40 is a table of empirical example patient data reflecting responder rates for subjects treated with one or more endobronchial reinforcement implants.(0047] FIG. 41 is a flowchart schematic of an example method of treatment of a patient with one or more endobronchial reinforcement implants, in accordance with the present technology.

[0048] FIG. 42 is a flowchart schematic of an example method of treatment of a patient with one or more endobronchial reinforcement implants, in accordance with the present technology.DETAILED DESCRIPTION

[0049] The present technology relates to methods for planning, predicting, and / or monitoring treatment procedures for patients having a pulmonary disease, such as COPD. In some embodiments, for example, a method for planning a treatment for a patient includes receiving patient data including computed tomography (CT) data of a lung of the patient. The method can include generating a set of lung metrics by inputting the patient data into a first machine learning algorithm. The method can include predicting a response of the patient to treatment for the pulmonary disease (e.g., treatment with an endobronchial implant) by inputting the set of lung metrics into a second machine learning algorithm. The method can further include evaluating whether the patient is a candidate for the treatment for the pulmonary disease, based on the predicted response.

[0050] As another example, a method for evaluating a treatment outcome of a patient includes receiving patient data including CT data of a lung of the patient after placement of an endobronchial implant in the lung. The method can include generating a set of status metrics (e.g., lung metrics, implant metrics) by inputting the patient data into a first machine learning algorithm. The method can also include determining a response of the patient to the endobronchial implant by inputting the set of status metrics into a second machine learning algorithm. The method can further include predicting an outcome of the patient after the placement of the endobronchial implant by inputting the set of status metrics and / or the determined response into a third machine learning algorithm.

[0051] The present technology can provide numerous advantages for treatment of patients with pulmonary disease. For instance, the methods described herein can be used to diagnose and treat patients at an earlier stage of the disease (e.g., stage 2 COPD). which can improve therapeutic efficacy and lead to better outcomes. Additionally, the methods herein can provide patients with personalized, evidence-based treatment recommendations that are more likely to lead to successful results. The methods herein can also improve planning of treatment procedures, which can reduce procedure time, improve patient safety, and lead to improved outcomes. Moreover, the methods of the present technology can monitor the patient after the procedure to predict problems before they occur, thus reducing the frequency of additional hospitalization and doctor visits after the treatment procedure.

[0052] Embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings in which like numerals represent like elements throughout the several figures, and in which example embodiments are shown. Embodiments of the claims may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. The examples set forth herein are non-limiting examples and are merely examples among other possible examples.10053] The headings provided herein are for convenience only and do not interpret the scope or meaning of the claimed present technology. Embodiments under any one heading may be used in conjunction with embodiments under any other heading.I. Anatomy and Physiology

[0054] In normal respiration, the act of inhaling draws air into the lungs via the nose or mouth and the trachea. Within each lung, inhaled air moves into a branching network of progressively narrower airways called bronchi, and then into the narrowest airways calledbronchioles. The bronchioles end in bunches of tiny round structures called alveoli. Small blood vessels called capillaries run through the walls of the alveoli. When inhaled air reaches the alveoli, oxygen moves from the alveoli into blood in the capillaries. At the same time, carbon dioxide moves in the opposite direction, i.e., from blood in the capillaries into the alveoli. This process is called gas exchange. In a healthy lung, the airways and alveoli are elastic and stretch to accommodate air intake. When a breath is drawn in, the alveoli fill up with air like small balloons. When a breath is expelled, the alveoli deflate. This expansion of the alveoli is an important part of effective gas exchange. Alveoli that are free to expand exchange more gas than alveoli that are inhibited from expanding.100551 FIG. l is a schematic illustration of a bronchial tree of a human subj ect within a chest cavity' of the subject. As shown in FIG. 1, the bronchial tree includes a trachea T that extends downwardly from the nose and mouth and divides into a left main bronchus LMB and a right main bronchus RMB. The left main bronchus and the right main bronchus each branch to form lobar bronchi LB, segmental bronchi SB, and sub-segmental bronchi SSB, which have successively smaller diameters and shorter lengths as they extend distally. FIG. 2 is a schematic illustration of the bronchial tree in isolation. As shown in FIG. 2, the sub-segmental bronchi continue to branch to form bronchioles BO, conducting bronchioles CBO, and finally terminal bronchioles TBO, which are the smallest airways that do not contain alveoli. The terminal bronchioles branch into respiratory bronchioles RBO, which divide into alveolar ducts AD. FIG. 3 is an enlarged view of a terminal portion of the bronchial tree. As shown in FIG. 3, the alveolar ducts terminate in a blind outpouching including two or more small clusters of alveoli A called alveolar sacs AS. Various singular alveoli can be disposed along the length of a respiratory bronchiole as well.[00561 Bronchi and bronchioles are conducting airways that convey air to and from the alveoli. They do not take part in gas exchange. Rather, gas exchange takes place in the alveoli that are found distal to the conducting airways, starting at the respiratory' bronchioles. It is common to refer to the various airways of the bronchial tree as ‘"generations” depending on the extent of branching proximal to the airways. For example, the trachea is referred to as “generation 0” of the bronchial tree, various levels of bronchi, including the left and right main bronchi, are referred to as “generation 1,” the lobar bronchi are referred to as “generation 2,” and the segmental bronchi are referred to as “generation 3.” Further, it is common to refer to any of the airways extending from the trachea to the tenninal bronchioles as "conductingairways.” FIG. 4 is a table indicating examples of dimensions and generation numbers of different portions of the bronchial tree.[00571 The respiratory bronchioles, alveoli, and alveolar sacs receive air via more proximal portions of the bronchial tree and participate in gas exchange to oxygenate blood routed to the lungs from the heart via the pulmonary artery, branching blood vessels, and capillaries. Thin, semi-permeable membranes separate oxy gen-depleted blood in the capillaries from oxygen-rich air in the alveoli. The capillaries wrap around and extend between the alveoli. Oxygen from the air diffuses through the membranes into the blood. Carbon dioxide from the blood diffuses through the membranes to the air in the alveoli. The newly oxygen-enriched blood then flows from the alveolar capillaries through the branching blood vessels of the pulmonary venous system to the heart. The heart pumps the oxygen-rich blood throughout the body. The oxy gen-depleted air in the lungs is exhaled when the diaphragm and intercostal muscles relax and the lungs and chest wall elastically return to their normal relaxed states. In this manner, air flows through the branching bronchioles, segmental bronchi, lobar bronchi, main bronchi, and trachea, and is ultimately expelled through the mouth and nose.[0058| FIG. 5 is a diagram showing lung volumes during normal lung function. Approximately one-tenth of the total lung capacity is used at rest. Greater amounts are used as needed (e.g., with exercise). Tidal Volume (TV) is the volume of air breathed in and out without conscious effort. The additional volume of air that can be exhaled with maximum effort after a normal inspiration is Inspiratory Reserve Volume (IRV). The additional volume of air that can be forcibly exhaled after normal exhalation is Expiratory' Reserve Volume (ERV). The total volume of air that can be exhaled after a maximum inhalation is Vital Capacity (VC). VC equals the sum of the TV, IRV. and ERV. Residual Volume (RV) is the volume of air remaining in the lungs after maximum exhalation. The lungs can never be completely emptied. The Total Lung Capacity (TLC) is the sum of the VC and RV. Evaluation of lung function may be used to determine a patient’s eligibility for therapy, as well as to evaluate a therapy’s effectiveness.|0059] FIG. 6 is a table showing airway wall composition at different portions of a bronchial tree. FIG. 7 is an anatomical illustration of airway wall composition at different portions of a bronchial tree. As shown in FIGS. 6 and 7, the walls of the bronchi, bronchioles, alveolar ducts and alveoli include epithelium, connective tissue, goblet cells, mucous glands, club cells, smooth muscle elastic fibers, and hyaline cartilage with nerves, blood vessels, and inflammatory' cells interspersed throughout. Most of the epithelium (from the nose to the bronchi) is covered in ciliated pseudostratified columnar epithelium, commonly calledrespiratory epithelium. The cilia located on these epithelium beat in one direction, moving mucous and foreign material such as dust and bacteria from the more distal airways to the more proximal airways and eventually to the throat, where the mucus and / or foreign material are cleared by swallowing or expectoration. Moving down the bronchioles, the cells are more cuboidal in shape but are still ciliated.[0060 | The proportions and properties of various components of the airway wall vary depending on the location within the bronchial tree. For example, mucous glands are abundant in the trachea and main bronchi but are absent starting at the bronchioles (e.g., at approximately generation 10). In the trachea, cartilage presents as C-shaped rings of hyaline cartilage, whereas in the bronchi the cartilage takes the form of interspersed plates. As branching continues through the bronchial tree, the amount of hyaline cartilage in the w alls decreases until it is absent in the bronchioles. Smooth muscle starts in the trachea, where it joins the C-shaped rings of cartilage. It continues down the bronchi and bronchioles, which it completely encircles. Instead of hard cartilage, the bronchi and bronchioles are composed of elastic tissue. As the cartilage decreases, the amount of smooth muscle increases. The mucous membrane also undergoes a transition from ciliated pseudostratified columnar epithelium to simple cuboidal epithelium to simple squamous epithelium.[0061 j FIGS. 25A-25C are anatomical illustrations of fissures of the right and left lungs. The right horizontal fissure separates the right upper lobe (RUL) and the right middle lobe (RML). The right oblique fissure divides the right middle lobe (RML) and the right lower lobe (RLL), and separates the RUL and the RLL posteriorly. The left oblique fissure separates the left upper lobe (LUL) and the left lower lobe (LLL). In normal, healthy tissue, pulmonary fissures are double layers of infolded invaginations of visceral pleura, and exit between the different lobes. Additionally, there are segmental fissures (not shown) separating 18 segments across the five lobes (RUL, RML, RLL, LUL, LLL). How ever, the appearance of fissures may vary' widely from patient to patient, and may be incomplete or even absent and / or distorted (in position, shape, etc.) due to diseases such as COPD. Integrity or degree of completeness of fissures indicates how well-separated the lung lobes are. Incomplete fissures may, for example, indicate that air from one lobe can flow into another (e.g., collateral ventilation). CT imaging may be used to visualize certain features of lung fissures, though different CT protocols may lead to different appearances of the fissure. One or more various algorithms may be used to automatically’ identify and / or characterize lung fissures in CT imaging, such as fissure segmentation algorithms (e.g., an algorithm to perform implicit surface fitting to a surfaceshaped structure of the lung volume, a trained machine learning algorithm such as a supervised fissure enhancement filter, an algorithm to perform adaptive fissure sweeping and wavelet transform, etc.) and / or algorithms based on anatomical knowledge (e.g., fuzzy reasoning system to search fissures based at least in part on ridgeness image intensity and smoothness, etc.), and / or other suitable algorithms for fissure characterization.II. Pulmonary Disease

[0062] COPD is a major public health issue. There are over one million patients in the United States alone with severe emphysema and severe hyperinflation. An overwhelming majority of these patients are underserved by currently available treatments. The global unmet clinical need, including in countries with high incidence of respiratory' disease due to smoking, is many times greater than in the United States.

[0063] In COPD-affected lung tissue, less air flows through the airways for a variety of reasons. The airways and / or alveoli may be relatively inelastic, the walls between the alveoli may be damaged or destroyed, the walls of the airways may be thick or inflamed, and / or the airways may generate excessive mucus resulting in mucus buildup and airway blockage. In a ty pical case of COPD, the disease does not equally affect all airways and alveoli in a lung. A lung may have some regions that are significantly more affected than other regions. In severe cases, the airways and alveoli that are unsuitable for effective gas exchange may make up 20 to 30 percent or more of total lung volume.|0064] The effects of COPD are often most pronounced when a patient exercises or engages in other physical exertion that would cause a healthy person to breath heavily. A patient with COPD may not be able to breathe heavily because diseased portions of the patient’s lungs trap air, resulting in an inability to exhale completely. This, in turn, inhibits subsequent expansion of healthy lung tissue. Thus, during exercise or other physical exertion, the lungs of a COPD patient may operate in a state of dynamic hyperinflation that impairs respiratory mechanics and increases the work of breathing. Hyperinflation of the lungs may also hinder cardiac filling, lead to dyspnea, and / or reduce a patent’s exercise performance. These and / or other detrimental effects of COPD can lead to a cascade of symptoms that eventually impairs a patient’s quality of life and increases the risk of severe disability and death.

[0065] The term COPD includes both chronic bronchitis and emphysema. About 25% of COPD patients have emphysema. About 40% of these emphysema patients have severe emphysema. Furthermore, it is common for COPD patients to have symptoms of both chronicbronchitis and emphysema. In chronic bronchitis, the lining of the airways is inflamed, generally as a result of ongoing irritation. This inflammation results in thickening of the airway lining and in production of a thick mucus that may coat and eventually congest the airways. Emphysema, in contrast, is primarily a pathological diagnosis concerning abnormal permanent enlargement of air spaces distal to the terminal bronchioles. In emphysematous lung tissue, the small airways and / or alveoli typically have lost their structural integrity’ and / or their ability to maintain an optimal shape. For example, damage to or destruction of alveolar walls may have resulted in fewer, but larger alveoli. This may significantly impair normal gas exchange. Within the lung, focal or “diseased” regions of emphysematous lung tissue characterized by a lack of discernible alveolar walls may be referred to as pulmonary' bullae. These relatively inelastic pockets of dead space are often greater than 1 cm in diameter and do not contribute significantly to gas exchange. Pulmonary bullae tend to retain air and thereby create hyperinflated lung sections that restrict the ability of healthy lung tissue to fully expand upon inhalation. Accordingly, in patients with emphysema, not only does the diseased lung tissue no longer contribute significantly to respiratory’ function, it impairs the functioning of healthy lung tissue.[0066| FIG. 8 is an anatomical illustration showing small airway narrowing in emphysematous lung tissue. FIG. 9 is an anatomical illustration showing alveolar wall damage in emphysematous lung tissue. FIG. 10 is an anatomical illustration showing normal airway patency during exhalation. FIG. 11 is an anatomical illustration showing airway collapse during exhalation in emphysematous lung tissue. COPD, and emphysema in particular, is characterized by irreversible destruction of the alveolar walls that contain elastic fibers that maintain radial outward traction on small airways and are useful in inhalation and exhalation. As shown in FIGS. 8-11, when these elastic fibers are damaged, the small airways are no longer under radial outward traction and collapse, particularly during exhalation. Furthermore, emphysema destroys the alveolar walls. As shown in FIG. 9, this results in one larger air space and reduces the surface area available for gas exchange. The lungs are thus unable to perform gas exchange at a satisfactory' rate, which causes a reduction in oxygenated blood. Additionally, the large air spaces of diseased lung combined with collapsed airways results in hyperinflation (air trapping) of the lung and an inability to fully exhale. Moreover, the hyperinflated lungs apply continuous pressure on the chest wall, diaphragm, and surrounding structures, which causes shortness of breath and can prevent a patient from walking short distances or performing routine tasks. Both quality’ of life and life expectancy’ for patients with late-stage emphysema are extremely low7, with fewer than half of patients surviving an additional five years.[0067| There are three ty pes of emphysema: centriacinar, panacinar, and paraseptal. FIG. 12 is an anatomical illustration showing normal acinar. FIG. 13 is an anatomical illustration showing centriacinar emphysema, which involves the alveoli and airways in the central acinus, including destruction of the alveoli in the walls of the respiratory bronchioles and alveolar ducts. FIG. 14 is an anatomical illustration showing panacinar emphysema, which is characterized by destruction of the tissues of the alveoli, alveolar ducts, and respiratory bronchioles. This produces a fairly uniform dilatation of the air space throughout the acini and evenly distributed emphysematous changes across the acini and the secondary' lobules. FIG. 15 is an anatomical illustration showing paraseptal emphysema, which is characterized by enlarged airspaces at the periphery of acini resulting predominately from destruction of the alveoli and alveolar ducts. The distribution of the paraseptal emphysema is usually limited in extent and occurs most commonly along the posterior surface of the upper lung. It often coexists with other forms of emphysema.[0068J Emphysema can also be characterized as heterogenous or homogenous. Generally, heterogenous emphysema in a lung (right or left) is characterized by any two or more regions (e.g., lobes, segments) having a relative difference of emphysema destruction above a threshold amount, while homogenous emphysema in a lung is characterized by any two or more regions (e.g., lobes, segments) having a relative difference of emphysema destruction below a threshold amount. In some patients, both right and left lungs may be heterogenous or homogenous, or one lung may be heterogenous while the other lung may be homogenous.[0069| Pharmacological treatment can be prescribed for COPD. A treatment regimen of bronchodilators. B2-agonists. muscarinic agonists, corticosteroids, or combinations thereof may provide short term alleviation of the symptoms of COPD. Non-pharmaceutical management solutions, such as home oxygen, non-invasive positive pressure ventilation, and pulmonary rehabilitation, can also be used. Another treatment option for patients with severe emphysema is lung volume reduction surgery (LVRS). This surgery involves removing poorly functioning portions of a lung (typically up to 20 to 25 percent of lung volume) thereby reducing the overall size of the lung and making more volume within the chest cavity available for expansion of relatively healthy lung tissue. With greater available volume for expansion, the lung tissue remaining after LVRS has an enhanced capacity for effective gas exchange.[00701 Procedures for lung volume reduction w ithout surgical removal of diseased lung tissue also exist. Examples include use coils or clips to seize and physically compact diseasedlung tissue. These procedures can reduce the overall volume of a lung for an effect similar to that of LVRS. Another device-based treatment for COPD involves placement of onedirectional stent valves in airways proximal to emphysematous tissue. These valves allow air to flow out of but not into overinflated portions of the lung. Although not conventionally used to treat COPD, stents are sometimes used in the lumen of the central airways (e.g., the trachea, main bronchi, lobar bronchi, and / or segmental bronchi) to temporarily improve patency of these airways. For example, stents may be used to temporarily improve patency in a central airway affected by a benign or malignant obstruction.[00711 Steam / vapor therapy, such as bronchoscopic thermal vapor ablation (BTVA), is yet another COPD treatment option. BTVA involves introducing heated water vapor into diseased lung tissue. This produces a thermal reaction leading to an initial localized inflammatory’ response followed by permanent fibrosis and atelectasis. Similar to thermal treatments like BTVA, there are also biochemical treatments that involve injecting glues or sealants into diseased lung tissue. Both thermal and biochemical procedures may precipitate remodeling that results in reduction of tissue and air volume at targeted regions of hyperinflated lung.

[0072] Some other known COPD treatments involve bypassing an obstructed airway. For example, a perforation through the chest wall into the outer portions of the lung can be used to create a direct communication (e.g., a bypass tract) between diseased alveoli and the outside of the body. If no other steps are taken, these bypass tracts will typically close by normal healing or by the formation of granulation tissue. Accordingly, placing a tubular prosthetic in the bypass tract can temporarily extend the therapeutic benefit.III. Endobronchial Implants

[0073] In some embodiments, the present technology provides endobronchial placement of an implant to establish or improve airway’ patency (also referred to herein as “endobronchial implant therapy”). The implant can incorporate features designed to minimize or reduce foreign body reaction, such as minimal surface area coverage (or at least significantly reduced surface area coverage compared to other types of implants), an open helical structure, and / or high contrast between outward radial force and flexibility’ along the longitudinal axis. Such endobronchial implants may also be referred to herein as minimal airway reinforcement implants or minimal endobronchial reinforcement implants. A minimal endobronchial reinforcement implant can be advantageous compared to other types of endobronchial implants(e.g., valves, airway stents) whose therapeutic effect can be undermined due to foreign body reaction (e.g., granulation tissue reaction, mucous impaction, airway constriction).[0074| The implant can be placed at a treatment location including a previously collapsed airway, such as a previously collapsed distal airway. Deployment of the implant can release air trapped in a hyperinflated portion of the lung and / or reduce or prevent subsequent trapping of air in this portion of the lung. In at least some cases, it is desirable for a treatment location at which an implant is deployed to include an airway of generation 4 or higher / deeper, such as (from distal to proximal) the respiratory bronchioles, terminal bronchioles, conducting bronchioles, bronchioles or sub-segmental bronchi and then run proximally to a more central, larger airway (e.g., 6th generation or more proximal / lower) such as (from distal to proximal) sub-segmental bronchi, segmental bronchi, lobar bronchi and main bronchi. A single implant may create a contiguous path distal to proximal to reliably create passage for the trapped air. In an alternative embodiment, multiple, discrete implants can be used instead of a single, longer implant. The multiple, discrete implants may be placed in bronchial airways that have collapsed or are at risk of collapse. The use of multiple, discrete implants in select locations in the bronchial tree may have the advantage of using less material, thereby reducing contact stresses and foreign body response and allow for greater flexibility and customization of therapy. For example, whereas a single implant embodiment may run from a higher generation airway distally to a lower generation airway proximally, a system of multiple, discrete implants may allow for placement of implants in multiple airw ays of the same generation.

[0075] The devices, systems and methods described herein may be administered to different bronchopulmonary segments in order to release trapped air from regions of the lung in the safest and most efficient manner possible. For example, treatment of the left lung may involve one or more of the following segments: Upper Lobe (Superior: apical-posterior, anterior; Lingular: superior, inferior); Lower Lobe: superior, antero-medial basal, lateral basal. Treatment of the right lung may involve one or more of the following segments: Upper Lobe: apical, anterior, posterior; Middle Lobe: medial, lateral; Lower Lobe: superior, anterior basal, lateral basal. The treatments described herein may involve placement of a single implant in a single lung (right or left), a single implant in each lung or multiple implants in each lung. Treatment within a particular lung may involve placing an implant in a specific lobe (e.g., upper lobe) and a specific segment within such lobe or it may involve placement of at least one implant in multiple lobes, segments within a lobe or sub-segments within a segment. Determination of which parts of the lung to treat can be made by the clinical operator (e.g.,pulmonologist or surgeon) with the assistance of imaging (e.g., CT, ultrasound, radiography, or bronchoscopy) to assess the presence and pathology of disease and impact on pulmonary function and airflow dynamics.|0076] FIGS. 16 and 17 illustrate an example of a minimal endobronchial reinforcement implant configured as an expandable device 100 for placement in an airway lumen. In particular, FIG. 16 is a side view of the expandable device 100 in an expanded, unconstrained state, and FIG. 17 is an end view of the device 100. As shown in FIG. 16, the device 100 can comprise a generally tubular structure configured to be positioned within an airway lumen. For example, the device 100 may be configured to be implanted in an airway lumen such that the device 100 maintains a lumen of a minimum desired diameter in the airway. The device 100 has a first end portion 100a, a second end portion 100b opposite the first end portion 100a, and a central longitudinal axis LI extending between the first and second end portions 100a, 100b. As used herein, the term “longitudinal” can refer to a direction along an axis that extends through the lumen of the device while in a tubular configuration, the term “circumferential” can refer to a direction along an axis that is orthogonal to the longitudinal axis and extends around the circumference of the device when in a tubular configuration, and the term “radial” can refer to a direction along an axis that is orthogonal to the longitudinal axis and extends toward or away from the longitudinal axis.| 077] The device 100 can comprise an elongated member 102 wound about the longitudinal axis LI of the device 100. In some embodiments, the elongated member 102 is heat set in a novel three-dimensional (3D) configuration such that the elongated member 102 is configured to self-expand to the preset configuration. In some embodiments, the elongated member 102 is not heat set and / or configured to self-expand. For example, the elongated member 102 is balloon-expandable. In some embodiments, the elongated member 102 is balloon-expandable and self-expanding. The elongated member 102 has a first end 102a and a second end 102b opposite the first end 102a along a longitudinal axis L2 of the elongated member 102. The elongated member 102 can comprise a wire, a coil, a tube, a filament, a single interwoven filament, a plurality of braided filaments, a laser-cut sheet, a laser-cut tube, a thin film formed via a deposition process, and other suitable elongated structures and / or methods, such as cold working, bending, EDM, chemical etching, water jet, etc. The elongated member 102 can be formed using materials such as nitinol, stainless steel, cobalt-chromium alloys (e.g., 35N LT®, MP35N (Fort Wayne Metals, Fort Wayne. Indiana)). Elgiloy, magnesium alloys, tungsten, tantalum, platinum, rhodium, palladium, gold, silver, orcombinations thereof, or one or more polymers, or combinations of polymers and metals. In some embodiments, the elongated member 102 may include one or more drawn- filled tube (‘ DFT”) wires comprising an inner material surrounded by a different outer material. The inner material, for example, may be radiopaque material, and the outer material may be a superelastic material.

[0078] Although the device 100 shown in FIG. 16 comprises a single elongated member 102, the device 100 may comprise any number of elongated members 102. A single elongated member, such as a single wire expandable device, can be easier to remove and / or reposition as the operator can grab the elongated member on one end and pull it through a working channel of a scope. The elongated member will straighten out in either the balloon expandable or self-expanding form.|0079] Referring to FIG. 16, the elongated member 102 may be wound about the longitudinal axis LI of the device 100 in a series of windings or loops 104, four of which are shown in FIG. 16 and individually labeled 104a-104d. Each of the loops 104 can extend around the longitudinal axis LI of the device 100 between a first end 106 and a second end 108. In some embodiments, the loops 104 are connected end to end such that, for example, a second end 108 of the first loop 104a is the first end 106 of the second loop 104b. The second end 108 can be disposed approximately 360 degrees from the first end 106 about the longitudinal axis LI of the device 100. That is, the first and second ends 106, 108 can be disposed at generally equivalent circumferential positions relative to the longitudinal axis LI of device 100. In some embodiments, the device 100 has a circular cross-sectional shape. In other embodiments, the device 100 may have other suitable cross-sectional shapes (e.g., oval, square, triangular, polygonal, irregular, etc.). The cross-sectional shape of the device 100 may be generally the same or vary along the length of the device 100 and / or from loop to loop.

[0080] The expanded cross-sectional dimension of the device 100 may be generally constant or vary along the length of the device 100 and / or from loop to loop. For example, as discussed herein, the device 100 can have varying cross-sectional dimensions along its length to accommodate different portions of the airway. For instance, the device 100 can have a first cross-sectional dimension along a first portion configured to be positioned in a more distal portion of the airw ay (such as, for example, in a terminal bronchiole and / or emphysematous areas of destroyed and / or collapsed airways), and a second cross-sectional dimension along a second portion configured to be positioned more proximally (such as in a primary bronchus and / or another portion that has not collapsed). The second portion, for example, can beconfigured to be positioned in a portion of the airway that is less emphysematous than the collapsed distal portion and / or has cartilage in the airway wall (preferably rings of cartilage and not plates), which can occur at the lobar (generation 2) or segmental (generation 3) level.|0081] In some embodiments, the expanded cross-sectional dimension of the device 100 in an unconstrained (e g., removed from the constraints of a catheter or airway), expanded state is oversized relative to the diameter of the native airway lumen. For example, the expanded, unconstrained cross-sectional dimension of the device 100 can be at least 1.5X the original (non-collapsed) diameter of the airway lumen in which it is intended to be positioned. In some embodiments, the device 100 has an expanded, cross-sectional dimension that is about 1.5X to 6X, 2X to 5X, or 2X to 3X the diameter of the original airway lumen. Without being bound by theory, it is believed that expanding the airway lumen to the greatest diameter possible without tearing the airway wall will provide the greatest improvement in pulmonary function (for example, as measured by outflow. FEV, and others).

[0082] As shown in FIG. 16, the elongated member 102 may undulate along its longitudinal axis L2 as it winds around the longitudinal axis LI of the device 100, forming a plurality of alternating peaks 110 (closer to the second end portion 100b of the device 100) and valleys 112 (closer to the first end portion 100a of the device 100). At least some of the valleys 112 can be at different locations along the longitudinal axis LI of the device 100 than at least some of the peaks 110. Additionally or alternatively, at least some of the valleys 112 can be at different longitudinal locations than at least some others of the valleys 112 and / or at least some of the peaks 110 can be at different longitudinal locations than at least some others of the peaks 110.

[0083] As an example, three peaks 110 and four valleys 112 of the first loop 104ahave been individually labeled as peaks 110a-l 10c and valleys 112a-d. As shown in FIGS. 16 and 17, for the first loop 104a in the direction of the wind W, the elongated member 102 extends from the first end 106 of the elongated member 102, which comprises a first valley 112a of the first loop 104a, to a first peak 110a of the first loop 104a along a first longitudinal direction toward the second end portion 100b of the device 100. The elongated member 102 can then extend from the first peak 110a to a second valley 112b along a second longitudinal direction opposite of the first longitudinal direction, from the second valley 112b to a second peak 110b along the first longitudinal direction, from the second peak 110b to a third valley 112c along the second longitudinal direction, from the third valley 112c to a third peak 110c along the first longitudinal direction, and from the third peak 110c to a fourth valley 112d (which is also thesecond end 108 of the first loop 104a) along the second longitudinal direction. Thus, when traveling in a direction of the wind W around a given loop 104, the loop 104 does not consistently progress from the first end portion 100a of the device 100 to the second end portion 100b of the device 100 (or vice versa), but rather undulates so that along certain portions of its length, the loop 104 becomes progressively closer to the first end portion 100a of the device 100, and along other portions of its length the loop becomes progressively closer to the second end portion 100b of the device 100.

[0084] Although the first and second ends 106, 108 of one of the loops 104 may be generally aligned circumferentially, the first and second ends 106, 108 are longitudinally offset. The first peak 110a can be closer to the second end portion 100b of the device 100 than the first valley 112a. The second valley 112b can be closer to the first end portion 100a of the device 100 than the first peak 110a and / or the first valley 112a. The second peak 110b can be closer to the second end portion 100b of the device 100 than the second valley 112b, the first peak 110a, and / or the first valley 112a. The third valley 112c can be closer to the first end portion 100a of the device 100 than the second peak 110b and / or closer to the second end portion 100b of the device 100 than the first valley 112a and / or the second valley 112b. In some embodiments, the third valley 112c can be substantially longitudinally aligned with the first peak 110a. The third peak 110c can be closer to the second end portion 100b of the device 100 than the third valley 112c, the second peak 110b, the second valley 112b, the first peak 110a, and / or the first valley 112a. The fourth valley 112d can be closer to the first end portion 100a of the device 100 than the third peak 110c and / or closer to the second end portion 100b of the device 100 than the third valley 112c, the second valley 112b, the first peak 110a, and / or the first valley 112a. In some embodiments, the fourth valley 112d can be substantially longitudinally aligned with the second peak 110b.

[0085] Although FIGS. 16 and 17 show a device 100 comprising four loops 104, each having four peaks 110 and four valleys 112, in some embodiments one or more of the loops 104 has more or fewer peaks 110 and / or more or fewer valleys 112. For example, in some embodiments one or more of the loops 104 has one, two, three, four, five, six, seven, eight, etc. peaks 110 per loop 104 and one, two, three, four, five, six, seven, eight, etc. valleys 112 per loop 104. The loops 104 may have the same or a different number of peaks 110, and the loops 104 may have the same or a different number of valleys 112. A circumferential distance (e.g.. an angular separation) between adjacent ones of the peaks 1 10 and valleys 112 can be uniform or non-uniform in a given loop 104. In some embodiments, adjacent ones of thepeaks 110 and valleys 112 can be spaced apart around a circumference of the device 100 by about 90 degrees, about 120 degrees, about 150 degrees, about 180 degrees, about 210 degrees, about 240 degrees, about 270 degrees, about 300 degrees, and / or about 330 degrees. In addition, the amplitude of the peaks 110 may be the same or different along a given loop 104 and / or amongst the loops 104, and the amplitude of the valleys 112 may be the same or different along a given loop 104 and / or amongst the loops 104. Moreover, the peaks 110 and valleys 112 can have the same or different amplitudes.[0086| As shown in FIG. 16. a portion of the elongated member 102 between adjacent peaks 110 and valleys 112 can be linear, curved, or both. Adjacent portions of the elongated member 102 between two sets of adjacent peaks 110 and valleys 112 can form a V-shaped and / or U-shaped structure. At least some of the valleys 112 can be concave toward the second end portion 100b of the device 100 and / or at least some of the peaks 110 can be concave toward the first end portion 100a of the device 100.[0087 j In some embodiments, for example as shown in FIG. 16, the elongated member 102 can extend around a circumference of the device 100 and / or along a longitudinal axis LI of the device 100, without substantially extending radially away or towards the longitudinal axis LI. Still, in some embodiments, a device 200 can comprise an elongated member 202 that undulates radially with respect to a longitudinal axis LI of the device 200. As shown in FIG. 18, for example, the elongated member 202 can form peaks 204 and / or valleys 204 that are located closer to the longitudinal axis LI than intermediate portions of the elongated member 202 between the peaks 204 and valleys 204. The apices of each “V” can be bent radially inward toward the center of the lumen, so that only the longitudinally-extending portions of the elongated member 202 are touching the bronchial wall. Such a configuration can prevent the stent from impeding mucus flow along the wall of the bronchus.[0088| The radial mechanism of expansion allows the expandable device 200 to be easily designed and delivered by both self-expansion and balloon-expansion. The zig-zag pattern of the devices disclosed herein, including the example shown in FIG. 16, is configured to confonn to different diameter airways with a single design, whereas conventional coils are a fixed diameter. This is especially advantageous for achieving gradual airway dilation over time. The expandable device stores expansion potential in the implant design which is achieved via beams that bend and elastic potential is established. The expandable device in balloon expandable form also has a unique potential to form a coil by expanding the zig-zags all the way to a straight line when geometrically designed this way.[00891 In some embodiments, the devices described herein include at least one material configured to facilitate visualization, such as a radiopaque material. The material can be the same material used to form the device, or can be incorporated into the device via doping, coating, attachment of a separate component incorporating the material (e.g., a radiopaque marker), etc.

[0090] Additional examples of devices suitable for use w ith the present technology are described in International Application No. PCT / US2022 / 073962, the disclosure of which is incorporated herein by reference in its entirety.IV. Methods for Patient Selection. Treatment Planning, and Monitoring[0091 [ FIG. 19 is a block diagram providing a general overview7of a workflow 1900 for the selection of patients for treatment as well as planning and monitoring a treatment procedure, in accordance with embodiments of the present technology. For example, the treatment procedure can be a procedure for treating a patient having a pulmonary disease (e.g., COPD) by placing one or more implanted devices into one or both lungs of the patient. The devices can be any of the embodiments of endobronchial implants described herein, such as a minimal endobronchial reinforcement implant.

[0092] As shown in FIG. 19, the workflow71900 can be divided into a pre-procedure phase 1902, a peri -procedure phase 1904, and a post-procedure phase 1906. In some embodiments, the pre-procedure phase 1902 occurs before the patient has been diagnosed with a pulmonary disease. Alternatively, the pre-procedure phase 1902 can occur after the patient has been diagnosed with the pulmonary7disease, but before the patient has received treatment (e.g., endobronchial implant therapy and / or other therapy) for the pulmonary disease.

[0093] The pre-procedure phase 1902 can involve determining whether the patient is a candidate for a treatment for the pulmonary disease (block 1908). For example, the treatment can be or include an airway treatment for COPD. The airway treatment can include pharmacological treatment (e.g., bronchodilators), interventional treatment (e.g., implant and / or non-implant procedures), or combinations thereof. In some embodiments, the interventional treatment includes vapor therapy (e.g., BTVA), administration of a sealant, transbronchial fenestration (e.g.. airway bypass stents), and / or endobronchial implant therapy (e.g., placement of an endobronchial coil, placement of an endobronchial valve, and / or placement of a minimal endobronchial reinforcement implant).[0094| The process of block 1908, also referred to herein as "patient selection,” can involve analyzing patient data (e.g.. CT data and / or other image data, medical records, questionnaires, other diagnostics) to assess the current state of the patient. For instance, the patient selection process can determine whether the patient has or is at risk of developing a pulmonary disease, and, optionally, the disease profde (e.g., type, locations, severity). Optionally, patient data obtained over time can be used to track disease progression. The patient selection process can also involve evaluating whether the patient is a good candidate for a particular treatment for the pulmonary disease, e.g., the predicted likelihood of achieving a successful treatment outcome with the treatment.[0095 In some embodiments, the patient selection process involves analyzing patient data and predicting the degree of response, based on factors such as homogeneity / heterogeneity of emphysema and / or collateral ventilation, locations of diseased portions (e.g.. tissue destruction, air trapping), proximity to anatomical structures (e.g., pleura, heart, nodules), proximity to other medical devices, appropriate bronchial pathways to target(s), alveolar collapse, diaphragm movement, where air flows in response to breath and / or flow patterns, and / or calculated measures of lung volume and / or health (e.g.. RV, TLC, RV / TLC ratio). Optionally, the patient selection process can involve screening the patient data for exclusionary characteristics, such as tumors, lesions, giant bullae, central airway collapse, etc.1 096] In some embodiments, the patient selection process involves predicting patient outcomes by comparing the patient’s disease profile against aggregated, normalized data of other patients. This approach can be used to predict how the patient’ s quality of life may decline over time (e.g., creating trendlines), and / or how the patient may respond to different treatments (e.g.. pharmacological; interventional such as valves, coils, steam, hydrogel glue, thermal ablation, non-thermal ablation; surgical) in comparison with endobronchial implant therapy using a minimal endobronchial reinforcement implant.|0097J In some embodiments, the patient selection process of block 1908 is implemented at least in part by a patient engagement utility . The patient engagement utility can be a software module that administers an automated questionnaire relating to symptoms and / or quality of life metrics. Based on the patient's answers, the patient engagement utility can put in orders for imaging (e.g., CT, X-Ray, magnetic resonance imaging (MRI), single-photon emission computerized tomography (SPECT), bronchoscopy) and / or tests (spirometry, arterial blood gas). Based on the image data and / or test results, the patient engagement utility' can generate an interactive patient report that provides a COPD ‘“risk” assessment (e.g., X%likelihood that the patient has COPD) and a referral to a respiratory physician. The patient report can also include information on available treatments and predictions of potential benefit of the treatments, based on the patient’s provisional disease profile. For instance, the patient report can provide personalized, evidence-based recommendations that are understandable by laypersons (e.g., if the patient gets treatment, their quality of life can be restored by X%, they will be able to walk two flights of stairs versus one flight previously), which can promote patient driven market development.[00981 Optionally, the patient engagement utility can send the referral to the respiratory physician, along with a detailed physician report with the data, analytics, and risk assessment generated for the patient. The physician report can include a preliminary diagnosis, recommendations for additional and / or confirmatory testing, and / or predictive analytics related to progression of disease, responsiveness to medical and / or pharmacological treatment, interventional treatment, surgery, etc. Optionally, the physician report can also include a referral to an interventionalist, if appropriate.[0099| If the patient is determined to be a good candidate for treatment, the preprocedure phase 1902 can generate a plan for a treatment procedure for the patient (block 1910). The process of block 1910, also referred to herein as “procedure planning,” can involve determining parameters of a treatment plan. For instance, parameters for endobronchial implant therapy (e.g., using a minimal endobronchial reinforcement implant) can include the number of implants, implant size (e.g., length, outer diameter), implant geometry, other implant characteristics (e.g., flexibility, material, design features), placement location (e.g., which airway(s), segment(s), sub-segment(s), lobe(s), etc. to place the implant in), placement pathway, and / or other treatment solutions that can be used in combination with endobronchial implant therapy (e.g., medications, surgery, other medical devices). The selection of the implant configuration can be based on factors such as the outer diameter of the target airway, wall thickness, lumen inner diameter, proximity to anatomical structures and / or the airwaygeneration. The treatment plan can also include information such as the airway(s) to place the implant in, how the implants are predicted to supply air to areas of the lung, airway wall structure and sizing, how airway wall structure and sizing is predicted to interface with the implant, proximity to structures, airway mechanical strength (e.g., radius, wall thickness), and / or types of tissues (e.g., fat, muscle, connective tissue — which may suggest implant characteristics). In some embodiments, the procedure planning process is part of a software tool used by an interventionalist.[0100| In some embodiments, the procedure planning process involves modeling the patient’s lungs, e.g.. before and after implant placement. For instance, a 3D model of the bronchial tree can be optionally generated from CT data and / or other image data. In some embodiments, CT data can identify the most diseased lobes (and / or segments, sub-segments, etc.) and help with identifying incomplete fissures, but may be limited in identifying the exact airways that are most impactful due to resolution, and thus may be combined with higher resolution imaging modalities such as MRI to allow for assessment of specific airways. Moreover, it may not be necessary to determine a precise airway target since any opened airway that creates a connected expressway from peripheral to a proximal airway having integrity may be sufficient to release trapped air, particularly if collateral ventilation further facilitates air movement through parenchyma.

[0101] The 3D model can be used to model various treatment options and the associated results (e.g., placement of a valve at a target location produces an X% increase in FEVi, whereas placement of a minimal endobronchial reinforcement implant produces a Y% increase in FEVi). The modeling results can be used to select the appropriate therapy type, target location(s) for implant placement (e.g., target airways, segments, and / or lobe), and / or sequence of target locations. Modeling techniques (e.g.. computational flow dynamics, AI- based approaches) can also be used to identify optimal target airways and / or predict an optimal implant plan based on factors such as predicted effectiveness for removing trapped air, regions with trapped air, presence or proximity to incomplete fissures, safety for avoiding iatrogenic injury (e.g., damage to pleura, blood vessel, organs), safety and durability for reducing implant fatigue, safety and effectiveness for reducing risk of friction against adjacent implants, minimizing the number of implants (e.g., for purposes of safety, ease of use, reducing procedure time), minimizing the amount of foreign material (e.g., for safety7purposes), minimizing the number of bends, minimizing the risk of implant rejection or migration (e.g., due to coughing), and / or minimizing procedure time. Modeling can also be used to predict how the lung may behave with endobronchial implant therapy. For example, modeling can be used to predict which lung segments will have reduced air trapping and, accordingly, contribute less to residual volume compared to the baseline (e.g., pre-treatment) measurements, and / or which lung segments may contribute more to improved pulmonary function. It is anticipated that lung segments that have less disease and are proximate or adjacent to lung segments with substantial air trapping may be able to expand substantially compared to baseline and, accordingly, may contribute most to improved pulmonary function. Additionally, modeling may be useful inpredicting risk of pneumothorax. Analysis of inspiratory and expiratory' CT may be able to detect the presence and location of adhesions which are at risk of tearing from the pleural wall following treatment. This perforation and pneumothorax risk analysis associated with predictive modeling may inform the location and sequence of treatment.[0102} The peri-procedure phase 1904 can occur during the treatment procedure, immediately before the procedure (e.g., when preparing for the procedure), and / or immediately after the procedure (e.g., when assessing the immediate outcomes of the procedure). The periprocedure phase 1904 can involve using data produced during the pre-procedure phase 1902 to assist the interventionalist in performing the treatment procedure. For instance, 3D models of the patient anatomy generated during the pre-procedure phase 1902 can be integrated with periprocedural visualization (e.g., fluoroscopy, bronchoscopy) and navigation technology (e.g., robotic navigation and delivery’ systems) to enhance delivery’ and targeting of an endobronchial implant to the appropriate location in the lung.[0103J In some embodiments, the peri-procedure phase 1904 involves modifying and / or augmenting the treatment plan during the procedure by assessing the outcome of a previous step in the plan and providing recommendations for next steps. For instance, real-time data characterizing the patient's response to a previously placed implant can be used to demonstrate procedure success, evaluate whether the procedure should continue as planned, and / or determine whether modifications should be made (e.g., repositioning of the implant, removal of the implant, placement of additional implants, administration of other therapies). The real-time data can include, for example, image data, Al analyses, physician input, pressure and / or flow measurements, physiological metrics (e.g., O2 saturation, breathing metrics), etc. Optionally, the real-time data can be generated by a ventilator or other device that examines the correlation between data such as O2 saturation and air flow, and uses such data as a metric of success. This approach can improve efficacy and reduce procedure time.[01041 The post-procedure phase 1906 can occur after the treatment procedure, such as at least 24 hours, 48 hours, 1 week, 2 week, 3 weeks, 4 weeks, 1 month, 2 months, 3 months, 6 months, or 1 year after the procedure. In some embodiments, the post-procedure phase 1906 involves assessing a treatment outcome of the patient (block 1912) at one or more timepoints after the procedure (e.g., at a single timepoint following treatment, or repeatedly following treatment, such as periodically or intermittently). The process of block 1912, also referred to herein as “outcome assessment,’' can involve analyzing patient data (e.g., CT data and / or other image data, medical records, questionnaires, other diagnostics) to evaluate the patient'sresponse to treatment, such as whether the patient’s condition is improving, stable, or deteriorating over time. For instance, post-procedure lung function metrics can be compared to pre-procedure lung function metrics, such as airway patency, lung / lobe / segment volume in inspiration and expiration, RV, TLC, RV / TLC ratio, flow through targeted airways, and / or flow through adjacent airw ays. Optionally, the state of the patient’s lung can be assessed by determining a set of lung metrics from the patient data (e.g., CT data of the lung), then comparing the set of lung metrics to a set of second lung metrics determined from other types of data, such as image data of the same lung before treatment (e g., a baseline CT image obtained before placement of an endobronchial implant), image data of the same lung at an earlier time point after treatment (e.g., comparison of 6 month follow up and 12 month follow up CT images after placement of an endobronchial implant), image data of the same lung after placement of another endobronchial implant at a different location than the location of the current endobronchial implant, and / or a library of lung image data of patients having COPD. The outcome assessment process can also involve evaluating the state of the endobronchial implant over time, such as whether the implant is still positioned at the target location and functioning as intended, whether the implant has migrated, whether the implant has collapsed or otherwise failed, etc.|0105| In some embodiments, the process of block 1912 can include transforming some or all of the patient data into a “virtual bronchoscopy” with which a user may interact. For example, a 3D model of the lung (including airways and placed implant(s)) can be reconstructed from imaging (e.g., follow up CT imaging) of the lung post-procedure. In some embodiments, for example, developing a virtual bronchoscopy can include obtaining CT slice images of at least a portion of a lung in which an endobronchial implant is placed, reconstructing a 3D model of the lung based on the CT slice images, and segmenting the 3D model to differentiate various structures in the lung including cardiopulmonary structures (e.g., 3D airway structures and / or pulmonary vasculature) and the placed endobronchial implant. Additionally or alternatively, the CT slice images can be segmented to differentiate between structures in the lung prior to 3D model construction. Such segmentation can, for example, be based at least in part one density differences between different cardiopulmonary features and the endobronchial implant itself, as represented as different voxel densities in the CT images.[0106| The 3D model can be displayed on a suitable display (e.g., on a computing device) and / or navigable by a user in a virtual bronchoscopy interaction. For example, the 3D model can be displayed on a monitor and / or on a wearable device (e.g., glasses, headset,goggles, etc.). In some embodiments, the 3D model can additionally or alternatively be displayed in an augmented reality (AR) and / or virtual reality (VR) environment. The 3D model can be navigated with a suitable user interface device (e.g., mouse, joystick, handheld controllers, buttons, scroll wheel or scroll balls, etc.).

[0107] In some embodiments, the display of the 3D model may include a highlighting or other emphasis of one or more implant features. For example, the implant may be visually indicated with an outline of the implant itself (e.g., colored line, or thicker line weight). As another example, one or more individual implant features may additionally or alternatively be visually indicated with markers associated with relevant implant features (e.g., markers corresponding to the proximal and distal ends of an endobronchial implant, outline of cross- sectional profile of an endobronchial implant at one or more locations along the airway in which the endobronchial implant is placed). In some embodiments, the implant outline and / or markers associated with individual implant features may be toggled on and / or off for display, such as to enable clearer visualization of certain features in the 3D model.

[0108] A virtual bronchoscopy can provide more detailed information regarding the lung, airways, and / or implant than what otherwise can be visually observ ed during a non-virtual bronchoscopy, or existing virtual bronchoscopy technologies. For example, in many instances, the placed implant may be configured to blend into the contours of the airways and minimize the induction of foreign body reactions, so the implant may not be easily visible on the tissue surface during a non-virtual bronchoscopy. In contrast, a virtual bronchoscopy in accordance with the present technology that allows navigation of a reconstructed 3D model of the lung, airways, and / or placed implant, as described above, can allow visualization and investigation of the airways, the placed implant, implant-airway tissue interactions, and the lung as a whole, even beyond the airway surface. In particular, visualization of the placed implant relative to its surroundings may be helpful for assessing treatment outcome for the patient post-procedure. In some embodiments, assessments of treatment outcome using the virtual bronchoscopy can be performed manually (e.g., by a user operating and navigating the virtual bronchoscopy), and / or with a software algorithm such as a trained machine learning algorithm (e.g., similar to those described herein).

[0109] In some embodiments, the outcome assessment process of block 1912 involves predicting future outcomes of the patient, such as the predicted disease progression, therapeutic benefits, implant state, etc. For instance, the outcome assessment process can predict whether any post-procedural issues are likely to arise, such as physiological issues (e.g., excessivemucus, granulation tissue, and / or fibrosis) as well as issues with the implant (e.g., collapse, displacement, and / or other failure). If any issues are predicted to arise, the post-procedure phase 1906 can generate recommendations for interventions to prevent, mitigate, or otherwise address such issues (block 1914). The process of block 1914, also referred to herein as “intervention recommendation,'’ can produce recommendations for additional treatment procedures such as cleanup bronchoscopy, implant removal, implant replacement, placement of additional implants, consultations with healthcare professionals, etc.[0110| In some embodiments, patient data is collected during the post-procedure phase1906 using at-home devices, such as take-home spirometers and / or wearable devices (e.g., smart watches with sensors for blood oxygen levels, heart rate, activity (such as steps), altitude, position, and / or sleep; wearable stethoscopes that analyze lung sounds to detect early signs of disease exacerbation)). The data generated from such devices can be used to track the patient over extended time periods (e.g., weeks, months) and can allow for remote monitoring, thus reducing the frequency of doctor visits. Optionally, if the data indicates that there are potential health concerns (e.g., the patient’s condition suddenly deteriorates), the patent can be instructed to see the doctor for follow-up.

[0111] Any of the processes of FIG. 19 (e.g., the patient selection process, procedure planning process, outcome assessment process, and / or intervention recommendation process) can be performed using one or more software algorithms, such as rule-based algorithms, machine learning algorithms, or combinations thereof. Examples of machine learning algorithms that may be used include: regression algorithms (e.g., ordinary' least squares regression, linear regression, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing), instance-based algorithms (e.g., k- nearest neighbor, learning vector quantization, self-organizing map, locally weighted learning), regularization algorithms (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, least-angle regression), decision tree algorithms (e g., Iterative Dichotomiser 3 (ID3), C4.5, C5.0, classification and regression trees, chi-squared automatic interaction detection, decision stump, M5), Bayesian algorithms (e.g., naive Bayes, Gaussian naive Bayes, multinomial naive Bayes, averaged one-dependence estimators, Bayesian belief networks, Bayesian networks, hidden Markov models, conditional random fields), clustering algorithms (e.g., k-means, single-linkage clustering, k-medians, expectation maximization, hierarchical clustering, fuzzy clustering, density-based spatial clustering of applications with noise (DBSCAN), ordering points to identify cluster structure (OPTICS), non negative matrixfactorization (NMF), latent Dirichlet allocation (LDA), Gaussian mixture model (GMM)), association rule learning algorithms (e.g., apriori algorithm, equivalent class transformation (Eclat) algorithm, frequent pattern (FP) growth), artificial neural network algorithms (e.g., perceptrons, neural networks, back-propagation, Hopfield networks, autoencoders, Boltzmann machines, restricted Boltzmann machines, spiking neural nets, radial basis function networks), deep learning algorithms (e.g., deep Boltzmann machines, deep belief networks, convolutional neural networks, stacked auto-encoders), dimensionality reduction algorithms (e.g., principle component analysis (PCA), independent component analysis (ICA), principle component regression (PCR), partial least squares regression (PLSR), Sammon mapping, multidimensional scaling, projection pursuit, linear discriminant analysis, mixture discriminant analysis, quadratic discriminant analysis, flexible discriminant analysis), ensemble algorithms (e.g., boosting, bootstrapped aggregation, AdaBoost, blending, gradient boosting machines, gradient boosted regression trees, random forest), or suitable combinations thereof. The machine learning algorithms described herein can be trained using any suitable technique, including supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, or suitable combinations thereof.[0112| In some embodiments, the software algorithms described herein include at least one computer vision algorithm, which may or may not be a machine learning algorithm (e.g., a deep learning algorithm such as a convolutional neural network). The computer vision algorithm can receive image data as input, and can generate output data characterizing one or more objects present in the image data. For instance, the computer vision algorithm can receive CT data of one or both of the patient’s lungs, and can identify objects in the lung such as anatomical structures, healthy tissues, diseased tissues, implanted devices (e.g., endobronchial implants), etc.

[0113] FIG. 20 is a flow diagram illustrating a method 2000 for planning a treatment for a patient, in accordance with embodiments of the present technology. The method 2000 can be performed as part of the pre-procedure phase 1902 of the workflow 1900 of FIG. 19. The method 2000 can involve receiving patient data, such as questionnaire information, medical record information, image data, pulmonary function test (PFT) data, and / or data from other diagnostic technologies. The method 2000 can implement one or more software algorithms that use the patient data to determine whether patients are candidates for endobronchial implant therapy (and / or other therapies for pulmonary diseases) and. optionally, assist with planning such therapies.[0114| The questionnaire information can include the patient’s responses to one or more questionnaires, which may be administered by a patient engagement utility as described above. The medical record information can include information from electronic health records for the patient, such as the patient’s name, date of birth, demographic information, height, weight, medical history, familial medication history', symptoms, comorbidities, diagnoses, medications, test results, previous treatments and outcomes, and so on. The questionnaire information and / or medical record information can provide any of the following information: whether the patient has a cough, whether the patient is a smoker, whether the patient finds it hard to breathe, whether the patient is able to take deep breaths, whether the patient is able to perform typical activities (e.g., walking, showering), whether the patient has exacerbations, whether the patient has significant mucus, whether patient has had lung infections, and / or the patient’s cunent and / or past drug regimen.

[0115] The image data can include data generated by any suitable imaging modality, such as CT, X-ray (e g., chest radiography, fluoroscopy), MRI (e.g.,3He MRI,129Xe MRI), SPECT, bronchoscopy, ultrasound, ventilation-perfusion scan data, photographs (e.g., from multiple external cameras to map the surface topography of the chest), etc. For example, the CT data can include inspiratory CT data (e.g., obtained at the end of full inspiration) and / or expiratory CT data (e.g., obtained at the end of forced expiration). In some embodiments, the image data can be generated after the administration of a contrast agent in the patient (e.g., via intrapleural injection, inhalation, etc.), where the contrast agent can help provide contrast enhancement in CT imaging. For example, for pulmonary CT, suitable contrast agents include iodinated compounds (e.g., derivatives of diatrizoic acid), barium, radiolabeled albumin to track blood flow in the lungs, and labeled gases (e.g., Xenon-133) for tracking ventilation in the lungs. The CT data can include a series of 2D cross-sectional images of the patient’s anatomy, with each image having a specified slice thickness and spaced apart by a specific slice interval (e.g.. 10 mm intervals). For example, the 2D cross-sectional images can have a slice thickness of at least 3mm (e g., within a range from 3 mm to 10 mm), or can have a slice thickness of less than 3 mm, such as within a range from 1 mm to 2 mm (e.g., 1 mm). In some embodiments, the 2D cross-sectional images can be combined to generate a 3D volumetric CT model of the imaged lung tissue.[O1I6| The PFT data can include any data characterizing the function of the patient’s lungs, such as spirometry' data, plethysmography’ data, exercise test results (e.g., 6 minute walk distance test), etc. The PFT data can include measurements of any of the following: tidalvolume, minute volume, VC, functional residual capacity, residual volume (RV), total lung capacity (TLC), RV / TLC ratio, forced vital capacity (FVC), forced expiratory volume (FEV) (e.g., FEV in 1 second (FEVi)), forced expiratory flow, peak expiratory flow, closing volume (CV), inspiratory capacity (IC), IC / TLC ratio, and / or diffusion capacity for carbon monoxide..

[0117] The other data from other diagnostic technologies can include data from one or more sensors configured to monitor the patient’s state, which can include implanted sensors, non-invasive sensors, wearable sensors, or suitable combinations thereof. Examples of sensors suitable for use with the present technology include, but are not limited to, impedance sensors, pressure sensors, flow sensors, and wearable stethoscopes. As another example, bronchoscopy can be used for automated collection of information at specific locations in the lungs, such as information regarding flow, pressure, granulation tissue, fibrosis, mucus, epithelialization, and / or obstruction.

[0118] Any of the patient data types described herein can be obtained over a plurality of time points, such as two. three, four, five. 10. 20, 50, or more time points. For instance, patient data can be obtained over a plurality of time points spanning multiple seconds, minutes, hours, days, weeks, months, and / or years. In some embodiments, patient data is obtained at two or more of the following time points: before treatment (e.g., before placement of an endobronchial implant, before administration of a bronchodilator), after treatment (e.g.. after placement of an endobronchial implant, after administration of a bronchodilator), before exercise, during exercise, after exercise, and / or during different phases of the respiratory cycle (e.g., inspiration, expiration). In other embodiments, however, some or all of the patient data can be obtained at a single time point.[01191 As shown in FIG. 20, the patient data can be provided to a first software algorithm. The first software algorithm can be a first machine learning algorithm (also referred to herein as a patient characterization machine learning algorithm) that has been trained (e.g., via supervised learning) to synthesize the patient data to compute metrics that characterize lung properties and / or the patient’s disease state. In some embodiments, the first software algorithm can additionally or alternatively include other suitable automated processes. The output of the first software algorithm can be a set of lung metrics representing a state of one or both lungs of the patient. For example, the lung metrics can characterize any of the following lung parameters: FEV (e.g., FEVi), FVC, VC, IC, IC / TLC ratio, functional residual capacity, TLC, diffusion capacity' for carbon monoxide, RV, RV / TLC ratio, CV, lobar and / or segmental tissue destruction, lobar and / or segmental air trapping, lobar and / or segmental fissure status, extentof lobar and / or segmental fissure completion, lobar and / or segmental ventilation, lung function (e.g., regional assessment), disease phenotype (e.g., homogeneity / heterogeneity of lobar and / or segmental emphysema, type of emphysema (such as centriacinar emphysema, panacinar emphysema, or paraseptal emphysema), locations of diseased portions of the lung), lobar volume, segmental volume, segment locations, diaphragm shape, tissue density, opacity', proximity’ of diseased portions to anatomical structures (e.g., pleura, heart, nodules), proximity of diseased portions to other medical devices, lumen inner diameter of bronchial sections, air flow mapping, collapsed airways, airway pressure, one or more mechanical properties of an airway (e.g., airway compliance, airway resistance, airway elastance, etc.), pleural pressure, airway diameter to wall thickness, airway wall deformation, vascular perfusion, parenchyma density, bullae, information that localizes the disease in the lung (e.g., at the segmental and / or subsegmental levels), obstruction score mucus score, and / or degree of epithelialization. At least some of these (e.g., airway resistance, airway compliance, airway elastance, etc.) may be measured at any of various levels of the lung, such as a segmental level of the lung, other regional level of the lung, lobar level of the lung, and / or a lung level of the lung.[0120 In some embodiments, the first software algorithm can be configured to correct for breathing effort-related noise, such as in expiratory CT scans. The expiratory or RV scans are ideally obtained at maximal expiration by the patient; however, patients are often unable (e.g., cannot exert enough breathing effort) to completely exhale when an expiratory CT scan is perfonned. For example, the extent of expiration may depend at least in part on body posture of the patient. For example, a patient may be able to exhale more fully while sitting down (e.g., during a typical pulmonary function test), but exhale less while tying down (e.g., during a ty pical expiratory CT scan). Incomplete expiration may lead to some uncertainty in the interpretation of the expiratory' CT scans, as w ell as an inconsistency between (i) quantitative CT (QCT)-derived reduction in lung volume at maximal expiration and (ii) “actual” reduction in lung volume at maximal expiration based on. for example, pulmonary function test results. This inconsistency can be characterized as noise in the patient data caused by variability in breathing effort.

[0121] Accordingly, in some embodiments, the first software algorithm can be configured to correct for such variability' in breathing effort. For example, as shown in FIG. 29 A, a method 2900 for evaluating a patient can include receiving patient data including CT data of a lung of the patient 2910, generating a set of initial lung metrics for the patient based on the patient data 2920 by inputting the patient data into a patient characterization machinelearning algorithm, and generating a set of corrected lung metrics corrected for variability' in breathing effort 2930, by applying one or more correction factors generated by a correction machine learning algorithm. In some embodiments, the method can further include generating corrected expiratory CT data (e.g., corrected expiratory- CT scan) 2940, by inputting the set of one or more correct lung metrics into a transformation machine learning algorithm. The corrected expiratory CT scan can, for example, be a reconstructed expiratory CT scan that is adjusted for breathing effort-related noise (e.g., is consistent with an expiratory CT scan that embodies lung metrics achieved with a pulmonary function test, without the patient performing such a pulmonary function test).(01221 FIG. 29B depicts a floyvchart illustrating an example of information flow in the method 2900. At block 2910, CT data (e.g., expiratory CT scan) and other patient data (e.g., PFT data) can be received, and at block 2920 the CT data can be input into a patient characterization machine learning algorithm to generate initial lung metrics including total lung capacity determined from the CT data (TLCCT) and residual volume determined from the CT data (RVCT). Correction factors for total lung capacity and residual volume (CFTLC and CFRV, respectively) are applied to TLCcr and RVCT correction machine learning algorithm at block 2930 to generate corrected lung metrics including corrected total lung capacity (TLCcorr) and corrected residual volume (RVcon-). In some embodiments, TLCcorr and RVco can subsequently be input into a transformation machine learning algorithm at block 2940, to generate corrected expiratory' CT data (CTcorr) such as a corrected expiratory CT scan, that is corrected for breathing effort-related noise.[0123 FIG. 29C depicts a flowchart illustrating an example process for training a correction machine learning algorithm (e.g.. used in method 2900). In some embodiments, the correction machine learning algorithm can be trained using training data from a plurality of training patients, each of yvhich undergoes a pulmonary' function test (PFT) and an expiratory CT scan. For each training patient, reference lung metrics of total lung capacity (TLCactuai) and residual volume (RVactuai) can be derived from the PFT, and initial CT-derived lung metrics of total lung capacity (TLCCT) and residual volume (RVCT) can be derived from the expiratory CT scan using a patient characterization machine learning algorithm (e.g., first machine learning algorithm, as described elsewhere herein). The reference lung metrics and initial CT- derived lung metrics can serve as training data for a correction machine learning algorithm, which can be configured to generate correction factors that represent a conversion of the initial CT-derived lung metrics to the reference lung metrics. For example, the correction machinelearning algorithm can be configured to generate correction factors for total lung capacity and residual volume (CTTLC and CFRV. respectively). Similar approaches can be developed to identify correction factors for other relevant lung metrics including lobar volume, segmental volume, disease metrics (e.g., emphysema destruction or air trapping measures), and ventilation / perfusion metrics.

[0124] The correction machine learning algorithm can be any suitable kind of machine learning algorithm, such as supervised, unsupervised, and / or reinforcement machine learning algorithms. Furthermore, in some embodiments the correction factors generated by the correction machine learning algorithm can be validated and / or undergo iteration by generally comparing a subject’s corrected lung metrics (that is, obtained with the correction machine learning algorithm’s correction factors applied to the subject’s expiratory7CT data) against a control. Specifically, for example, a subject can undergo anon-spirometry gated expiratory CT scan, and correction factors generated by the correction machine learning algorithm can be applied to generate corrected lung metrics (e g., TLCcorr and RVcorr). These corrected lung metrics can then be compared to lung metrics obtained through well-controlled, standard inspiratory and expiratory spirometry CT. Sufficient similarity between (i) the corrected lung metrics and (ii) the lung metrics obtained through spirometry-gated CT can indicate validation of the correction factors and / or the correction machine learning algorithm itself. However, dissimilarity between the corrected lung metrics and the lung metrics obtained through spirometry -gated CT can indicate that the correction factors and / or correction machine learning algorithm should be revised further (e.g., in a continual learning process).

[0125] Although the method 2900 described above refers to discrete correction factors, in some embodiments the method 2900 can include directly inputting initial CT-derived lung metrics into a correction machine learning algorithm, which can then output corrected lung metrics (e.g., without relying upon discrete correction factors).|0126| The lung metrics can characterize any of the above lung parameters at a single time point, and / or can characterize a change in any of the above lung parameters over a plurality of time points (e.g.. before endobronchial implant therapy, after endobronchial implant therapy, before administration of a bronchodilator, after administration of a bronchodilator, before exercise, or during exercise). The lung metrics can correlate to whether the patient has a pulmonary disease such as COPD. Optionally, the lung metrics can be used to generate a risk assessment for the pulmonary disease (e.g., a percent likelihood that the patient has COPD).[0.127| Additionally or alternatively, the lung metrics can be used to generate a disease"score'’ characterizing severity of pulmonary disease and / or representing a predictor of patient response to a treatment of the pulmonary' disease. For example, the disease score can represent degree of emphysema destruction, extent of hyperinflation (e.g., static hyperinflation), and / or extent of air trapping in one or more regions of the lung. In some embodiments, the lung metrics can include one or more disease scores, where each disease score corresponds to a respective region of the lung (e.g., a particular lung, a particular lobe, a particular segment, a particular sub-segment). In some embodiments, the lung metrics can include a single disease score at least partially based on multiple “local” disease scores each corresponding to a respective region of the lung (e.g., a particular lung, a particular lobe, a particular segment, a particular sub-segment). For example, a single disease score can be an average value of the multiple local disease scores, or be based on any suitable calculation incorporating the multiple local disease scores. FIG. 27 is a flow diagram illustrating a method 2700 for evaluating a patient using such a disease score lung metric, including receiving patient data including CT data of a lung of the patient (block 2710), and generating a pulmonary disease score for a region of interest of the lung by inputting the patient data into a machine learning algorithm (block 2720).[0128| As another example, in some embodiments, the disease score can be configured to characterize severity7of dynamic hyperinflation in a lung of the patient. Dynamic hyperinflation, as distinct from static hyperinflation, refers to the condition that can occur when the patient inhales more than they are able to exhale during exercise (as opposed to static hyperinflation that occurs when the patient is at rest). Typically, dynamic hyperinflation has a more severe effect of hyperinflation than static hyperinflation and can be valuable to assess when characterizing a patient’s lung condition. Conventional methods of measuring dynamic hyperinflation in a patient involve having the patient perform a cardiopulmonary' exercise test (e.g., perform a predetermined exercise regimen such a series of defined cycling exercises on a stationary bike) and then obtaining inspiratory and expiratory CT scans of the patient after such exercises. However, these additional processes require extra equipment and additional time and / or analysis in order to assess dynamic hyperinflation, which may not always be available or convenient.[O.l29| In accordance with the present technology, to address such challenges in characterizing dynamic hyperinflation in a patient, CT data can be analyzed by a patient characterization machine learning algorithm or other first machine learning algorithm. For example, as shown in FIG. 30, a method 3000 of evaluating a patient can include receivingpatient data including CT data of a lung of the patient 3010 (similar to that described elsewhere herein), and generating at least one disease score for the patient by inputting the patient data (e.g., CT data) into a patient characterization machine learning algorithm 3020, where the disease score characterizes severity of dynamic hyperinflation in the lung of the patient. The patient characterization machine learning algorithm can be trained with training data associated with a plurality of training patients, where the training data includes CT images (e.g., inspiratory CT scans and / or expiratory CT scans when the patient is at rest) and cardiopulmonary exercise testing results for the training patients. For example, the patient characterization machine learning algorithm can be trained to correlate CT images and cardiopulmonary exercise testing results for the training patients, Once trained, the patient characterization machine learning algorithm can be used to output a characterization of dynamic hyperinflation (e.g., with a disease score) based on an input of inspiratory CT scan(s) taken when the patient is at rest (and / or based on an input of an expiratory' CT scan taken when the patient is at rest). In some embodiments, the patient characterization machine learning algorithm is configured to provide a characterization of dynamic hyperinflation in a lung of a patient, without the patient performing a cardiopulmonary' exercise test.[0130| In some embodiments, as described above, lung metrics produced by the first software algorithm can include disease phenotype. For example, the distribution of disease scores can be used to characterize homogeneity7or heterogeneity7of emphysema. In some embodiments, for example, a difference of disease score in ipsilateral lung regions (e.g., in different lobes, segments, and / or sub-segments in a particular lung) below a threshold difference value can correspond to a characterization of homogenous emphysema for that lung, while a difference of disease score in ipsilateral lung regions (e.g., in different lobes, segments, and / or sub-segments in a particular lung) above a threshold difference value can correspond to a characterization of heterogenous emphysema for that lung. In some embodiments, the threshold difference value used to characterize emphysema homogeneity / heterogeneity can range from about 10% to about 25%, from about 10% to about 20%, or from about 10% to about 15%. Additionally or alternatively, in some embodiments, the distribution of disease scores across a lung or lung region (e.g., across different lobes and / or different segments and / or different sub-segments) can be used to determine emphysema type. In particular, such lung metrics regarding emphysema tissue destruction at the segmental level and / or sub-segmental level may advantageously provide more insight into the disease and lung anatomy of a patient on a more granular level than analysis solely performed at the lobar level.[0.1311 In some embodiments, lung metrics produced by the first software algorithm can additionally or alternatively include an identification of at least one type of pulmonary disease causing air trapping in the lung of the patient. For example, it may be advantageous to distinguish between air trapping caused by different pulmonary diseases, as the desired treatment (if any) to address air trapping may depend at least in part on the ty pe of disease. However, conventional techniques, such as those involving CT scans, are limited in their ability to distinguish between different causes of air trapping in a lung.[0132| In accordance with the present technology, to address such limitations in evaluating causes of air trapping in a lung, CT data can be analyzed by a patient characterization machine learning algorithm or other first machine learning algorithm. For example, as shown in FIG. 31, a method 3100 for evaluating a patient can include receiving patient data including CT data of a lung of the patient 3110 (similar to that described elsewhere herein) and generating an identification of at least one type of pulmonary disease that is a cause of air trapping and / or obstruction in the lung of the patient, by inputting the patient data into a patient characterization machine learning algorithm 3120. For example, in some variations the patient characterization machine algorithm can be configured to identify that air trapping in a lung caused by emphysema as opposed to another air trapping-causing disease or condition, such as small airway disease. In some variations, the patient characterization machine algorithm can be configured to identify that one or more obstructions in a lung is caused by emphysema, small airway disease, asthma, chronic bronchitis, and / or pulmonary fibrosis. In some variations, the patient characterization machine learning algorithm can additionally or alternatively be configured to generate one or more additional lung metrics including a disease score (e.g., as described elsewhere herein). For example, in some variations the disease score can characterize severity of a particular ty pe of pulmonary7disease in the patient. In some variations, the patient characterization machine learning algorithm can be configured to generate multiple disease scores for the lung. For example, one disease score can quantify an amount of air trapping or obstruct on (e.g., percent of a given amount of air trapping or obstruction) in the lung of a patient that is caused by a first pulmonary disease, and a second disease score can quantify an amount of air trapping (e.g., percent of a given amount of air trapping or obstruction) in the lung of the patient that is caused by a second pulmonary disease. The patient characterization machine learning algorithm can be trained (e.g., in a supervised learning process) with training data associated with a plurality of training patients, where the training data includes CT images (e.g., inspiratory CT scans and / or expiratory7CT scans) oftraining patients identified as having any one or more pulmonary disease types, such as emphysema, small airways disease, chronic bronchitis, and / or pulmonary fibrosis. In some embodiments, the training patients can be identified as either having a pulmonary disease that is emphysema, or only pulmonary diseases that are not emphysema. In some embodiments, training patients whose CT scans are labeled as exhibiting emphysema can be identified as those patients who were treated with an endobronchial implant placed in a target region of the lung and specifically designed for treatment of emphysema, but who have successful treatment outcomes (e.g., sustained airway patency). In contrast, training patients whose CT scans are labeled as exhibiting one or more other pulmonary diseases can be identified as those patients who were treated with an endobronchial implant placed in a target region of the lung and specifically designed for treatment of emphysema, but who do not have successful treatment outcomes. Once trained, the patient characterization machine learning algorithm can be used to output an identification of at least one type of pulmonary disease causing air trapping and / or obstruction in a lung of the patient, based on an input of a CT scan of the lung of the patient.[0133| Additionally or alternatively, in some embodiments as described above, lung metrics produced by the first software algorithm can include characterization of lobar and / or segmental fissure status and / or completion, and / or lobar and / or segmental collateral ventilation. In particular, such lung metrics regarding fissure status, fissure completion, and / or ventilation at the segmental level may advantageously provide more insight into the disease and lung anatomy of a patient on a more granular level than analysis solely performed at the lobar level.[0134| In some embodiments, lung metrics produced by the first software algorithm can be based at least in part on patterns of voxel density from one or more 3D models of patient tissue generated from inspiratory and / or expiratory CT scans. In general, voxel density is proportional to the attenuation of an X-ray beam through the tissue, which generally corresponds to the physical density of the tissue and other substances (e.g., air) being imaged. Voxel density can be represented in Hounsfield units (HU), which indicate along the Hounsfield scale the amount of X-ray attenuation that occurs in tissue corresponding to a particular voxel. In some embodiments, the first software algorithm can include evaluating voxel density across one or more lung regions relative to one or more voxel density7thresholds, to characterize emphysema occurring on a lobar and / or segmental level, air trapping on a lobar and / or segmental level, the lobar and / or segmental lung volumes, amount of perfusion on alobar and / or segmental level, and / or airway dimensions (e.g., lumen inner diameter of segmental airways along the length of airway pathways, airway wall thickness, etc.).

[0135] The first software algorithm can incorporate different voxel density thresholds associated with different types of CT scans and / or different lung metrics. In some embodiments, the first software algorithm can output lung metrics based on analysis of both inspiratory CT scans and expiratory CT scans. In some embodiments, the first software algorithm can output lung metrics based on an expiratory CT scan, but not an inspiratory CT scan. In some embodiments, the first software algorithm can output lung metrics based on an inspiratory CT scan, but not an expiratory CT scan. Additionally or alternatively, the first software algorithm can include other suitable quantitative CT (QCT) techniques, such as those described in further detail herein.|0136] For example, as shown in FIG. 23 A, the first software algorithm can incorporate one or more voxel density thresholds associated with an inspiratory CT scan. In some embodiments in which CT images from an inspiratory CT scan have a slice thickness of about 3mm or greater (e.g., between 3 mm and 10 mm), emphysema can be quantified or otherwise characterized by assessing lung voxels on an inspiratory CT scan having attenuation below - 910 HU (e.g., percent of the lung voxels below this threshold, integral of density values of all voxels below this threshold, and / or other density-based calculations). The quantification of emphysema can characterize an emphysematous disease state at a lobar level (generation 2), and / or at a segmental level (generation 3), and / or at a sub-segmental level (generation 4+). Additionally or alternatively, this -910 HU threshold can be used to characterize lobar lung volume and / or segmental lung volume. As described in further detail below, such lung metrics can be analyzed by additional software algorithm(s) to predict optimized treatment for particular lobe(s) and / or segment(s) of the lung.

[0137] As further shown in FIG. 23B, in some embodiments in which CT images from an inspiratory CT scan have a slice thickness of less than 3 mm (e.g., 1 mm), emphysema can be quantified or otherwise characterized by assessing lung voxels on an inspiratory' CT scan having attenuation below -950 HU (e.g., percent of the lung voxels below this threshold, integral of density values of all voxels below this threshold, and / or other density-based calculations). The quantification of emphysema can characterize an emphysematous disease state at a lobar level and / or at a segmental level. Additionally or alternatively, this -950 HU threshold can be used to characterize lobar lung volume and / or segmental lung volume, and / or lack of perfusion at a lobar level and / or at a segmental level.[0.138| As another example, as shown in FIG. 23B, the first software algorithm can incorporate one or more voxel density thresholds associated with an expiratory CT scan. In some embodiments, air trapping can be quantified or otherwise characterized by assessing lung voxels on an expiratory CT scan having attenuation below -856 HU (e.g., percent of the lung voxels below this threshold, integral of density value of all voxels below this threshold, and / or other density-based calculations). Additionally or alternatively, in some embodiments the first software algorithm can include generating lung metrics characterizing one or more lung anatomical features (e.g., measurements of airways within the lung) from image data from an expiratory CT scan. For example, the first software algorithm can generate lung metrics from an expiratory' CT scan including geometrical, physical, and / or mechanical properties of the airways, such as airway compliance, pleural pressure, airway diameter to wall thickness, and / or airway wall deformation. Such properties can be measured particularly at the segmental level, which (compared to such information determined solely at the lobar level) can provide more insight into the patient condition for use in determining patient candidacy for treatment, treatment planning, etc. Additionally or alternatively, the first software algorithm can generate lung metncs from an expiratory CT scan including characterization of lobar volume and / or segmental lung volume, and / or identification of collapsed airways (and their severity7of collapse, such as airway diameter) that may be contributing to the disease state (e.g., hyperinflation and impairment of breathing).

[0139] As another example, voxel density thresholds for an inspiratory7CT scan and / or expiratory CT scan can be analyzed by the first software algorithm to characterize a shape of an airway of the lung. In some embodiments, CT scans can be analyzed to characterize diameter, length, radius of curvature, tortuosity, and / or any' suitable shape characteristic of an airway in the lung. For example, as shown in FIG. 32, a method 3200 of evaluating a patient can include receiving patient data including CT data of a lung of the patient 3210 (e.g., similar to that described elsewhere herein), and generating a set of lung metrics by inputting the patient data into a patient characterization machine learning algorithm 3220, where the set of lung metrics characterizes a shape of an airway of the lung at one or more timepoints within a respiratory cycle. In some embodiments, the first software algorithm can incorporate one or more voxel density thresholds to characterize a shape of an airway of the lung at one or more timepoints within a respiratory cycle, such as maximal inspiration, maximal expiration, tidal volume inspiration, and / or tidal volume expiration, w here the boundaries of the airw ay in CT data can be predicted based on those lung voxels having attenuation below7a suitable threshold,such as about 700HU. Accordingly, in some embodiments, once the patient characterization machine learning algorithm is trained, the method 3200 can be performed to characterize airway shape for a patient without the patient performing a separate pulmonary function test.|0140] In some embodiments, the patient characterization machine learning algorithm can be trained with training data associated with a plurality of training patients, where the training data includes CT images (e.g., inspiratory CT scans and / or expiratory7CT scans) and pulmonary function tests (e.g., spirometry7data) for the plurality7of training patients, across different timepoints during a respiratory7cycle. As such, the patient characterization machine learning algorithm can be trained to additionally or alternatively predict the relative change in lung volume at various timepoints across a respiratory cycle. For example, in some embodiments the patient characterization machine learning algorithm can be configured to estimate a relative change in lung volume between maximal inspiration and tidal volume inspiration. As another example, in some embodiments the patient characterization machine learning algorithm can be configured to estimate a relative change in lung volume between maximal expiration and tidal volume expiration.[0141 [ It should be understood that the above-described density7threshold values for analyzing CT scans are only examples, and that other suitable threshold values may additionally or alternatively be used to evaluate voxel density7data and generate suitable lung metrics. For example, a density threshold value for an inspiratory CT scan can be a value ranging from -910 HU to -990 HU, or ranging from -950 HU to -990 HU, or ranging from - 970 HU to -990 HU (e.g., -950 HU, -960 HU, -970 HU, -980 HU, or -990 HU).

[0142] The lung metrics produced by the first software algorithm can be provided to a second software algorithm. The second software algorithm can be a second machine learning algorithm that has been trained (e.g., via supervised learning) to analyze the lung metrics to determine whether the patient is a candidate for one or more treatments for the pulmonary disease. In some embodiments, the second software algorithm can additionally or alternatively include other suitable automated processes. For instance, the output of the second software algorithm can be the likelihood of success if the patient receives the treatment. The treatment can be an airway treatment for COPD, such as a pharmacological treatment (e.g., bronchodilators), an interventional treatment (e g., vapor therapy, administration of a sealant, transbronchial fenestration, placement of an endobronchial coil, placement of an endobronchial valve, or placement of a minimal endobronchial reinforcement implant), or a combination thereof.[01431 In some embodiments, the output of the second software algorithm is a predicted response of the patient to the treatment. The predicted response can include a prediction of any of the following: FEV (e.g., FEVi), FVC, VC, IC, IC / TLC, functional residual capacity, TLC, diffusion capacity for carbon monoxide, RV / TLC, RV, segmental volume, rnMRC score, SGRQ score (or the score for a subset of SGRQ questions), CAT score (or the score for a subset of CAT questions), 6-minute walk test results, cycle ergometry results, cardiopulmonary exercise testing (CPET) results, patient health metrics (e.g., heart rate, blood pressure, body mass index), patient exercise metrics (e g., number of steps taken), patient visit metrics, quality of life metrics (e.g., ability7to breathe), number of required implant removals, time to reintervention, durability of treatment, comorbidities, drug regimen, length of hospitalization, healthcare utilization, and / or cost: and / or a change (e.g.. increase or decrease) in any of the above. Optionally, the predicted response can include a comparison of the response to endobronchial implant therapy with a minimal endobronchial reinforcement implant versus other treatment procedures, such as pharmacological treatments, interventional treatments (e.g., valves, coils, steam, hydrogel glue, thennal ablation, non-thermal ablation), surgical treatment, etc.[01441 If the patient is predicted to have a favorable response to the treatment (e.g., to endobronchial implant therapy with a minimal endobronchial reinforcement implant), the method 2000 can include using a third software algorithm to generate a treatment plan for the patient. The third software algorithm can be a third machine learning algorithm (also referred to herein as a treatment planning machine learning algorithm) that has been trained (e.g., via supervised learning) to analyze the lung metrics and / or predicted response to predict an optimized placement of one or more endobronchial implants to treat the patient’s pulmonary disease. In some embodiments, the third software algorithm can additionally or alternatively include other suitable automated processes. The output of the third software algorithm can be a treatment plan. In embodiments where the treatment includes endobronchial implant therapy, the treatment plan can include any of the following information: implant placement location, number of implants, implant size, implant ty pe, implant geometry7, pathway to a target location for implant placement, and / or localized treatment solutions. For example, implant placement location may be based at least in part on location of dynamic airway collapse as observed in or otherwise determined from expiratory CT data, severity of pulmonary disease in the peripheral lung, the location of the pleural wall, and / or the location of lobar, segmental, and / or sub- segmental airways.[0.145| For example, in some embodiments, the third software algorithm can assist in the selection of one or more lobes and / or one or more airway segments and / or sub-segments for implant placement locations based on lung metrics (e.g., lung metrics provided by the first software algorithm). For example, in identifying suitable implant placement locations, the third software algorithm can target lobes with higher amounts of emphysema destruction (e.g., higher disease score) and with larger lobar volume. Additionally or alternatively, the third software algorithm can target airway segments with higher amounts of emphysema destruction (e.g., higher disease score) and with larger segment volume.[0.146| In some embodiments, the third software algorithm can additionally or alternatively predict a target location in the lung of a patient that exhibits optimum ratio of ventilation and perfusion (e.g., via an analysis of a ventilation / perfusion (V / Q) profile across the lung), such as for maximizing efficacy of an endobronchial implant for treating a pulmonary disease of the patient. For example, as shown in FIG. 33, a method 3300 of planning a treatment for a patient includes receiving patient data including CT data of a lung of the patient 3310 (e.g., similar to that described elsewhere herein), determining a ventilation / perfusion profile of the lung based on the patient data 3320, and identifying a target location for placing an endobronchial implant in the lung of the patient 3330, by inputting the ventilation / perfusion profile into a treatment planning machine learning algorithm. In some embodiments, the treatment planning machine learning algorithm can be configured to additionally or alternatively predict a target location in the lung of a patient that exhibits optimum ventilation and / or optimum perfusion for placement of a particular implant of interest, such as an endobronchial implant (e.g., a minimal endobronchial reinforcement implant as described herein).[01471 In some embodiments, the treatment planning machine learning algorithm can be trained with training data associated with a plurality of training patients having gone through treatment for a pulmonary disease involving placement of an endobronchial implant in the patient. The training data can include CT images (e g., inspiratory CT scans and / or expiratory CT scans), V / Q data for implant location, blood oxygenation data, and / or outcomes of treatment with placement of an implant for the plurality of training patients. In some embodiments, the CT scan(s), V / Q data, and / or blood oxygenation data for patients having a successful treatment outcome with an implant in their lung (e.g., sustained airway patency) are labeled as having a V / Q profile (and / or ventilation, and / or perfusion) that is advantageous for efficacy of the implant. Conversely, the CT scan(s), V / Q data, and / or blood oxygenation datafor patients having a poor treatment outcome with an implant in their lung (e.g., sustained airway patency) are labeled as having a V / Q profile (and / or ventilation, and / or perfusion) that is disadvantageous for efficacy of the implant. Accordingly, the treatment planning machine learning algorithm can be configured to correlate optimum V / Q profile, ventilation, and / or perfusion with target implant locations that encourage high or maximum efficacy of an implant in a particular patient (and / or non-ideal V / Q profile, ventilation, and / or perfusion with target implant locations that discourage high or maximum efficacy of an implant in a particular location). Once trained, the treatment planning machine learning algorithm can be used to output an identification of at least one target location for an implant (e.g., endobronchial implant) in a lung of the patient, based on an input of one or more CT scans and / or V / Q profile of the lung of the patient. For example, in some embodiments, the treatment planning machine learning algorithm may be configured to output an identification of at least one target location for an implant (e.g., endobronchial implant) in a lung of the patient that correlates with a target region of the lung having a higher level of perfusion relative to other, non-target region(s) of the lung (e.g., a threshold level of perfusion, such as a threshold quantified perfusion level in a region or a threshold difference in perfusion between a target region of the lung and one or more non-target region(s) of the lung). Without being bound by theory, it is believed that targeting lung regions or patients exhibiting higher levels of perfusion will lead to better treatment outcomes with treatment with placement of an endobronchial implant as reflected in partial pressure of oxygen metrics (PaCh).[0148) In some embodiments, the third software algorithm can additionally or alternatively predict a pathw ay in the lung of the patient towards a target location for placing an implant (e.g., endobronchial implant) at the target location. For example, the third software algorithm can be a treatment planning algorithm configured to identify a pathw ay from segmental bronchi in the lung to a pleural surface of the lung. For example, as shown in FIG. 34. a method 3400 for planning a treatment of a patient includes receiving patient data including CT data of a lung of the patient 3410 (e.g., as described elsewhere herein), generating a set of lung metrics by inputting the patient data into a patient characterization machine learning machine algorithm 3420 (e.g., the first machine learning algorithm, as described elsewhere herein), and / or mapping (e.g., identifying) a potential pathway and / or identifying a target airway segment in the lung of the patient for placing an implant at a target location in the lung 3430, by inputting at least a portion of the patient data and / or at least a portion of the set of lung metrics into a treatment planning machine algorithm. In some embodiments, thegeneration of lung metrics (at 3420) may be omitted, and the method may include mapping a potential pathway and / or identifying a target airway segment by inputting solely the patient data (e.g., CT data) into a treatment planning algorithm. In some embodiments, the treatment planning machine algorithm can, for example, be configured to maximize access to the implant to a region of the lung having trapped air, maximize access of the implant to emphysematous tissue at a periphery of the lung, minimize tortuosity of the mapped pathway, and / or minimize risk of damage to blood vessels in the lung dunng delivery and placement of the implant.[0149[ In some embodiments, the treatment planning machine learning algorithm can be trained with training data associated with a plurality of training patients having gone through treatment for a pulmonary' disease involving placement of an endobronchial implant in the patient. The training data can include CT images (e.g., inspiratory' CT scans and / or expiratory CT scans), information regarding pathway in the lungs for delivering or placing the implant in the patient, and / or outcomes of treatment with placement of an implant for the plurality’ of training patients. In some embodiments, the CT scan(s) and their pathways for patients having a successful treatment outcome with an implant in their lung (e.g., sustained airway patency, low pathway tortuosity, low damage to blood vessels) are labeled as having a pathway to a target implant location that is advantageous for successful treatment outcome. Conversely, the CT scan(s) and their pathways for patients having a poor treatment outcome with an implant in their lung are labeled as having a pathway to a target implant location that is disadvantageous for successful treatment outcome. Once trained, the treatment planning machine learning algorithm can be used to identify or otherwise map at least one pathway in the lung of a patient toward (or including) a target location for an implant (e.g., endobronchial implant), based on an input of one or more CT scans and / or other patient data.[01501 In some embodiments, the treatment planning machine learning algorithm can additionally or alternatively predict a pathway and / or identify a target airway segment for placement of an implant (e g., endobronchial implant) at a target location, utilizing one or more disease scores. For example, in some embodiments, the set of lung metrics can include at least one disease score characterizing severity of pulmonary disease in the patient. The method may include, for example, generating a set of one or more lung metrics by inputting the patient data into a patient characterization machine learning algorithm (e.g., as described elsewhere herein), where the set of lung metrics includes a first disease score corresponding to severity of a first fype(s) of pulmonary disease and a second disease score corresponding to a severity of a second fype(s) pf pulmonary disease. The first disease score can, for example, correspond to severityof emphysema, and the second disease score can correspond to a severity of non-emphysema disease (e.g., small airways disease, bronchitis, asthma, pulmonary fibrosis, type 2 inflammation, and / or neutrophilic inflammation, etc.). In some embodiments, the treatment planning machine learning algorithm may be configured to receive the first disease score as an input, and map a potential pathway (and / or target airway segment) based on the first disease score. Additionally or alternatively, the treatment planning machine learning algorithm can be configured to receive the second disease score as an input, and exclude one or more pathways from being mapped as a potential pathway and / or exclude one or more airways from being identified as a target airway segment. For example, if the second disease score (e.g., nonemphysema disease score) for a particular region of the lung satisfies a pre-determined threshold and / or exceeds the first disease score (e.g.. emphysema disease score), then the treatment planning machine learning algorithm may provide an output recommendation that no implant be placed in (or delivered via) that region of the lung.

[0151] Additionally or alternatively, in some embodiments, the third software algorithm can analyze lung metrics relating to one or more fissures in the lung (e.g., lobar fissures and / or segmental fissures) to generate a treatment plan for the patient. The analyzed lung metrics can include, for example, identification of position, shape, and / or degree of completeness of fissures between lobes and / or between segments within lobes. In some embodiments, to optimize placement of a minimal endobronchial reinforcement implant, it may be advantageous to place the implant at target locations where the fissures are incomplete. Without being bound by any particular theory, it is believed that incomplete fissures allow for more communication of airflow between adjacent segments and / or adjacent lobes, thereby allowing release of trapped air from more segments (and / or lobes) with few er endobronchial reinforcement implant(s). Accordingly, the third software algorithm can be configured to identify an endobronchial reinforcement implant placement location that is proximate one or more incomplete lobar and / or segmental fissures. In contrast, for some interventional treatments (e.g., placement of one-directional stent valves, which allow air to flow out of but not into overinflated portions of the lung), it may be advantageous to place the implant at target locations where fissures are complete.[0.152| Additionally or alternatively, in some embodiments, the treatment planning machine learning algorithm (and / or other suitable machine learning algorithm) can be configured to identify one or more pharmacological treatment options for treatment of the pulmonary disease. For example, one or more of the patient data or the set of one of more lungmetrics can be input into the treatment planning machine learning algorithm. Based on the patient data and / or lung metrics, the treatment planning machine learning algorithm may be configured to generate an output recommendation for at least one pharmacological treatment (e.g., such as those described herein) for treating the pulmonary disease(s) of the patient. In some embodiments, for example, the placement of an implant may be configured to treat one type of pulmonary disease (e.g., emphysema), and the pharmacological treatment(s) may be configured to treat another type(s) of pulmonary disease (e.g., non-emphysema disease). The recommended pharmacological treatment(s) can be administered in combination with, or separate from (e.g., instead of, or administered at different times and / or target locations), the placement of the implant (e.g., endobronchial implant) in the patient. In some embodiments, the treatment planning machine learning algorithm may be configured such that in response to the first disease score satisfying a first predetermined threshold and the second disease score satisfying a second predetermined threshold, the treatment planning machine learning algorithm can provide an output indicating that both at least one pharmacological treatment and placement of an implant (and / or potential pathway and / or target airway segment to placement of the implant) are warranted for treatment of the pulmonary disease.[0153| The method 2000 can be modified in many different ways. For example, any of the software algorithms illustrated in FIG. 20 can be combined with each other, subdivided into separate algorithms, and / or omitted altogether. For example, as shown in FIG. 28, a method 2800 can be similar to method 2000 except the method 2800 omits at least the second software algorithm (e.g., predicting response of the patient to the treatment). For example, method 2800 can include receiving patient data including CT data of a lung of the patient (block 2810), generating a set of lung metrics by inputting the patient data into a first machine learning algorithm (block 2820), identifying a potential target region in the lung for a treatment for the pulmonary disease based at least in part on the generated lung metrics (block 2830), and generating a plan for treatment of the pulmonary disease (block 2840). In some embodiments, block 2830 and block 2840 can incorporate or be similar to one or more aspects of the third software algorithm. In some embodiments, the first and second software algorithms are combined or replaced with a single software algorithm that directly predicts the patient response from the patient data. Optionally, the third software algorithm can generate a treatment plan directly from the patient data, without requiring the lung metrics and / or predicted response generated by the first and second software algorithms, respectively.[0.154| In some embodiments any of the outputs of the first software algorithm, the second software algorithm, and / or the third software algorithm in the method 2000 can be provided to a user in the form of a patient report. For example, the report can include any one or more of the lung metrics of interest generated by the first software algorithm, a response of the patient predicted by the second software algorithm, and / or a treatment plan generated by the third software algorithm. The report may include written summaries or descriptions of any of such outputs of the software algorithms of the method 2000, annotated diagrams, annotated images, and / or other media (e.g., videos). For example, the report can include an anatomical illustration representing at least a portion of a lung and / or an image (e.g., CT image) of at least a portion of a lung that is annotated with suitable information. Suitable annotation information can include airway measurements (e.g., lumen diameter, wall thickness, etc.) along one or more sections of an airway, disease score for selected regions such as lobe(s), segment(s), and / or sub-segment(s) of a lung, lobe(s), portions of the lung having a disease score exceeding a predetermined threshold, and / or proposed implant placement locations, for example. In some embodiments, such annotations may be descriptive (e.g., with text or numbers) and / or otherwise visualized such as with color coding or line weights (e.g., thicker lines to emphasize airway walls in lung segments or sub-segments of interest). For example, an illustration or image of the lung may include a map or other visualization of the airways in the lung, and an identification of lung lobe(s) or segment(s) having a disease score that exceeds a predetermined threshold (e.g.. with color coding or line weights). As another example, an illustration or image of the lung may include a map or other visualization of the airways in the lung, and an identification of one or more proposed implant placement locations (e.g., with an outline of the implant, highlighting of airway walls in a targeted airway segment with color or line thickness, etc.). In some embodiments, the report may be communicated to a patient medical record (e.g., electronic medical record) for reference.|0155] The method 2000 can be performed using any suitable system or device. In some embodiments, some or all of the processes of the method 2000 are implemented as computer-readable instructions (e.g., program code) that are configured to be executed by one or more processors of a computing device.[O.l56| FIG. 21 is a flow diagram illustrating a method 2100 for evaluating a treatment outcome of a patient, in accordance with embodiments of the present technology. The method 2100 can be performed as part of the post-procedure phase 1906 of the workflow 1900 of FIG. 19. Although the method 2100 is described herein in connection with evaluating the outcomeof endobronchial implant therapy, in other embodiments, the method 2100 can be modified for use in evaluating the outcomes of other types of airway treatments (e.g., pharmacological treatments, non-implant interventional treatments).|0157] The method 2100 can involve receiving patient data, such as questionnaire information, medical record information, image data (e.g., CT data, MRI data, chest radiography data, fluoroscopy data, photographs), PFT data (e.g., spirometry data), bronchoscopy data, data from other diagnostic technologies, and / or any of the other patient data types described herein. Some or all of the patient data can be obtained at a plurality of time points, as discussed above with respect to FIG. 20. The method 2100 can implement one or more software algorithms that use the patient data to analyze whether the treatment procedure was successful and, optionally, propose additional procedures that may further improve the patient outcome.

[0158] The patient data (e g., questionnaire information, PFT data, image data, bronchoscopy data, and / or data from other diagnostic technologies) can be provided to a fourth software algorithm. The fourth software algorithm can be a fourth machine learning algorithm (e.g., lung characterization machine learning algorithm, implant characterization machine learning algorithm, or a combination thereof) that has been trained (e.g., via supervised learning) to synthesize the patient data to compute lung metrics and / or implant metrics. In some embodiments, the fourth software algorithm can additionally or alternatively include other suitable automated processes. For instance, the lung metrics can represent a state of the patient’s lung after the placement of one or more endobronchial implants (e.g., minimal endobronchial reinforcement implants), such as any of the following: FEV (e.g., FEVi), FVC, VC, IC, IC / TLC ratio, functional residual capacity, TLC, diffusion capacity for carbon monoxide, RV, RV / TLC ratio, CV, lobar and / or segmental tissue destruction, lobar and / or segmental air trapping, lobar and / or segmental fissure status, extent of lobar and / or segmental fissure completion, lobar and / or segmental ventilation, lung function, disease phenotype (e.g., homogeneity / heterogeneity of emphysema, type of emphysema (such as centriacinar emphysema, panacinar emphysema, or paraseptal emphysema), location of diseased portions of the lung), lobar volume, segmental volume, segment locations, diaphragm shape, tissue density7, opacity7, proximity of diseased portions to anatomical structures, proximity7of disease portions to other medical devices, lumen inner diameter of bronchial sections, air flow mapping, collapsed airways, airway pressure, , one or more mechanical properties of an airway (e.g., airway compliance, airway resistance, airway elastance, etc.), pleural pressure, airwaydiameter to wall thickness, airway wall deformation, vascular perfusion, parenchyma density, bullae, information that localizes disease in the lung, obstruction score, mucus score, degree of epithelialization, other implant-tissue interactions (e.g., granulation tissue, implant-induced airway deformation, airway tissue invagination into a lumen of the implant, etc.). At least some of these (e.g., airway resistance, airway compliance, airway elastance, etc.) may be measured at any of various levels of the lung, such as a segmental level of the lung, other regional level of the lung, lobar level of the lung, and / or a lung level of the lung. The set of lung metrics can characterize the state of the lung at a single time point, and / or can characterize a change in the state of the lung over plurality of time points (e.g., before and after endobronchial implant therapy, before and after administration of a bronchodilator, before and during exercise). Additionally or alternatively, the lung metrics can be used to generate a disease “score” characterizing severity of pulmonary disease that may remain in one or more regions of the lung following treatment and / or representing an estimate of patient response to a treatment of the pulmonary disease. In some embodiments, the lung metrics can include one or more disease scores, where each disease score corresponds to a respective region of the lung (e.g., a particular lung, a particular lobe, a particular segment, a particular sub-segment). In some embodiments, the lung metrics can include a single disease score at least partially based on multiple “local” disease scores each corresponding to a respective region of the lung (e.g., a particular lung, a particular lobe, a particular segment, a particular sub-segment). For example, a single disease score can be an average value of the multiple local disease scores, or be based on any suitable calculation incorporating the multiple local disease scores. The method 2700 as show n in FIG. 27 for evaluating a patient using such a disease score lung metric (described above with respect to a pre-procedure evaluation) can additionally or alternatively be performed as a post-procedure evaluation. Furthermore, in some embodiments, the method 3200 as shown in FIG. 32 for evaluating a patient to characterize a shape of an airw ay of the lung (described above with respect to a pre-procedure evaluation) can additionally or alternatively be performed as a post-procedure evaluation.

[0159] The implant metrics can represent a state of each endobronchial implant after placement in the lung, such as any of the following characteristics: implant location, distance between a distal end of the implant and pleura, implant length, implant diameter at any one or more locations along the implant length, implant cross-sectional profile at any one or more locations along the implant length, implant integrity' (e.g., implant breakage, deformation), indication of implant expansion and / or collapse (e.g., biasing of the implant in a longitudinaldirection, or “pancaking”) such as pitch of implant loops or angle of implant loop profile relative to a longitudinal axis of the implant), implant position relative to other placed implant(s), movement of one or more implants between inspiration and expiration, occlusion of implant, and / or implant dislodgement. The implant metrics can represent the state of the implant at a single time point, or can represent a change in the state of the implant over a plurality of time points. For example, the fourth software algorithm identifying changes in implant properties from CT imaging taken over various time points may be helpful to understand impacts to patient benefit and / or implant interactions.[0160| As another example, voxel density thresholds for an inspiratory CT scan and / or expiratory CT scan can be analyzed by the fourth software algorithm to characterize a shape of an implant placed in the lung. For example, some embodiments, CT scans can be analyzed to characterize diameter, length, radius of curvature, tortuosity, and / or any suitable shape characteristic of an implant in the lung. For example, as shown in FIG. 35, a method 3500 of evaluating a patient can include receiving patient data including CT data of a lung of the patient 3510 (e.g., similar to that described elsewhere herein), and generating a set of lung metrics by inputting the patient data into an implant characterization machine learning algorithm 3520, where the set of lung metrics characterizes a shape of an implant of the lung at one or more timepoints within a respiratory cycle. In some embodiments, the fourth software algorithm can incorporate one or more voxel density thresholds to characterize a shape of an implant at one or more timepoints within a respiratory cycle, such as maximal inspiration, maximal expiration, tidal volume inspiration, and / or tidal volume expiration, where the boundaries of the airway in CT data can be predicted based on those lung voxels having atenuation above a suitable threshold, such as above 1500HU. Accordingly, in some embodiments, once the implant characterization machine learning algorithm is trained, the method 3500 can be performed to characterize implant shape for a patient using CT data as an input.|0161] As another example, in some embodiments, the fourth software algorithm can additionally or alternatively analyze a follow up expiratory CT scan to generate lung metrics including geometrical, physical, and / or mechanical properties of the airways, such as airway compliance, pleural pressure, airway diameter to wall thickness, and / or airway wall deformation. Such properties can be measured particularly at the segmental level, which (compared to such information determined solely at the lobar level) can provide more insight into the patient condition for use in determining a response of the patient to treatment. Additionally or alternatively, the fourth software algorithm can generate lung metrics includingidentification of any remaining collapsed airways (and their severity of collapse, such as airway diameter). Furthermore, in some embodiments the fourth software algorithm can generate implant metrics characterizing state of the placed implant from the expiratory CT scan, such as any of those described above. Additionally or alternatively, the fourth software algorithm can include other suitable quantitative CT (QCT) techniques, such as those described in further detail herein. Identification of geometric and / or positional changes of the implant over time as well as changes to lung anatomy such as lobe volume, diaphragm shape, density, opacity, and / or segment location could be extracted to draw connections between patient anatomy, implant orientation, and treatment results.(01621 In some embodiments, the lung metrics may be a post-procedural set of lung metrics characterizing a state of the patient’s lung after the placement of one or more endobronchial implants in the lung, such as shape of an airway and / or any of the lung metrics described above, with the second set of lung metrics based at least in part on a pre-procedural set of lung metrics characterizing a state of the patient’s lung prior to the placement of one or more endobronchial implants in the lung. For example, the pre-procedural set of lung metrics and / or patient data relating to implant placement (e.g., number of endobronchial implants to be placed in the lung, location(s) of endobronchial implant(s) to be placed in the lung) may be input into a patient characterization machine learning algorithm (e.g., similar to the first machine learning algorithm, as described elsewhere herein), where the patient characterization machine learning algorithm may be trained to predict a state of the patient’s lung (e.g., shape of an airway in which the implant is placed) based at least in part on the pre-procedural set of lung metrics and / or patient data relating to implant placement.[01 31 Additionally or alternatively, the method may further include generating a set of one or more predicted implant metrics that represent a predicted state (e.g., shape and / or other implant metrics such as those described elsewhere herein) of the endobronchial implant after placement of the endobronchial implant in the lung, such as by inputting the postprocedural set of lung metrics into an implant characterization machine learning algorithm (e.g., similar to that described elsewhere herein). Such an implant characterization machine learning algorithm may be trained to predict a state of the endobronchial implant (e.g., shape) based at least in part on the pre-procedural set of lung metrics and / or patient data relating to implant placement. For example, in some embodiments, the pre-procedural lung metrics may include baseline characteristics of a lung of the patient prior to placement of one or more endobronchial implants in the lung, and the set of one or more predicted implant metrics maybe based on at least a portion of such pre-procedural lung metrics and / or patient data relating to implant placement (e.g.. number of endobronchial implants to be placed in the lung, location(s) of endobronchial implant(s) to be placed in the lung). As such, the method may further include predicting how lung metrics and / or implant metrics would change given baseline characteristics of the lung of the patient and / or implant placement information (e.g., number and / or location of endobronchial implants to be placed in the patient).[0164} In some embodiments, the method 2100 uses a fifth software algorithm that analyzes the patient data (e.g., questionnaire information, PFT data, image data, bronchoscopy data, medical record information, and / or data from other diagnostic technologies), lung metrics, and / or implant metrics to determine a response of the patient to endobronchial implant therapy. The fifth software algorithm can be a fifth machine learning algorithm that has been trained (e.g., via supervised learning) to compare the lung metrics, implant metrics, and / or patient data pre- and post-procedure to quantify the benefits to the patient. In some embodiments, the fifth software algorithm can additionally or alternatively include other suitable automated processes. The output of the fifth software algorithm can be indicators of the degree of patient response such as FEV (e.g., FEVi), FVC, VC, IC, IC / TLC, functional residual capacity', TLC, diffusion capacity for carbon monoxide, RV / TLC, RV. segmental volume, mMRC score, SGRQ score or a subset thereof, CAT score or a subset thereof, 6-minute walk test results, cycle ergometry results, CPET results, patient health metrics (e.g., heart rate, blood pressure, body mass index), patient exercise metrics (e.g., number of steps taken), patient visit metrics, quality of life metrics (e.g.. ability to breathe), number of required implant removals, time to reintervention, durability’ of treatment, comorbidities, drug regimen, length of hospitalization, healthcare utilization, and / or cost; and / or a change (e.g., increase or decrease) in any of the above.[0165| In some embodiments, for example, the fifth software algorithm can include analyzing pre- and post-procedure CT scans, yvhich may provide additional insight to the effect and / or efficacy of treatment. For example, as shown in FIG. 24, the fifth software algorithm can analyze changes in lung volume, lobar volume, segmental volume, fissure position(s) (e.g., status and / or extent of completeness), diaphragm shape, central airway shape (e.g., collapsed central airways due to hyperinflation compared to normal central airways due to release of trapped air post-procedure), etc. based on information derived from CT scans (e.g., evaluation of voxel density, etc ). Additionally or alternatively, the fifth software algorithm can include other suitable quantitative CT (QCT) techniques, such as those described in further detail herein.[0.166| In some embodiments, the method 2100 uses a sixth software algorithm that analyzes the patient data (e.g., questionnaire information, PFT data, image data, bronchoscopy data, medical record information, and / or data from other diagnostic technologies), lung metrics, implant metrics, and / or determined response to predict patient outcome after endobronchial implant therapy. The sixth software algorithm can be a sixth machine learning algorithm that has been trained (e.g., via unsupervised learning) to analyze post-procedure data metrics over time to predict future outcomes. In some embodiments, the sixth software algorithm can additionally or alternatively include other suitable automated processes. For example, the predicted outcomes can include whether the patient’s prognosis is likely to improve, remain stable, or deteriorate over time. As another example, the predicted outcomes can include a prediction of a post-procedure issue, such as copious mucus, excessive granulation tissue, excessive fibrosis, implant collapse, implant migration, implant failure, implant invagination, implant occlusion, implant expectoration, inadequate lung function, pneumothorax, infection, pneumonia, and / or hospitalization.[0167| In some embodiments, the sixth software algorithm determines one or more interventions to prevent, mitigate, or otherwise address the predicted issue, such as cleanup bronchoscopy, retrieval and / or removal of one or more implants, replacement of one or more implants, repositioning of one or more implants, dilation of one or more implants, placement of one or more additional implants (e.g., placing one or more additional implants in other lobes or a contralateral lung), consultation with healthcare professionals, and / or additional treatment procedures (e.g., medication, surgery, other medical devices). For instance, cleanup bronchoscopy at scheduled intervals can be recommended if copious mucus is predicted. Implant removal can be recommended if excess granulation tissue and / or fibrosis is predicted. Balloon dilatation can be recommended if implant collapse is predicted. Placement of an additional implant can be recommended if inadequate improvement in FEVi is predicted. Implant removal or replacement can be recommended if implant failure is predicted. Preventative doctor visits can be recommended if hospitalization is predicted.

[0168] For example, in some embodiments, the sixth software algorithm may be configured to analyze data metrics (e.g., patient data such as image data including QCT data) to evaluate the predicted need to place one or more additional implants (e.g., placing one or more additional implants in other lobes, or a contralateral lung) following a first treatment of placing one or more implants in the lung. In some embodiments, the sixth software algorithm (or other suitable software algorithm) can, for example, receive various baseline data as aninput (e.g., prior to first treatment of placing one or more implants in the lung) and be configured to develop a treatment plan including multiple treatments of implant placement based on the baseline data. For example, the sixth software algorithm (or other suitable software algorithm) can develop a plan for multiple treatments over time, based on patient data and / or lung metrics such as emphysema severity, heterogeneity7, and / or degree of hyperinflation. The priority, sequence, and / or time urgency of the multiple treatments may be based at least in part on such patient data and / or lung metrics. Additionally or alternatively, in some embodiments, the sixth software algorithm (or other suitable software algorithm) can, for example, receive baseline data and new data as an input after each treatment (e.g., a first set of data after first treatment of placing one or more implants in the lung, and a second set of data after a second treatment of placing one or more additional implants in the lung), and develop a plan for the next subsequent treatment. For example, in some embodiments a QCT comparison analysis may be performed between patient data originating at two different time points (e.g., baseline data and data after first treatment, baseline data and data after second treatment, or data after first treatment and data after second treatment) in order to help inform generation of a treatment plan for any additional implant(s) to be placed in the patient and / or other treatment (e g., pharmacological treatment) to be administered to the patient for treatment of pulmonary disease(s). As such, the sixth software algorithm and / or other suitable software algorithms can be configured to help with treatment planning including an initial treatment of placement of one or more implants, and subsequent treatment(s) of placement of one or more additional implants (e.g., subsequent “top up” treatments). The sixth software algorithm and / or other suitable software algorithms can, for example, be configured to help with generating a treatment plan including one, two, three, four, five, or more than five treatments of implant placement.

[0169] The method 2100 can be modified in many different ways. For example, any of the software algorithms illustrated in FIG. 21 can be combined with each other, subdivided into separate algorithms, and / or omitted altogether. Optionally, the fifth software algorithm can determine the patient response directly from the patient data, without requiring the lung metrics and / or implant metrics generated by the fourth software algorithm, respectively. Similarly, the sixth software algorithm can predict the patient outcome directly from the patient data, without requiring the lung metrics and / or implant metrics generated by the fourth software algorithm, and / or without the patient response determined by the fifth software algorithm.[0.1701 Similar to that described with respect to the method 2000, in some embodiments any of the outputs of the fourth software algorithm, the fifth software algorithm, and / or the sixth software algorithm in the method 2100 can be provided to a user in the form of a patient report. For example, the report can include any one or more of the lung and implant metrics of interest generated by the fourth software algorithm, a patient response to endobronchial implant therapy (e.g., quantified patient benefit) generated by the fifth software algorithm, and / or predicted future patient outcome(s) and / or proposed interventions generated by the third software algorithm. The report may include written summaries or descriptions of any of such outputs of the software algorithms of the method 2100, annotated diagrams, annotated images, and / or other media (e.g., videos). For example, the report can include an anatomical illustration representing at least a portion of a lung and / or an image (e.g., CT image) of at least a portion of a treated lung and / or implant placed in the treated lung that is annotated with suitable information. Suitable annotation information can include airway measurements (e.g., lumen diameter, wall thickness, etc.) along one or more sections of an airway, disease score for selected regions such as lobe(s), segment(s). and / or sub-segment(s) of a lung, lobe(s), portions of the lung having a disease score exceeding a predetermined threshold, and / or implant placement location(s), for example. In some embodiments, such annotations may be descriptive (e.g., with text or numbers) and / or otherwise visualized such as with color coding or line weights (e.g., thicker lines to emphasize airway walls in lung segments of interest). For example, an illustration or image of the lung may include a map or other visualization of the airways in the lung, and an identification of lung lobe(s), segment(s), or sub-segment(s) having a disease score that exceeds a predetermined threshold (e.g., with color coding or line weights). As another example, an illustration or image of the lung may include a map or other visualization of the airways in the lung, and an identification of one or more implant locations (e.g., with an outline of the implant, highlighting of airway walls in an airway segment with color or line thickness, etc.). As another example, the report can include access to a virtual bronchoscopy based on a reconstructed 3D model of the treated lung and placed implant, as described herein. In some embodiments, the report may be communicated to a patient medical record (e.g., electronic medical record) for reference.[0.1711 The method 2100 can be performed using any suitable system or device. In some embodiments, some or all of the processes of the method 2100 are implemented as computer-readable instructions (e.g., program code) that are configured to be executed by one or more processors of a computing device.[0.172| FIG. 22 is a flow diagram illustrating a method 2200 for updating the software algorithms of FIGS. 20 and 21, in accordance with embodiments of the present technology. The method 2200 can involve obtaining data regarding patient responses to endobronchial implant therapy from a plurality of different patients. The data may be generated by the fifth software algorithm of the method 2100 of FIG. 21. The patient response data can be provided to a seventh software algorithm. The seventh software algorithm can be a seventh machine learning algorithm that is trained (e.g., via supervised learning techniques) to classify the patient response to treatment into various categories, such as “best case,” “worst case,” “mediocre case,” etc. In some embodiments, the seventh software algorithm can additionally or alternatively include other suitable automated processes. The outputs of the seventh software algorithm can be correlated to other types of data, such as indicators of treatment success (e.g., lung metrics), image data (e.g., CT data), and / or other types of patient data (e.g., medical record information). These correlations can be used to update at least some of the software algorithms of FIGS. 20 and 21.[0173| For example, correlations between patient responses and lung metrics (and / or other indicators of treatment success) can be used to update the second software algorithm (FIG. 20), third software algorithm (FIG. 20). fifth software algorithm (FIG. 21), and / or sixth software algorithm (FIG. 21). In embodiments where these software algorithms are or include machine learning algorithms, the correlations can be used as training data for the machine algorithms (e.g., for unsupervised learning). The correlations can inform the algorithms on which lung metrics and / or other indicators of treatment success are associated with successful treatment procedures versus unsuccessful procedures.[O.I74| As another example, correlations between patient responses and image data (e.g., CT data) can be used to update the first software algorithm (FIG. 20), fourth software algorithm (FIG. 21), fifth software algorithm (FIG. 21), and / or sixth software algorithm (FIG. 21). In embodiments where these software algorithms are or include machine learning algorithms, the correlations can be used as training data for the machine algorithms (e.g., for unsupervised learning). The correlations can inform the algorithms on which observations from CT data and / or other image data are associated with successful treatment procedures versus unsuccessful procedures. Optionally, observations from CT data and / or other image data that are associated with successful and / or unsuccessful treatment procedures can be used to update (e.g.. train) the second software algorithm (FIG. 20), third software algorithm (FIG. 20), fifth software algorithm (FIG. 21), and / or sixth software algorithm (FIG. 21) to determine new lungmetrics and / or other indicators of treatment success that may be helpful in predicting the success or failure of a treatment procedure.[01751 In a further example, correlations between patient responses and medical record information can be used to update the second software algorithm (FIG. 20). fifth software algorithm (FIG. 21), and / or sixth software algorithm (FIG. 21). In embodiments where these software algorithms are or include machine learning algorithms, the correlations can be used as training data for the machine algorithms (e.g., for unsupervised learning). The correlations can infonn the algorithms on what information from patient medical records are associated with successful treatment procedures versus unsuccessful procedures.[0176| Any of the software algorithms described herein (e.g., Algorithms 1-7 of FIGS. 20-22) can be updated and / or refined based on historical and / or repository patient data. This historical patient data may be sourced from a database of previous patients, which could include data from the same patient at earlier time points as well as data of other patients. This historical patient data may also be sourced from a repository that may include patients with lung diseases (e.g., GOLD III COPD, GOLD IV COPD) with or without interventions, such as endobronchial implants (e.g., minimal endobronchial reinforcement implants, valves, coils), vapor therapy, or pharmacological treatments (e.g., inhaled bronchodilators). For example, the software algorithms can be updated for predictive power using hierarchical Bayesian modeling, with the prior probability in the Bayesian scheme derived from historical or repository patient data.

[0177] Additional aspects of methods of patient selection for treatment with an endobronchial reinforcement implant are described below, and which can be combined with any of the other methods for patient selection, target selection, treatment planning, treatment, and / or monitoring as described herein. For example, such methods for patient selection may be for treatment of the selected patients with the device 100 described herein with respect to FIGS. 16 and 17. As shown in FIG. 36, in some variations, a method 3600 may include receiving a set of lung metrics characterizing lung state of a patient 3610, where the set of lung metrics include disease heterogeneity status, residual volume (RV), lung capacity (TLC), emphysema score, and / or any combination thereof. The method 3600 may further include detennining that patient is a candidate for treatment with an endobronchial reinforcement implant 3620 based on the set of lung metrics. In some variations, the method 3600 may further include a treatment process including placing an endobronchial reinforcement implant in at least one lung of the patient 3630 in response to determining the patient is a candidate for treatment.[0.178| The set of lung metrics may be obtained in any suitable manner. For example, heterogeneity’ status may be derived at least in part from medical imaging of the lung, such as CT scan data. A patient’s emphysematous lungs may be characterized as bilateral heterogeneous (both left and right lungs exhibit heterogeneous emphysema), unilateral homogeneous (one lung exhibits heterogeneous emphysema, one lung exhibits homogeneous emphysema), or bilateral homogeneous (both left and right lungs exhibit homogeneous emphysema). In some variations, a heterogeneous lung may be defined as a lung having at least 15% difference in areas of emphysema destruction between upper and lower lobes of the lung (e.g., areas having attenuation values below about -910 HU or -950 HU in CT scan data). RV and TLC may be obtained through baseline lung function tests such as spirometry (e.g., measurements in accordance with the Global Lung Function Initiative (GLI) framework). Further lung metrics may be based on combinations of these and / or other lung function metrics (e.g., RV / TLC may be a separate lung metric), such as any suitable lung metric representative of extent of hyperinflation. Emphysema score (e.g., emphysema destruction score, emphysema index) as an indicator of disease severity may be determined through medical imaging of the lung (e.g., CT scan data). The emphysema score may, for example, be at least in part based on the percentage area of the lung with attenuation values below about -950 HU in CT scan data. Additionally or alternatively, in some variations the emphysema score may be at least part derived from spirometry, using any suitable index calculation.

[0179] The set of lung metrics may be used to determine whether a patient is a likely candidate for treatment. Based on empirical data, a number of potential criteria have been identified.[01801 For example, in some variations, a patient may be identified as a candidate for treatment in response to determining, based on the set of lung metrics, that (i) the patient has two lungs exhibiting heterogeneous emphysema (bilateral heterogeneous) and (ii) the patient has a baseline RV / TLC metric that is equal to or above a threshold for each lung, for one lung (left or right lung), for the whole patient (e.g., both lungs), or combination thereof. In some variations, such threshold is at least 0.55. In some variations, such threshold is at least 0.65. Accordingly, as shown in Table 1 below, a patient may be identified as a likely candidate for treatment if the patient is bilateral heterogeneous and has an RV / TLC of equal to or greater than at least 0.55 (‘‘Patient Selection Type 1”). Similarly, a patient may be identified as a likely candidate for treatment if the patient is bilateral heterogeneous and has an RV / TLC of equal to or greater than at least 0.65.[0.1811 Additionally or alternatively, in some variations, a patient may be identified as a candidate for treatment in response to determining, based on the set of lung metrics, that (i) the patient has two lungs exhibiting heterogeneous emphysema (bilateral heterogeneous) and (ii) the patient has a baseline RV that is equal to or above a threshold. In some variations, such threshold is between about 150% and about 180%, or at least 180%. Accordingly, as shown in Table 1, below, a patient may be identified as a likely candidate for treatment if the patient is bilateral heterogeneous and has an RV of equal to or greater than 180% ("Patient Selection Type 2”).[0.182} Additionally or alternatively, in some variations, a patient may be identified as a candidate for treatment in response to determining, based on the set of lung metrics, that (i) the patient has two lungs exhibiting heterogeneous emphysema (bilateral heterogeneous) and (ii) the patient has at least an emphysema score (e.g., emphysema index) equal to above a threshold for each lung, for one lung (left or right lung), for the whole patient (e.g., both lungs), or combination thereof. In some variations, such threshold is at least 25%. Accordingly, as shown in Table 1 below, a patient may be identified as a likely candidate for treatment if the patient is bilateral heterogeneous and has an emphysema score equal to or greater than at least 25% (“Patient Selection Type 3”).[0183| Additionally or alternatively, in some variations, a patient may be identified as a candidate for treatment in response to determining, based on the set of lung metrics, that (i) the patient has one lung exhibiting heterogeneous emphysema (unilateral homogeneous) and (ii) the patient has a baseline RV / TLC metric that is equal to or above a threshold for each lung, for one lung (left or right lung), for the whole patient (e.g., both lungs), or combination thereof. In some variations, such threshold is at least 0.55. Accordingly, as shown in Table 1 below, a patient may be identified as a likely candidate for treatment if the patient is bilateral heterogeneous and has an RV / TLC of equal to or greater than at least 0.55 (“Patient Selection Type 4”).|0184| Additionally or alternatively, in some variations, a patient may be identified as a candidate for treatment in response to determining, based on the set of lung metrics, that (i) the patient has one lung exhibiting heterogeneous emphysema (unilateral homogeneous) and (ii) the patient has a baseline RV that is equal to or above a threshold for each lung, for one lung (left or right lung), for the whole patient (e.g., both lungs), or combination thereof. In some variations, such threshold is betw een about 175% and about 225%, such as at least 200%. Accordingly, as shown in Table 1 below, a patient may be identified as a likely candidate fortreatment if the patient is unilateral homogeneous and has an RV of equal to or greater than 200% (“Patient Selection Type 5”).(01851 Additionally or alternatively, in some variations, a patient may be identified as a candidate for treatment in response to determining, based on the set of lung metrics, that (i) the patient has two lungs exhibiting homogeneous emphysema (bilateral homogeneous) and (ii) the patient has a baseline RV that is equal to or above a threshold for each lung, for one lung (left or right lung), for the whole patient (e.g., both lungs), or combinations thereof. In some variations, such threshold is between about 200% and about 225%, such as at least 215%. Accordingly, as shown in Table 1 below, a patient may be identified as a likely candidate for treatment if the patient is bilateral homogeneous and has an RV of equal to or greater than 215% (“Patient Selection Type 6”).|0186] Additionally or alternatively, in some variations, regardless of heterogeneity status, RV, TLC, and RV / TLC, a patient may be identified as a candidate for treatment in response to determining, based on the set of lung metrics, that the patient has an emphysema score equal to or greater than a threshold for each lung, for one lung (left or right lung), for the whole patient (e.g., both lungs), or combination thereof. In some variations, such threshold may be between about 10% and about 30%, such as about 10%, about 15%, about 20%, about 25%, or about 30% based on -950HU. In some variations, such threshold may be between about 25% and about 50%, such as about 25%, about 30%, about 35%, about 40%, about 45%, or about 50% based on -910HU.

[0187] For example, in some variations, such threshold is at least 25%. Accordingly, as shown in Table 1 below, a patient may be identified as a likely candidate for treatment if the patient has an emphysema score equal to or greater than at least 25% (“Patient Selection Type 7”).Table 1. Example patient selection criteria[0188J In some variations, the set of lung metrics may additionally or alternatively include other metrics that may be analyzed to determine whether a patient is a candidate for treatment. For example, the set of lung metrics may additionally or alternatively include metric(s) representative of mucus plugging, suspected pulmonary hypertension, dynamic hyperinflation (e.g.. as quantified by measuring end expiratory lung volume (EELV)), bronchial wall thickening, fibrosis, scarring, extent of disease in small airways, and / or extent of disease in large airways in the patient. In such variations, a patient may be identified as a candidate for treatment in response to determining whether the patient has minimal mucus plugging, minimal suspected pulmonary hypertension, minimal bronchial wall thickening, minimal fibrosis or scarring, minimal small airways disease, and / or minimal large airways disease. For purposes of this analysis, “minimal” may be defined as meeting a respective predetermined threshold for the analyzed metric where appropriate (e.g., by QCT metrics), and / or may be qualitatively assessed (e.g., by radiology read).[0.1891 Additional examples of potential patient metrics for use in determining whether a patient is a candidate for treatment are shown in the table of FIG. 39. However, based on an analysis of clinical data for subjects receiving one or more endobronchial reinforcement implants (specifically, an implant similar to device 100 shown in FIGS. 16 and 17), it was discovered that those subjects who responded the best to treatment with such implants (e.g., based on improvement in FEV1, RV, etc. as shown in FIG. 40) were those subjects whose baseline characteristics correlated to heterogeneity status, RV. RV / TLC, and emphysema score as described above (e g., with respect to Table 1 ).[019 [ As described above, after a patient is identified as a candidate for treatment, the method 3600 may include placing at least one endobronchial reinforcement implant in at least one lung of the patient. In some variations, the endobronchial reinforcement implant comprisesa minimal endobronchial reinforcement implant such as device 100 described herein (e.g., with respect to FIGS. 16 and 17). In some variations, the method may include placing multiple endobronchial reinforcement implants in one lung or multiple lungs. Target airway segments for receiving endobronchial reinforcement implants may, in some variations, be identified using methods such as that described herein.

[0191] Also described below are aspects of methods for selecting one or more target airways for treatment with an endobronchial reinforcement implant, and which can be combined with any of the other methods for patient selection, target selection, treatment planning, treatment, and / or monitoring as described herein. For example, such methods for target selection may be for treatment of a patient with the device 100 described herein with respect to FIGS. 16 and 17. As shown in FIG. 37, in some variations, a method 3700 may include receiving computer tomography (CT) or other medical imaging of a lung of a patient 3710, identifying one or more candidate airway segments in the lung based on the CT data 3720, calculating a volume of emphysema destruction in each candidate airway segment 3730, determining one or more target airway segments in the lung based on the calculated volumes of emphysema destruction 3740, and placing an endobronchial reinforcement implant in each of the one or more target airway segments 3750.[0192j Receiving CT data of a lung of a patient 3710 functions to receive medical imaging from which the extent of emphysema damage may be quantified. In some variations, the CT data comprises expiratory CT data and / or inspiratory CT data taken during expiration and / or inspiration by the patient, respectively. Analysis of the CT data may be based on expiratory CT data, inspiratory' CT data, or a combination thereof. From the CT data, metrics regarding airway segments in the lung may be determined, such as emphysema score (e.g., emphysema index), airway segment length, airway segment diameter, airway segment air volume, airway segment tissue volume, etc. These metrics may be determined manually, using suitable AI / ML and / or computer vision techniques, or a combination thereof. Examples of such analysis of CT data are described in further detail for example, in U.S. Provisional Patent Application No. 63 / 667,471, which is incorporated herein in its entirety by reference.

[0193] Identifying one or more candidate airway segments in the lung 3720 functions to identify those airway segments suitable for further assessment as potential treatment targets for placement of an endobronchial reinforcement implant. In some variations, an airway segment is identified as a candidate airw ay segment if the airway segment has an emphysema score of at least a threshold value. For example, in some variations such threshold value isbetween about 10% and about 30%, such as 20%. In some variations, one criterion for an airway segment being identified as a candidate airway segment is whether the airway segment is of a certain size, such as having segmental diameter within a predetermined range. Such predetermined range may, in some variations, depend on the diameter and / or other design characteristics of the endobronchial reinforcement implant to be implanted. For example, in some variations an airway segment may be identified as a candidate airway segment if the airway segment has both (i) an emphysema score of at least a threshold value (e.g., 20%) and a segmental diameter within a predetermined range (e g., between about 3 mm and about 9mm). In some variations, a criterion for an airway segment being identified as a candidate airway segment is whether the airway segment undergoes above a threshold change in airway diameter between inspiration and expiration (e.g., a decrease of at least between about 50% and about 100% in airway diameter between inspiration and expiration, or at least between about 70% and about 100% in airway diameter between inspiration and expiration). This change in airway segmental diameter may be correlated to extent of airway collapse. The segmental diameter may, for example, be an average airway diameter measured along the length of the airway segment, or may be a minimum segmental diameter measured along the length of the airway segment, or may be a maximum segmental diameter measured along the length of the airway.[019 1 As described herein, the method 3700 may further include calculating a volume of emphysema destruction in each candidate airway segment 3730. The volume of emphysema destruction for a candidate airway segment may be based on a volume metric of the candidate airway segment and the emphysema score for the candidate airway segment. In some variations, this volumetric calculation may be performed for only a subset of airway segments (e.g., candidate airway segments identified in process 3720), but in some variations the volumetric calculation may be performed for all airway segments.

[0195] In some variations, the volume metric used in process 3730 may include an air volume of the airway segment being assessed (e.g., total volume of the airway segment at inspiration minus total volume of the tissue in the airway segment at inspiration, or total volume of the segment at expiration minus total volume of the tissue in the airway segment at expiration), a tissue volume of the airway segment being assessed (e.g., total volume of the tissue in the airway segment at inspiration or at expiration), total volume of the airway segment at inspiration or expiration, or a combination thereof (e.g.. average of air volume and tissue volume, difference in air volume and tissue volume, etc.). In some variations, the air volume and / or tissue volume metric may be determined directly from CT data, such as measuring 3Dvolumetric imaging, measuring cross-sectional 2D imaging (e.g., calculations based on airway or tissue diameter of the airway segment), etc.[0196| In some variations, the calculation of the volume of emphysema destruction in a candidate airway segment may include adding and / or multiplying the volume metric and the emphysema score. Additionally or alternatively, the calculation of the volume of emphysema destruction in a candidate airway segment may include applying a weight factor to one or both of the volume metric and the emphysema core. For example, the calculation may include applying a first weight factor to the volume metric and applying a second weight factor to the emphysema score, where the first and second weight factors may be same or different (e.g., first weight factor is greater than the second weight factor, or second weight factor is greater than the first weight factor).|0197] As described herein, the method 3700 may further include determining one or more target airway segments in the lung based on the calculated volumes of emphysema destruction 3740. In some variations, a candidate airway segment having the largest calculated volume of emphysema destruction may be determined as a target airway segment for placement of an endobronchial reinforcement implant. However, multiple target airway segments may be identified from the calculated volumes of emphysema destruction. In some variations, determining one or more target airway segments may include identifying, as two target airway segments, two candidate airway segments having the largest two calculated volumes of emphysema destruction out of the candidate airway segments. In some variations, determining one or more target airway segments may include identifying, as three target airway segments, three candidate airway segments having the largest three calculated volumes of emphysema destruction out of the candidate airway segments.[0198| The method 3700 may further include placing an endobronchial reinforcement implant in each of the one or more target airway segments 3750. In some variations, the method 3700 may further include selecting a size of the endobronchial reinforcement implant to be placed in each target airway segment, for example selecting a length and / or a diameter of the implant, which may be selected relative a size (e.g., length and / or diameter) of the target airway segment). Example systems and methods for delivering an endobronchial reinforcement implant are described in PCT Application No. PCT / US24 / 13012, which is incorporated herein in its entirety by this reference. However, any suitable deli ery techniques may be used to place an endobronchial reinforcement implant.[0.199| In some variations, processes 3710, 3720, 3730, 3740, and 3750 may be performed for multiple lungs, such as for each of a left and a right lung of a patient. As such, the method 3700 may include determining one, two, or three (or more) target airway segments per lung for placement of a respective endobronchial reinforcement implant, and one, two, or three (or more) endobronchial reinforcement implants may be placed in each lung accordingly.V. Systems for Patient and Target Selection[0200J FIG. 38 is a schematic diagram of an example arrangement 2100 in accordance with the present technology'. As shown in FIG. 38, the arrangement 3800 may include a patient selection system 3810 and / or a target selection system 3820. Although the patient selection system 3810 and the target selection system 3820 are both shown in FIG. 38. it should be understood that in some variations, the arrangement 3800 may omit either the patient selection system or the target selection system 3820. The patient selection system 3810 and / or the target selection system 3820 may be communicatively coupled to an imaging system 3820 and / or an electronic health record system 3840 (EHR) that may be configured to store medical images and / or other personalized data for a patient. These systems may be communicatively coupled through a suitable wired and / or wireless connection (e.g., communications network 3850) that enables information transfer. Additionally or alternatively, information transfer between one or more of these components of the system 3800 can occur in a discrete manner, such as by storing information from one component (e.g., data) on a memory device that is readable by another component. Although the patient selection system 3810, the target selection system 3820, the imaging system 3830, and the EHR system 3840 are illustrated schematically in FIG. 38 as separate components, in some variations two or more of these components may be embodied in a single device. For example, any two, any three, any four, or all four of the patient selection system 3810, the target selection system 3820, the imaging system 3830, and the EHR system 3840 may be combined in a single device. However, in some variations each of the patient selection system 3810, the target selection system 3820, the imaging system 3830, and the EHR system 3840 can be embodied in a respective separate device.[0201 J The patient selection system 3810 may include one or more processors 3812 and one or more memory devices 3814 having instructions stored therein. The one or more memory devices 3814 can include any suitable computer-readable medium such as RAMs, ROMs, flash memory, EEPROMs, optical devices (e.g., CD or DVD), hard drives, floppy drives, or any suitable device. The memory device 3814 can include instructions (e.g., organized in one or more modules) for selecting a patient for treatment of a pulmonary diseasewith an endobronchial reinforcement implant in accordance with any of the methods described in further detail herein (e.g., methods 1900, 2000, 2100, 2700, and / or 3600).[02021 The processor 3812 may be configured to execute the instructions that are stored in the memory device 3814 such that, when it executes the instructions, the processor 3812 performs aspects of the methods herein. The instructions may be executed by computerexecutable components integrated with a software application, applet, host, server, network, website, communication service, communication interface, hardware, firmware, software elements of a user computer or mobile device, smartphone, or any suitable combination thereof. In some variations, the one or more processors 3812 can be incorporated into a computing device or system such as a cloud-based computer system, a mainframe computer system, a grid-computer system, or other suitable computer system.|0203] The patient selection system 3810 may be configured to receive one or more images of a lung that are generated by the imaging system 3830, such as directly from the imaging system 3830 or from a storage medium such as the EHR system 3840. The imaging system 3830 may, for example, include a CT imaging device.[0204| Similar to the patient selection system 3810, the target selection system 3820 may include one or more processors 3822 and one or more memory devices 3824 having instructions stored therein. The one or more memory' devices 3824 can include any suitable computer-readable medium such as RAMs, ROMs, flash memory, EEPROMs, optical devices (e.g., CD or DVD), hard drives, floppy drives, or any suitable device. The memory device 3824 can include instructions (e.g., organized in one or more modules) for selecting one or more target airway segments in a patient for placement of an endobronchial reinforcement implant in accordance with any of the methods described in further detail herein (e.g., methods 1900, 2000, 2100, 2800, 3400, and / or 3700).[0205| The processor 3822 may be configured to execute the instructions that are stored in the memory device 3824 such that, when it executes the instructions, the processor 3822 performs aspects of the methods herein. The instructions may be executed by computerexecutable components integrated with a software application, applet, host, server, network, website, communication service, communication interface, hardware, firmware, software elements of a user computer or mobile device, smartphone, or any suitable combination thereof. In some variations, the one or more processors 3822 can be incorporated into a computingdevice or system such as a cloud-based computer system, a mainframe computer system, a grid-computer system, or other suitable computer system.VI. Methods of Treatment

[0206] FIGS. 41 and 42 are illustrative schematics of example variations of methods of treatment of a patient with an endobronchial reinforcement implant, which may be performed alone or in combination with methods of patient selection and target selection as described herein (e.g., methods 1900, 2000, 2100, 2700, and / or 3600, methods 1900, 2000, 2100, 2800, 3400, and / or 37000). In some variations, the endobronchial reinforcement implant may be a minimal endobronchial reinforcement implant (e.g., device 100).[02071 For example, as show n in FIG. 41, a method of treatment 4100 may include determining one or more target airway segments in at least one lung of a patient 4110, wherein the patient is identified as having emphysema, and placing an endobronchial reinforcement implant 4120 in each of the one or more target airway segments.10208] In some variations, the number of implants placed in a particular lung may vary based at least in part on the type of emphysema exhibited in the lung. For example, in some instances, the patient may have at least one lung exhibiting heterogeneous emphysema. In some variations, only one implant may be placed in such a lung exhibiting heterogeneous emphysema. In some variations, only two implants may be placed in such a lung exhibiting heterogeneous emphysema. In some variations, only three implants may be placed in such a lung exhibiting heterogeneous emphysema.

[0209] Additionally or alternatively, in some instances, the patient may have at least one lung exhibiting homogeneous emphysema. In some variations, at least one (e.g., one, two, three, four, five, or six, etc.) implants may be placed in such a lung exhibiting homogeneous emphysema. For example, in some variations, at least three implants, more than three implants, up to five implants, or up to six implants may be placed in a lung exhibiting homogeneous emphysema. In some variations, at least one implant may be placed in each lobe of a lung exhibiting homogeneous emphysema (e.g., at least one implant in each major lobe of the lung, such as at least one implant in an upper lobe of the lung and at least one implant in a lower lobe of the lung).

[0210] Additionally or alternatively, in some instances, the patient may have one lung exhibiting homogeneous emphysema and one lung exhibiting heterogeneous emphysema (that is, the patient may be unilateral homogeneous). In such variations, more implants may beplaced in the lung exhibiting homogeneous emphysema than in the lung exhibiting heterogeneous emphysema. For example, placing an endobronchial reinforcement implant may include placing a first number of endobronchial reinforcement implants in the lung exhibiting homogeneous emphysema and placing a second number of endobronchial reinforcement implants in the lung exhibiting heterogeneous emphysema, wherein the second number is greater than the first number.[0 11 [ Additionally or alternatively, in some instances, the patient may have both lungs exhibiting homogeneous emphysema. In some variations, at least two (e.g., no less than two) implants may be placed in each such lung. In some variations, at least three, four, five, or six (e.g., no less than three, or no less than four, or no less than five, or no less than six) implants may be placed in each such lung. In some variations, up to a total of eight, nine, or ten implants may be placed across both such lungs.

[0212] As shown in FIG. 42, a method of treatment 4200 may include determining a plurality of target airway segments in a first lung and a second lung of the patient 4210, wherein the patient is identified as having a pulmonary disease (e.g., emphysema, COPD), during a first procedure at a first time, placing an endobronchial reinforcement implant in each of at least a first portion of the target airway segments 4220, and during a second procedure at a second time, placing an endobronchial reinforcement implant in each of at least a second portion of the target airway segments 4230.

[11213] In some variations, the first procedure may include placing one or more implants in only one lung (e.g., first lung), and the second procedure may include placing one or more implants in the contralateral lung (e.g., second lung). In some variations, the first procedure may include placing one or more implants in both lungs (e.g., at least one implant is placed in each lung), and the second procedure may include placing at least one implant in one or both lungs.10214] In some variations, some or all of the target airway segments may be determined based at least in part on a baseline set of lung metrics characterizing lung state of the patient prior to the first procedure. In some variations, the target airway segments may be determined based at least in part on a follow-up set of lung metrics characterizing lung state of the patient after the first procedure and before the second procedure, such as based on the follow-up set of lung metrics alone and / or based on a comparison between the follow-up and baseline sets of lung metrics.[0215| The elapsed time between the first procedure and the second procedure may vary. For example, in some variations, the elapsed time between the first and second procedures may be one month, two months, three months, six months, twelve months, more than 6 months, or more than twelve months. Additionally or alternatively, the elapsed time between the first and second procedures may be based at least in part on the health of the patient and / or progression of the pulmonary disease. For example, the elapsed time may be shorter if the patient is faring poorly after the first procedure and / or is experiencing significant progression of the pulmonary7disease.VII. Platform-Agnostic Quantitative CT (OCT)[02 ! 6| As described in further detail herein, imaging data from CT scans may be analyzed by one or more software algorithms (e.g., machine learning algorithms) to obtain lung metrics and / or implant metrics. To generate lung metrics and / or implant metrics, methods and systems in accordance with the present technology can include one or more software algorithms (e.g., machine learning algorithm or other automated algorithm) to identify and characterize quantitative image features. In order for a quantitative image feature to effectively serve as a biomarker for disease diagnosis and / or assessment of implant therapy (e.g., placement of an endobronchial reinforcement implant), the quantitative image feature must be reproducible. However, scanning techniques, parameters, and other scanner algorithms (also referred to collectively herein as “CT scan parameters”) can have a large impact on the reproducibility of QCT outputs. Different CT scanners (e.g., different scanner manufacturers) may vary and any particular CT scanner may also be operated with various scan parameters, thereby affecting the results of QCT analysis. For example, the radiation dose applied during scanning, and / or the reconstruction algorithm used to generate tomographic images from acquired x-ray projection data, may affect the results of QCT analysis. These and further examples of CT scan parameters that can impact QCT analysis are listed below in Table 1.Table 1 - CT scan parameters[0217 [ In some embodiments, it may be advantageous to compensate for variance inQCT results due to CT scan parameters, such as by applying at least one correction factor. The correction factors can function to quantify the impact of differences in one or more CT scan parameters on resulting QCT analysis. Generally, such correction factors can be used to obtain a normalized (e.g., standardized) result to more effectively assess lung metrics and / or implant metrics, such as in a more objective manner that is CT platform-agnostic. For example, correction factors can be used to normalize the densify of voxels associated with a particularx-ray atenuation threshold (e.g., -950 HU, -910 HU, etc.) in CT data. A single correction factor can be associated with a single respective CT scan parameter, or can be associated with multiple CT scan parameters used during a CT scan in combination. Furthermore, compensation for variance in a particular CT scan can involve the use of a single correction factor, or can involve the use of multiple correction factors.

[0218] In some embodiments, the correction factors associated with one or more particular CT scan parameters may be determined empirically. For example, CT scans of a phantom (or multiple phantoms) of known density can be repeatedly acquired under different predetermined imaging conditions (e.g., sets of known CT scan parameters). The effect on voxel density (and / or QCT analysis results) caused by change in any one particular CT scan parameter can be empirically determined by repeatedly acquiring CT scans of the phantom, while modulating the CT scan parameter in a known manner across different CT scans. For example, to assess the effect on voxel density caused by change in tube voltage, the tube voltage can be incrementally adjusted by a known amount between a series of CT scans of the phantom. The relationship between the CT scan parameter (and / or changes thereof) and resulting voxel density can be empirically determined (e.g., described by a formula). Additional CT scan parameters may be similarly individually modulated in a known manner to determine the relationship between other CT scan parameters (and / or changes thereof) and resulting voxel density7.

[0219] FIG. 26 is a flow diagram illustrating a method 2600 of normalizing one or more QCT results, such as lung metrics and / or implant metrics, for a patient (e.g., a patient having or suspected of having a pulmonary disease). The method 2600 can be incorporated in other methods described herein, such as in the method 1900 (e.g.. to generate lung metrics and / or implant metrics of interest in a pre-procedure phase, a peri-procedure phase, and / or a postprocedure phase), in the method 2000 (e.g., to generate lung metrics of interest), method 2100 (e.g., to generate lung metrics of interest and / or implant metrics of interest) and / or method 2200. As shown in FIG. 26, the method 2600 can include receiving first CT data for a patient where the first CT data is obtained under predetermined imaging conditions (block 2610), transforming the first CT data to second CT data by applying at least one correction factor associated with the predetermined imaging conditions (block 2620), and generating metrics associated with the patient based on the second CT data (block 2630). Similar to that described above, the one or more correction factors applied to CT data can function to quantify the impact of the known imaging conditions such as CT scan parameters. Accordingly, in some variations,the metrics (e.g., lung metrics and / or implant metrics) generated in block 2630 can be more objective and CT platform-agnostic.[02201 Receiving first CT data for a patient in block 2610 functions to obtain initial image data for a patient, taken under imaging conditions that are predetermined and / or otherwise known. The CT data can include inspiratory CT data (e.g., obtained at the end of full inspiration) and / or expiratory CT data (e.g., obtained at the end of forced expiration). The CT data can be generated through a CT scan acquisition process in a pre-procedure phase, a periprocedure phase, and / or a post-procedure phase. The predetermined imaging conditions can include any one or more of the CT scan parameters listed in Table 1 (e.g., slice thickness, slice interval, tube potential, pitch, tube current, reconstruction algorithm, existence of any contrast agent administered to patient, contrast agent type / dosage, contrast agent administration modality, etc.). While these imaging conditions under which a scan is acquired are predetermined, each CT scan parameter may not be necessarily concretely known or identifiable. For example, some CT scan parameters (e.g., reconstruction algorithm and / or other algorithms used to determine fissure integrity') may be simply associated with a particular CT scanning machine design or CT vendor operating in a manner typical to that machine design or vendor. Other CT scan parameters (e.g., slice thickness, slice interval) may be both predetermined and known.| 221J Transforming the first CT data to second CT data in block 2620 functions to convert the first CT data to a normalized (e.g., standardized) dataset from which more objective metrics can be analyzed (e.g., in block 2630). For example, the second CT data can include voxel density that are agnostic to differences in CT scan parameters (e.g., scan acquisition parameters, dose modulation parameters, image reconstruction algorithms, contrast media administration, etc.) and / or other variations such as CT scanner model and / or particular CT vendor providing CT imaging services. One or more correction factors can be applied to the first CT data to obtain the second, more normalized CT data. As described above, a single correction factor can be associated with a single respective CT scan parameter, or can be associated with multiple CT scan parameters used during a CT scan in combination.[0222[ In some embodiments, a correction factor can be associated with a group of multiple related CT scan parameters. For example, a correction factor can be associated with some or all of the CT scan parameters relating to scan acquisition as listed in Table 1, or including some or all of the CT scan parameters relating to dose modulation as listed in Table 1, or including some or all of the CT scan parameters relating to image reconstruction as listedin Table 1, or including some or all of the CT scan parameters relating to contrast media as listed in Table 1). In some embodiments, a correction factor can be associated with multiple groups of related CT scan parameters. Additionally or alternatively, in some embodiments, a correction factor can be associated with a particular CT scanning machine design (e.g., scanner model) and / or a particular vendor providing CT imaging services.

[0223] Generating metrics associated with the patient based on the second CT data in block 2630 functions to generate metrics (e.g., lung metrics and / or implant metrics) for use in further analysis. In some embodiments, such metrics are generated from the second CT data using one or more software algorithms, such as one or more trained machine learning algorithms and / or other automated algorithm. For example, any of the suitable software algorithms described above with respect to methods 1900, 2000, 2100, and / or 2200 can be used to generate lung metrics and / or implant metrics from the second CT data (e.g., the first software algorithm with respect to method 2000, the fourth software algorithm with respect to method 2100, etc.).

[0224] The lung metrics can include any of the lung metrics described herein, such as those described with respect to methods 1900, 2000, 2100, and / or 2200. For example, the lung metrics can represent a state of the patient’s lung after the placement of one or more endobronchial implants (e.g., minimal endobronchial reinforcement implants), such as any of the following: FEV (e.g., FEVi), FVC, VC, IC, IC / TLC ratio, functional residual capacity, TLC, diffusion capacity for carbon monoxide, RV, RV / TLC ratio, CV, lobar and / or segmental tissue destruction, lobar and / or segmental air trapping, lobar and / or segmental fissure status, extent of lobar and / or segmental fissure completion, lobar and / or segmental ventilation, lung function, disease phenotype (e.g., homogeneity / heterogeneity of emphysema, type of emphysema (such as centriacinar emphysema, panacinar emphysema, or paraseptal emphysema), location of diseased portions of the lung), lobar volume, segmental volume, segment locations, diaphragm shape, tissue density, opacity, proximity of diseased portions to anatomical structures, proximity’ of disease portions to other medical devices, lumen inner diameter of bronchial sections, air flow mapping, collapsed airways, airway pressure, one or more mechanical properties of an airway (e.g., airway compliance, airway resistance, airway elastance, etc.), pleural pressure, airway diameter to wall thickness, airway wall deformation, vascular perfusion, parenchyma density, bullae, information that localizes disease in the lung, obstruction score, mucus score, degree of epithelialization, other implant-tissue interactions (e.g., granulation tissue, implant-induced airway deformation, airway tissue invagination intoa lumen of the implant, etc.). At least some of these (e.g., airway resistance, airway compliance, airway elastance, etc.) may be measured at any of various levels of the lung, such as a segmental level of the lung, other regional level of the lung, lobar level of the lung, and / or a lung level of the lung. The set of lung metrics can characterize the state of the lung at a single time point, and / or can characterize a change in the state of the lung over plurality of time points (e.g., before and after endobronchial implant therapy, before and after administration of a bronchodilator, before and during exercise). Additionally or alternatively, the lung metrics can be used to generate a disease '‘score” characterizing severity of pulmonary disease that may remain in one or more regions of the lung following treatment. The lung metrics can include one or more disease scores, where each disease score characterizes severity of pulmonary disease in a respective region of the lung (e.g., a particular lung, a particular lobe, a particular segment, a particular sub-segment).

[0225] The implant metrics can include any of the implant metrics described herein, such as those described with respect to methods 1900, 2000, 2100, and / or 2200. For example, the implant metrics can represent a state of each endobronchial implant after placement in the lung, such as any of the following characteristics: implant location, distance between a distal end of the implant and pleura, implant length, implant diameter at any one or more locations along the implant length, implant cross-sectional profile at any one or more locations along the implant length, implant integrity (e.g., implant breakage, deformation), indication of implant expansion and / or collapse (e.g., biasing of the implant in a longitudinal direction, or “pancaking”) such as pitch of implant loops or angle of implant loop profile relative to a longitudinal axis of the implant), implant position relative to other placed implant(s), movement of one or more implants between inspiration and expiration, occlusion of implant, invagination, and / or implant dislodgement. The implant metrics can represent the state of the implant at a single time point, or can represent a change in the state of the implant over a plurality of time points.

[0226] In some embodiments, various methods in accordance with the present technology can further include analyzing the generated metrics associated with the patient based on the second CT data. Such analysis can be performed with one or more software algorithms, such as a trained machine learning algorithm or other automated algorithm. For example, lung metrics based on the second CT data can be analyzed in a patient selection process, a procedure planning process, an outcome assessment process, and / or an intervention recommendation process. As another example, implant metrics based on the second CT datacan be analyzed in an outcome assessment process and / or an intervention recommendation process.[02271 In some embodiments, any of the suitable software algorithms described above with respect to methods 1900, 2000, 2100. and / or 2200 can be used to analyze the metrics generated based on the second CT data. For example, lung metrics based on the second CT data can be analyzed using the second software algorithm described with respect to method 2000 to determine whether a patient is a candidate for treatment (e g., likelihood of treatment success), and / or using the third software algorithm described with respect to method 2000 to predict an optimized treatment plan (e.g., optimized implant placement). Additionally or alternatively, lung metrics and / or implant metrics based on the second CT data can be analyzed using the fifth software algorithm with respect to method 2100 to quantify patient benefit following implant placement, and / or using the sixth software algorithm to predict future outcomes and / or suggest interventions following implant placement..Conclusion[0228| Although many of the embodiments are described above with respect to systems, devices, and methods for treating COPD and emphysema, the technology is applicable to other applications and / or other approaches, such as identifying and / or treating tracheobronchomalacia (TBM), excessive dynamic airway collapse (EDAC), or benign prostatic hyperplasia (BPH). Moreover, other embodiments in addition to those described herein are within the scope of the technology. Additionally, several other embodiments of the technology can have different configurations, components, or procedures than those described herein. A person of ordinary skill in the art, therefore, will accordingly understand that the technology can have other embodiments with additional elements, or the technology can have other embodiments without several of the features shown and described above with reference to FIGS. 1-28.|0229| The various processes described herein can be partially or fully implemented using program code including instructions executable by one or more processors of a computing system for implementing specific logical functions or steps in the process. The program code can be stored on any type of computer-readable medium, such as a storage device including a disk or hard drive. Computer-readable media containing code, or portions of code, can include any appropriate media known in the art, such as non-transitory computer-readable storage media. Computer-readable media can include volatile and non-volatile, removable andnon-removable media implemented in any method or technology for storage and / or transmission of information, including, but not limited to, random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other memory technology; compact disc read-only memory (CD-ROM), digital video disc (DVD), or other optical storage; magnetic cassettes, magnetic tape, magnetic disk storage, or other magnetic storage devices; solid state drives (SSD) or other solid state storage devices; or any other medium which can be used to store the desired information and which can be accessed by a system device.

[0230] The descriptions of embodiments of the technology are not intended to be exhaustive or to limit the technology to the precise form disclosed above. Where the context permits, singular or plural terms may also include the plural or singular term, respectively. Although specific embodiments of, and examples for, the technology’ are described above for illustrative purposes, various equivalent modifications are possible within the scope of the technology^, as those skilled in the relevant art will recognize. For example, while steps are presented in a given order, alternative embodiments may perform steps in a different order. The various embodiments described herein may also be combined to provide further embodiments.[0231 j As used herein, the terms "generally." "substantially." "about." and similar terms are used as terms of approximation and not as terms of degree, and are intended to account for the inherent variations in measured or calculated values that would be recognized by those of ordinary skill in the art.

[0232] Moreover, unless the word “of’ is expressly limited to mean only a single item exclusive from the other items in reference to a list of two or more items, then the use of “or” in such a list is to be interpreted as including (a) any single item in the list, (b) all of the items in the list, or (c) any combination of the items in the list. As used herein, the phrase “and / or” as in “A and / or B” refers to A alone, B alone, and A and B. Additionally, the term “comprising” is used throughout to mean including at least the recited feature(s) such that any greater number of the same feature and / or additional types of other features are not precluded.

[0233] To the extent any materials incorporated herein by reference conflict with the present disclosure, the present disclosure controls.

[0234] It will also be appreciated that specific embodiments have been described herein for purposes of illustration, but that various modifications may be made without deviating fromthe technology. Further, while advantages associated with certain embodiments of the technology have been described in the context of those embodiments, other embodiments may also exhibit such advantages, and not all embodiments need necessarily exhibit such advantages to fall within the scope of the technology. Accordingly, the disclosure and associated technology can encompass other embodiments not expressly shown or described herein.

Claims

CLAIMSI / We claim:

1. A method for planning a treatment for a patient having a pulmonary disease, the method comprising: receiving patient data including computed tomography (CT) data of a lung of the patient; generating a set of one or more lung metrics by inputting the patient data into a patient characterization machine learning algorithm, wherein the set of one or more lung metrics represents a state of the lung of the patient; and mapping a potential pathway, identifying a target airway segment, or both, in the lung of the patient for placing an implant at a target location in the lung, by inputting one or more of the patient data or the set of one or more lung metrics into a treatment planning machine learning algorithm.

2. The method of claim 1. wherein mapping the potential pathway in the lung comprises identifying a pathway from segmental bronchi in the lung to a pleural surface of the lung.

3. The method of claim 1 or 2, wherein the treatment planning machine algorithm is configured to maximize access of the implant to a region of the lung having trapped air.

4. The method of any one of claims 1-3, wherein the treatment planning machine algorithm is configured to maximize access to emphysematous tissue at a periphery of the lung.

5. The method of any one of claims 1-4, wherein the treatment planning machine algorithm is configured to minimize tortuosity of the mapped pathway.

6. The method of any one of claims 1-5, wherein the treatment planning machine algorithm is configured to minimize risk of damage to blood vessels in the lung during deliver}' and placement of the implant.

7. The method of any one of claims 1-6, wherein the CT data comprises expiratory CT data.

8. The method of any one of claims 1-7, wherein the CT data comprises inspiratory CT data.

9. The method of any one of claims 1-8, wherein the set of lung metrics characterizes one or more lung parameters, the one or more lung parameters comprising one or more of the following: forced expiratory volume in 1 second, forced vital capacity, vital capacity, inspiratory capacity, inspiratory capacity / total lung capacity ratio, functional residual capacity, total lung capacity', diffusion capacity' for carbon monoxide, residual volume, residual volume / total lung capacity ratio, closing volume, lobar and / or segmental tissue destruction, lobar and / or segmental air trapping, lobar and / or segmental fissure status, extent of lobar and / or segmental fissure completion, lobar and / or segmental ventilation, lung function, homogeneity / heterogeneity of lobar and / or segmental emphysema, emphysema ty pe, locations of diseased portions of the lung, lobar volume, segmental volume, segmental locations, diaphragm shape, tissue density, opacity, proximity of diseased portions to anatomical structures, proximity of diseased portions to other medical devices, lumen inner diameter of bronchial sections, air flow mapping, collapsed airways, airway pressure, airway compliance, airway resistance, airway elastance, pleural pressure, airway diameter to wall thickness, airway wall deformation, vascular perfusion, parenchyma density, bullae, information that localizes disease in the lung, obstruction score, mucus score, or degree of epithelialization.

10. The method of claim 9, wherein the set of lung metrics characterizes one or more of airway resistance, airway compliance, airway elastance, or any combination thereof, at a segmental level of the lung, a lobar level of the lung, a lung level of the lung, or other regional level of the lung.

11. The method of any one of claims 1-10, wherein the set of lung metrics comprises at least one disease score characterizing severity of pulmonary disease in the patient.

12. The method of claim 11, wherein the at least one disease score comprises a first disease score corresponding to severity of emphysema and a second disease score corresponding to anon-emphysema pulmonary disease.

13. The method of claim 12, wherein the non-emphysema pulmonary disease is one of small airways disease, bronchitis, asthma, pulmonary’ fibrosis, type 2 inflammation, or neutrophilic inflammation.

14. The method of claim 12 or 13, wherein the treatment planning machine learning algorithm is configured to map a potential pathway, identify a target airway segment, or both, based at least in part on the first disease score.

15. The method of any one of claims 12-14, wherein the treatment planning machine learning algorithm is configured to map a potential pathway, identify a target airway segment, or both based at least in part on the second disease score, where the second disease score is utilized to exclude one or more pathways as a potential pathway, exclude an airway segment as a target airway segment, or both.

16. The method of any one of claims 12-15, further comprising identifying one or more pharmacological treatment options for treatment of the pulmonary disease, by inputting one or more of the patient data or the set of one or more lung metrics into the treatment planning machine learning algorithm.

17. The method of claim 16, wherein the treatment planning machine learning algorithm is configured such that in response to the first disease score satisfy ing a first predetermined threshold and the second disease score satisfying a second predetermined threshold, the treatment planning machine learning algorithm provides an output indicating that both at least one pharmacological treatment and placement of an implant are warranted for treatment of the pulmonary' disease.

18. The method of any one of claims 1-17, further comprising identifying the target location based at least in part on one of more of the following: location of dynamic airway collapse as determined from expiratory' CT data, severity' of disease in a peripheralregion of the lung, location of a pleural wall of the patient, or location of lobar, segmental, and / or sub-segmental airways.

19. The method of any one of claims 1-18, wherein the implant is a minimal endobronchial reinforcement implant.

20. A system comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of claims 1-19.

21. A computed tomography (CT) scanner comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the CT scanner to perform operations comprising the method of any one of claims 1-19.

22. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of claims 1-19.

23. A method for evaluating a patient having a pulmonary disease, the method comprising: receiving patient data including computed tomography (CT) data of a lung of the patient; and generating an identification of at least one type of pulmonary disease causing air trapping or obstruction in the lung of the patient, by inputting the patient data into a patient characterization machine learning algorithm.

24. The method of claim 23, wherein generating the identification of at least one type of pulmonary disease comprises identifying that air trapping or obstruction in the lung of the patient is caused by emphysema.

25. The method of claim 23 or 24, wherein generating the identification of at least one type of pulmonary disease comprises identifying that air trapping or obstruction in the lung of the patient is caused by small airway disease.

26. The method of any one of claims 23-25, wherein generating the identification of at least one type of pulmonary disease comprises identifying that obstruction in the lung of the patient is caused by chronic bronchitis.

27. The method of any one of claims 23-26, wherein generating the identification of at least one type of pulmonary disease comprises identifying that obstruction in the lung of the patient is caused by asthma.

28. The method of any one of claims 23-27, wherein generating the identification of at least one type of pulmonary disease comprises identifying that obstruction in the lung of the patient is caused by pulmonary fibrosis.

29. The method of any one of claims 23-28, further comprising generating at least one disease score characterizing severity of pulmonary disease in the patient.

30. The method of any one of claims 23-29, wherein the at least one disease score comprises a first disease score quantifying an amount of air trapping or obstruction in the lung of the patient caused by a first pulmonary disease.

31. The method of claim 30, wherein the first disease score comprises a percentage of an obstructed airway region that is exhibiting at least one of air trapping or obstruction in the lung of the patient and is caused by the first pulmonary disease.

32. The method of claim 30 or 31, wherein the at least one disease score comprises a second disease score quantifying an amount of at least one of air trapping or obstruction in the lung of the patient caused by a second pulmonary’ disease.

33. The method of any one of claims 23-32. wherein the CT data comprises expiratory CT data.

34. The method of any one of claims 23-33, wherein the CT data comprises inspiratory CT data.

35. The method of any one of claims 23-34, further comprising generating a characterization of one or more lung parameters, the one or more lung parameters comprising one or more of the following: forced expiratory volume in 1 second, forced vital capacity, vital capacity, inspiratory capacity, inspirator}' capacity / total lung capacity ratio, functional residual capacity, total lung capacity', diffusion capacity' for carbon monoxide, residual volume, residual volume / total lung capacity ratio, closing volume, lobar and / or segmental tissue destruction, lobar and / or segmental air trapping, lobar and / or segmental fissure status, extent of lobar and / or segmental fissure completion, lobar and / or segmental ventilation, lung function, homogeneity / heterogeneity of lobar and / or segmental emphysema, emphysema ty pe, locations of diseased portions of the lung, lobar volume, segmental volume, segmental locations, diaphragm shape, tissue density, opacity, proximity of diseased portions to anatomical structures, proximity of diseased portions to other medical devices, lumen inner diameter of bronchial sections, air flow mapping, collapsed airways, airway pressure, airway compliance, airway resistance, airway elastance, pleural pressure, airway diameter to wall thickness, airway wall deformation, vascular perfusion, parenchyma density, bullae, information that localizes disease in the lung, obstruction score, mucus score, or degree of epithelialization.

36. A system comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of claims 23-35.

37. A computed tomography (CT) scanner comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the CT scanner to perform operations comprising the method of any one of claims 23-35.

38. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of claims 23-35.

39. A method for evaluating a patient, the method comprising: receiving patient data including computed tomography (CT) data of a lung of the patient; generating a set of one or more lung metrics by inputting the patient data into a patient characterization machine learning algorithm, wherein the set of one or more lung metrics characterizes a shape of an airway of the lung of the patient at one or more timepoints within a respiratory cycle, including at least one of maximal inspiration, maximal expiration, tidal volume inspiration, or tidal volume expiration.

40. The method of claim 39, wherein the set of one or more lung metrics characterizes a shape of the airw ay at each of maximal inspiration, maximal expiration, tidal volume inspiration, and tidal volume expiration.

41. The method of claim 39 or 40, wherein the CT data comprises at least one of expiratory CT data or inspiratory CT data.

42. The method of 41, wherein the patient characterization machine learning algorithm is configured to utilize voxel density thresholding to identify the airway of the lung in the expiratory CT data or the inspiratory CT data.

43. The method of any one of claims 39-42, wherein the method further comprises estimating a relative change in lung volume betw een maximal inspiration and tidal volume inspiration.

44. The method of any one of claims 39-43, wherein the method further comprises estimating a relative change in lung volume betw een maximal expiration and tidal volume expiration.

45. The method of any one of claims 39-44, wherein the set of one or more lung metrics characterizes at least one of diameter, length, radius of curvature, or tortuosity of the airway of the lung.

46. The method of any one of claims 39-45, wherein the set of lung metrics characterizes one or more lung parameters, the one or more lung parameters comprising one or more of the following: forced expiratory volume in 1 second, forced vital capacity, vital capacity, inspiratory capacity, inspiratory capacity / lolal lung capacity ratio, functional residual capacity , total lung capacity , diffusion capacity for carbon monoxide, residual volume, residual volume / total lung capacity ratio, closing volume, lobar and / or segmental tissue destruction, lobar and / or segmental air trapping, lobar and / or segmental fissure status, extent of lobar and / or segmental fissure completion, lobar and / or segmental ventilation, lung function, homogeneity / heterogeneity of lobar and / or segmental emphysema, emphysema type, locations of diseased portions of the lung, lobar volume, segmental volume, segmental locations, diaphragm shape, tissue density, opacity, proximity of diseased portions to anatomical structures, proximity of diseased portions to other medical devices, lumen inner diameter of bronchial sections, air flow mapping, collapsed airways, airway pressure, airway compliance, airway resistance, airway elastance, pleural pressure, airway diameter to wall thickness, airway wall deformation, vascular perfusion, parenchyma density, bullae, information that localizes disease in the lung, obstruction score, mucus score, or degree of epithelialization.

47. The method of any one of claims 39-46, wherein the set of one or more lung metrics is a first set of one or more lung metrics characterizing a shape of an airway of the lung of the patient prior to placement of an endobronchial implant in the lung, wherein the method further comprises: generating a second set of one or more lung metrics that represent a predicted state of the lung after placement of the endobronchial implant in the lung, by inputting at least one of the first set of one or more lung metrics or patient data into the patient characterization machine learning algorithm; and generating a set of one or more implant metrics by inputting the second set of one or more lung metrics into an implant characterization machine learning algorithm, wherein the set of one or more implant metrics represent a predicted state of the endobronchial implantafter placement in the lung.

48. The method of claim 47, wherein the second set of one or more lung metrics is based at least in part on one or more of: at least a portion of the first set of one or more lung metrics, a number of one or more endobronchial implants placed in the lung, or placement location of each of one or more endobronchial implants placed in the lung.

49. A system comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of claims 39-48.

50. A computed tomography (CT) scanner comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the CT scanner to perform operations comprising the method of any one of claims 39-48.

51. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of claims 39-48.

52. A method for evaluating a patient having an endobronchial implant placed in a lung of the patient, the method comprising: receiving patient data including computed tomography (CT) data of the lung of the patient; generating a set of one or more implant metrics by inputting the patient data into an implant characterization machine learning algorithm, wherein the set of one or more implant metrics represent a state of the endobronchial implant after placement in the lung, wherein the set of one or more implant metrics characterizes a shape of the implant at one or more timepoints within a respiratory cycle, including at least one of maximal inspiration, maximal expiration, tidal volume inspiration, or tidalvolume expiration.

53. The method of claim 52, wherein the set of one or more implant metrics characterizes a shape of the implant at each of maximal inspiration, maximal expiration, tidal volume inspiration, and tidal volume expiration.

54. The method of claim 52 or 53, wherein the CT data comprises at least one of expiratory CT data or inspiratory CT data.

55. The method of claim 54, wherein the implant characterization machine learning algorithm is configured to utilize voxel density thresholding to identify the implant in the expiratory CT data or the inspiratory CT data.

56. The method of any one of claims 52-55, wherein the set of one or more implant metrics characterizes one or more of the following: implant location, distance between a distal end of the implant and pleura, implant length, implant diameter at any one or more locations along a length of the implant, implant cross-sectional profile at any one or more locations along a length of the implant, implant integrity, pitch of loops of an implant, angle of an implant loop profile relative to a longitudinal axis of the implant, implant position relative to one or more additional implants, movement of the implant between inspiration and expiration, occlusion of the implant, or implant dislodgment.

57. The method of any one of claims 52-56, wherein the endobronchial implant comprises a minimal endobronchial reinforcement implant.

58. A system comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of claims 52-57.

59. A computed tomography (CT) scanner comprising: a processor; and a memory' operably coupled to the processor and storing instructions that, whenexecuted by the processor, cause the CT scanner to perform operations comprising the method of any one of claims 52-57.

60. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of claims 52-57.

61. A method for evaluating a patient having a pulmonary disease, the method comprising: receiving patient data including computed tomography (CT) data of a lung of the patient; and generating at least one disease score for the patient by inputting the patient data into a patient characterization machine learning algorithm, wherein the at least one disease score characterizes severity of dynamic hyperinflation in the lung of the patient.

62. The method of claim 61, wherein the disease score is generated for the patient without the patient performing a cardiopulmonary exercise test.

63. The method of claim 61 or 62. wherein the CT data comprises inspiratory CT data.

64. The method of any one of claims 61-63, wherein the CT data comprises expiratory CT data.

65. A system comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of claims 61-64.

66. A computed tomography (CT) scanner comprising: a processor; anda memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the CT scanner to perform operations comprising the method of any one of claims 61-64.

67. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of claims 61-64.

68. A method for evaluating a patient, the patient comprising: receiving patient data including computed tomography (CT) data of a lung of the patient; generating a set of one or more initial lung metrics for the patient based on the patient data; generating a set of one or more corrected lung metrics corrected for variability in breathing effort, by applying one or more correction factors generated by a correction machine learning algorithm.

69. The method of claim 68, wherein generating the set of one or more initial lung metrics comprises inputting the patient data into a patient characterization machine learning algorithm.

70. The method of claim 68 or 69, wherein the set of one or more corrected lung metrics comprises at least one of total lung capacity or residual volume.

71. The method of any one of claims 68-70, wherein the one or more correction factors represent a difference in expiratory patient data due to variability in breathing effort by a patient in different body postures.

72. The method of claim 71, wherein the one or more correction factors represent a difference in expiratory patient data due to variability in breathing effort by a patient during a pulmonary function test compared to during an expiratory CT scan.

73. The method of any one of claims 68-72, wherein the CT data comprises inspiratory CT data.

74. The method of any one of claims 68-73. wherein the method for evaluating the patient does not include receiving expiratory CT data from an expiratory CT scan.

75. The method of any one of claims 68-74, further comprising generating corrected expiratory CT data that corrects for variability in breathing effort, by inputting the set of one or more corrected lung metrics into a transformation machine learning algorithm.

76. The method of claim 75, wherein generating corrected expiratory CT data comprises generating a corrected expiratory CT scan that corrects for variability in breathing effort.

77. A system comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of claims 68-76.

78. A computed tomography (CT) scanner comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the CT scanner to perform operations comprising the method of any one of claims 68-76.

79. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of claims 68-76.

80. A method for planning a treatment for a patient having a pulmonary disease, the method comprising: receiving patient data including computed tomography (CT) data of a lung of the patient; determining a ventilation / perfusion (V / Q) profile of the lung based on the patient data; andidentifying a target location for placing an endobronchial implant in the lung of the patient by inputting the V / Q profile into a treatment planning machine learning algorithm.

81. The method of claim 80, wherein the identified target location corresponds to a region of the lung exhibiting a threshold level of perfusion.

82. The method of claim 80 or 81, wherein the treatment planning machine learning algorithm is configured to identify a target location for maximizing efficacy of the endobronchial implant.

83. The method of any one of claims 80-82, wherein the CT data comprises ex pi rat ory CT data.

84. The method of any one of claims 80-83, wherein the CT data comprises inspiratory CT data.

85. The method of any one of claims 80-84, wherein the endobronchial implant is a minimal endobronchial reinforcement implant.

86. A system comprising: a processor; and a memory operably coupled to the processor and storing instructions that, w hen executed by the processor, cause the computing system to perform operations comprising the method of any one of claims 80-85.

87. A computed tomography (CT) scanner comprising: a processor; and a memoiy operably coupled to the processor and storing instructions that, when executed by the processor, cause the CT scanner to perform operations comprising the method of any one of claims 80-85.

88. A non-transitory computer-readable storage medium comprising instructionsthat, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of claims 80-85.

89. A method comprising: receiving a set of lung metrics characterizing lung state of a patient, the set of lung metrics comprising one or more of: at least one metric representative of amount of emphysema destruction in a first lung, at least one metric representative of amount of emphysema destruction in a second lung, or residual volume (RV), total lung capacity (TLC); and determining the patient is a candidate for treatment with an endobronchial reinforcement implant, in response to determining, based on the set of lung metrics:(iii) the patient has at least one lung exhibiting heterogeneous emphysema; and(iv) the patient has a baseline RV / TLC metric that is equal to or above a first threshold, or a baseline RV that is equal to or above a second threshold, or both.

90. The method of claim 89, wherein determining the patient as a candidate comprises identifying the patient as a candidate in response to determining, based on the set of lung metrics, the patient has two lungs exhibiting heterogeneous emphysema.91 . The method of claim 89 or 90, wherein the first threshold is at least 0.55.

92. The method of any one of claims 89-91, wherein the first threshold is at least 0.65.

93. The method of any one of claims 90-92, wherein the second threshold is between about 150% and about 180%.

94. The method of any one of claims 90-93. wherein the second threshold is about 180%.

95. The method of any one of claims 90-94, wherein the set of lung metrics further comprises an emphysema score representative of percentage of emphysema destruction in each of at least one of the first or second lungs, and wherein identifying thepatient as a candidate comprises identifying the patient as a candidate at least in part in response to determining that the emphysema score is above at least 25%.

96. The method of claim 89, wherein determining the patient as a candidate comprises identifying the patient as a candidate in response to determining, based on the set of lung metrics, the patient has a single lung exhibiting heterogeneous emphysema and a single lung exhibiting homogeneous emphysema and the second threshold is between about 175% and about 225%.

97. The method of claim 96, wherein the second threshold is about 200%.

98. The method of any one of claims 89-97, wherein the patient is considered to have at least one lung exhibiting heterogeneous emphysema if the at least one lung has an upper lobe exhibiting a first level of emphysema destruction and a lower lobe exhibiting a second level of emphysema destruction, the first and second levels of emphysema destruction differing by at least 15%.

99. The method of any one of claims 89-98, wherein the set of lung metrics further comprises at least one additional lung metric characterizing one or more of: mucus plugging, suspected pulmonary hypertension, dynamic hyperinflation, bronchial wall thickening, fibrosis, scarring, extent of disease in small airways, or extent of disease in large airways in the patient.

100. The method of any one of claims 89-99, wherein the set of lung metrics is derived from one or more of computed tomography (CT) data, plethysmography, or spirometry.

101. The method of claim 99 or 100, further comprising determining the patient is or is not a candidate for treatment with the endobronchial reinforcement implant, based on any one or more of the at least one additional lung metrics.

102. The method of any one of claims 89-101, further comprising, in response to determining the patient is a candidate for treatment with an endobronchial reinforcementimplant, placing at least one endobronchial reinforcement implant in at least one lung of the patient.

103. The method of claim 102, wherein the endobronchial reinforcement implant comprises a minimal endobronchial reinforcement implant.

104. A method comprising: receiving a set of lung metrics characterizing lung state of a patient, the set of lung metrics comprising an emphysema score representative of percentage of emphysema destruction in lungs of the patient; and determining the patient is a candidate for treatment with an endobronchial reinforcement implant, in response to determining that the emphysema score is above at least 25%.

105. The method of claim 104, wherein the set of lung metrics further comprises at least one metric representative of amount of emphysema destruction in a first lung, and at least one metric representative of amount of emphysema destruction in a second lung, and wherein identifying the patient as a candidate comprises identifying the patient as a candidate in response to determining, based on the set of lung metrics, the patient has two lungs exhibiting heterogeneous emphysema.

106. The method of claim 104 or 105, wherein the patient is considered to have at least one lung exhibiting heterogeneous emphysema if the at least one lung has an upper lobe exhibiting a first level of emphysema destruction and a lower lobe exhibiting a second level of emphysema destruction, the first and second levels of emphysema destruction differing by at least 15%.

107. The method of any one of claims 104 -106, wherein the set of lung metrics further comprises at least one additional lung metric characterizing one or more of: mucus plugging, suspected pulmonary hypertension, dynamic hyperinflation, bronchial wall thickening, fibrosis, scarring, extent of disease in small airw ays, extent of disease in large airways in the patient, airway resistance, airway compliance, or airway elastance.

108. The method of claim 107, wherein the set of additional lung metrics characterizes one or more of airway resistance, airway compliance, airway elastance, or any combination thereof, at a segmental level of the lung, a lobar level of the lung, a lung level of the lung, or other regional level of the lung.

109. The method of claim 107 or 108. further comprising determining the patient is or is not a candidate for treatment with the endobronchial reinforcement implant, based on any one or more of the at least one additional lung metrics.

110. The method of any one of claims 104 -109, further comprising, in response to identifying that the patient is a candidate for treatment with an endobronchial reinforcement implant, placing at least one endobronchial reinforcement implant in at least one lung of the patient.

111. The method of claim 110, wherein the endobronchial reinforcement implant comprises a minimal endobronchial reinforcement implant.

112. A method for treating a patient, comprising: receiving computed tomography (CT) data of a lung of the patient; identifying, based on the CT data, one or more candidate airway segments in the lung, wherein each candidate airway segment has an emphysema score of at least 20%, the emphysema score being representative of a percentage of emphysema destruction in the candidate airway segment; calculating a volume of emphysema destruction in each candidate airway segment based on a respective volume metric of the candidate airway segment and the respective emphysema score for the candidate airway segment; determining one or more target airway segments in the lung based on the calculated volumes of emphysema destruction; and placing an endobronchial reinforcement implant in each of the one or more target airway segments.

113. The method of claim 112, wherein the CT data comprises at least one of inspiratory or expiratory CT data.

114. The method of claim 112 or 113, wherein each candidate airway segment has a segmental diameter of between about 3mm and about 9 mm.

115. The method of any one of claims 112-114, wherein the volume metric comprises an air volume of the candidate airway segment.

116. The method of any one of claims 112—115, wherein the volume metric comprises a tissue volume of the candidate airway segment.

117. The method any one of claims 112-116, wherein calculating a volume of emphysema destruction comprises adding and / or multiplying the volume metric and the emphysema score.

118. The method of any one of claims 112-117, wherein calculating a volume of emphysema destruction comprises applying a weight factor to the volume metric, the emphysema score, or both.

119. The method of claim 118, wherein applying a weight factor comprises applying a first w eight factor to the volume metric and applying a second weight factor to the emphysema score, wherein the first and second weight factors are different.

120. The method of any one of claims 112-119, wherein determining one or more target airway segments comprises identifying, as a target airway segment, a candidate airway segment having the largest calculated volume of emphysema destruction out of the one or more candidate airway segments.

121. The method of claim 120, wherein determining one or more target airways comprises identifying, as two target airway segments, two candidate airway segments having the largest tw o calculated volumes of emphysema destruction out of the candidate airw ay segments.

122. The method of claim 121, wherein determining one or more target airways comprises identifying, as two target airway segments, three candidate airway segmentshaving the largest three calculated volumes of emphysema destruction out of the candidate airway segments.

123. The method of any one of claims 112-122, further comprising generating, from the CT data using a machine learning algorithm, a set of lung metrics characterizing one or more of airway resistance, airway compliance, airway elastance, or any combination thereof, at a segmental level of the lung, a lobar level of the lung, a lung level of the lung, or other regional level of the lung.

124. The method of claim 123, wherein determining one or more target airway segments is further based on the set of lung metrics.

125. The method of any one of claims 112-124, wherein placing an endobronchial reinforcement implant in each of the one or more target airw ay segments comprises placing one, two. or three endobronchial reinforcement implants in the lung.

126. The method of claim 125, wherein the lung is a first lung of the patient, the method further comprising: receiving computed tomography (CT) data of a second lung of the patient; identifying, based on the CT data, one or more candidate airway segments in the second lung, wherein each candidate airway segment has an emphysema score of at least 20%, the emphysema score being representative of a percentage of emphysema destruction in the candidate airway segment; calculating a volume of emphysema destruction in each candidate airway segment based on a respective volume metric of the candidate airway segment and the respective emphysema score for the candidate airway segment; determining one or more target airway segments in the second lung based on the calculated volumes of emphysema destruction; and placing an endobronchial reinforcement implant in each of the one or more target airway segments in the second lung.

127. The method of claim 126, comprising placing up to three endobronchial reinforcement implants in the first lung and placing up to three endobronchial reinforcement implants in the second lung.

128. The method of any one of claims 112-127, wherein the endobronchial reinforcement implant comprises a minimal endobronchial reinforcement implant.

129. A system comprising: a processor; and a memory operably coupled to the processor and storing instructions that, when executed by the processor, cause the computing system to perform operations comprising the method of any one of claims 89-128.

130. A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising the method of any one of claims 89-128.

131. A method for treating a patient, the method comprising: determining one or more target airway segments in at least one lung of the patient, wherein the patient is identified as having emphysema; and placing an endobronchial reinforcement implant in each of the one or more target airway segments.

132. The method of claim 131 , wherein the patient has at least one lung exhibiting heterogeneous emphysema.

133. The method of claim 132, wherein placing an endobronchial reinforcement implant comprises placing only a single endobronchial reinforcement implant in the at least one lung exhibiting heterogeneous emphysema.

134. The method of claim 132, wherein placing an endobronchial reinforcement implant comprises placing only two endobronchial reinforcement implants in the at least one lung exhibiting heterogeneous emphysema.

135. The method of claim 132, wherein placing an endobronchial reinforcement implant comprises placing only up to three endobronchial reinforcement implants in the at least one lung exhibiting heterogeneous emphysema.

136. The method of claim 131, wherein the patient has at least one lung exhibiting homogeneous emphysema.

137. The method of claim 136, wherein placing an endobronchial reinforcement implant comprises placing at least one endobronchial reinforcement implant in each lobe of the lung exhibiting homogeneous emphysema.

138. The method of claim 136, wherein placing an endobronchial reinforcement implant comprises placing at least one endobronchial reinforcement implant in each of an upper lobe and a lower lobe of the at least one lung exhibiting homogeneous emphysema.

139. The method of claim 136, wherein placing an endobronchial reinforcement implant comprises placing at least three implants in the at least one lung exhibiting homogeneous emphysema.

140. The method of claim 139, wherein placing an endobronchial reinforcement implant comprises placing more than three implants in the at least one lung exhibiting homogeneous emphysema.

141. The method of any one of claims 136-140, wherein placing an endobronchial reinforcement implant comprises placing up to a total of five implants in the at least one lung exhibiting homogeneous emphysema.

142. The method of any one of claims 136-140, wherein placing an endobronchial reinforcement implant comprises placing up to a total of six implants in the at least one lung exhibiting homogeneous emphysema.

143. The method of claim 131, wherein the patient has unilateral homogeneous emphysema, with a first lung exhibiting homogeneous emphysema and a second lung exhibiting heterogeneous emphysema.

144. The method of claim 143, wherein placing an endobronchial reinforcement implant comprises placing a first number of endobronchial reinforcement implants in the firstlung exhibiting homogeneous emphysema and placing a second number of endobronchial reinforcement implants in the second lung exhibiting heterogeneous emphysema, wherein the second number is greater than the first number.

145. The method of claim 131, wherein the patient has bilateral homogeneous emphysema.

146. The method of claim 145, wherein placing an endobronchial reinforcement implant comprises placing at least two endobronchial reinforcement implants in a first lung of the patient, and placing at least two endobronchial reinforcement implants in a second lung of the patient.

147. The method of claim 145 or 146, wherein the method comprises placing a total of at least six endobronchial reinforcement implants in the patient.

148. The method of claim 147, wherein the method comprises placing a total of at least eight endobronchial reinforcement implants in the patient.

149. The method of claim 148, wherein the method comprises placing a total of at least ten endobronchial reinforcement implants in the patient.

150. The method of any one of claims 131-149, wherein the endobronchial reinforcement implant is a minimal endobronchial reinforcement implant.

151. A method for treating a patient, comprising: determining a plurality of target airway segments in a first lung and a second lung of the patient, wherein the patient is identified as having a pulmonary disease; during a first procedure at a first time, placing an endobronchial reinforcement implant in each of at least a first portion of the target airway segments; and during a second procedure at a second time, placing an endobronchial reinforcement implant in each of at least a second portion of the target airway segments.

152. The method of claim 151, wherein the first portion of the target airwaysegments is in the first lung of the patient, and the second portion of the target airwaysegments is in the second lung.

153. The method of claim 151, wherein the first portion of the target airway segments comprises at least one airway segment in the first lung and at least one airway segment in the second lung, and wherein the second portion of the target airway segments comprises at least one airway segment in the first lung or at least one airway segment in the second lung.

154. The method of any one of claims 151-153, further comprising receiving a follow-up set of lung metrics characterizing lung state of the patient between the first time and the second time.

155. The method of claim 154, wherein determining a plurality of target airway segments comprises: determining the first portion of the target airway segments prior to the first time based at least in part on a baseline set of lung metrics characterizing lung state of the patient prior to the first time, and determining the second portion of the target airway segments between the first time and the second time based at least in part on the follow-up set of lung metrics.

156. The method of claim 155, wherein the second portion of the target airway segments is based on a comparison of the follow-up set of lung metrics and the baseline set of lung metrics.

157. The method of claim 155 or 156, further comprising: determining a number of endobronchial reinforcement implants to place in the patient at the first time based at least in part on the baseline set of lung metrics, and determining a number of endobronchial reinforcement implants to place in the patient at the second time based at least in part on the follow-up set of lung metrics.

158. The method of any one of claims 151-157, wherein the elapsed time between the first time and the second time is about one month, about three months, about six months, or about twelve months.

159. The method of any one of claims 151-158, wherein the elapsed time between the first time and the second time is determined at least in part based on health of the patient, progression of the pulmonary disease following the first time, or both.

160. The method of any one of claims 151-159, wherein the endobronchial reinforcement implant is a minimal endobronchial reinforcement implant.

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