Method and system for planning, predicting, and monitoring therapy for pulmonary disease - Patents.com

Abstract

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

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of priority to U.S. Patent Application No. 63 / 477,623, filed December 29, 2023, which is incorporated herein by reference in its entirety.

[0002] The present technology relates generally to treatment planning, and more particularly to methods and systems for planning, predicting, and monitoring therapy for pulmonary diseases. [Background technology]

[0003] Chronic obstructive pulmonary disorder (COPD) is a disease that impairs lung function. Symptoms of COPD include coughing, wheezing, shortness of breath, and chest tightness. Cigarette smoking is the primary cause of COPD, but long-term exposure to other lung irritants (e.g., air pollution, chemical fumes, and dust) can also cause or contribute to COPD. In most cases, COPD is a progressive disease that worsens over many years. Therefore, many people have COPD but are unaware of its progression. COPD is currently the leading cause of death and disability in the United States. Severe COPD can prevent patients from even performing basic activities such as walking, climbing stairs, or bathing. Unfortunately, there is no known cure for COPD. There are also no known medical techniques that can reverse the lung damage associated with COPD. Conventional approaches to treating COPD are associated with serious complications, have limited effectiveness, are appropriate for only a small proportion of COPD patients, and / or have other significant disadvantages. Given the prevalence of the disease and the inadequacies of conventional treatments, there is a great need for innovation in this field. Summary of the Invention [Means for solving the problem]

[0004] The subject technology is illustrated according to various aspects described below, including with reference to, for example, Figures 1-28. Various embodiments of aspects of the subject technology are described as numbered embodiments (1, 2, 3, etc.) for convenience. These are provided as examples and do not limit the subject technology. Example 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 the patient's lungs; generating a set of pulmonary metrics by inputting patient data into a first machine learning algorithm, the set of pulmonary metrics representing a pulmonary condition of the patient; predicting a patient's response to a treatment for the pulmonary disease by inputting the set of pulmonary metrics into a second machine learning algorithm; assessing whether the patient is a candidate for treatment for the pulmonary disease based on the predicted response; A method comprising: Example 2. The method of Example 1, wherein the patient data comprises one or more of the following: interview information, medical record information, magnetic resonance imaging (MRI) data, single photon emission computed tomography (SPECT) data, bronchoscopy data, ventilation-perfusion ratio data, pulmonary function test data, chest radiography data, fluoroscopy data, photographs, or sensor data. Example 3 The method of example 1 or 2, wherein the CT data comprises exhaled breath CT data. Example 4. The method of example 3, wherein the CT data comprises inhalation CT data. Example 5. The method of any one of Examples 1-4, wherein the patient data comprises data obtained at multiple different time points. Example 6. The method of any one of Examples 1-5, wherein the set of pulmonary metrics correlates to whether the patient has at least one of chronic obstructive pulmonary disease (COPD), severe emphysema, or severe emphysema with hyperinflation. Example 7. A set of pulmonary metrics characterizes one or more pulmonary parameters, the one or more pulmonary parameters being: forced expiratory volume in 1 second, forced vital capacity, vital capacity, inspiratory volume, inspiratory volume / total lung volume ratio, functional residual capacity, total lung capacity, diffusing volume for carbon monoxide, residual volume, residual volume / total lung volume ratio, occlusion volume, lobar and / or segmental tissue destruction, lobar and / or segmental air capture, lobar and / or segmental fissure status, lobar and / or segmental fissure completeness, lobar and / or segmental ventilation, lung function, homogeneity / disparity of lobar and / or segmental emphysema. 7. The method of any one of Examples 1-6, comprising one or more of: quality, emphysema type, location of diseased portion of lung, lobar volume, segment volume, segment location, diaphragm shape, tissue density, opacity, proximity of diseased portion to anatomical structures, proximity of diseased portion to other medical devices, luminal diameter of bronchial segment, airflow mapping, collapsed airway, airway pressure, airway compliance, intrathoracic pressure, airway diameter vs. wall thickness, airway wall deformation, vascular perfusion, parenchymal density, bullae, information localizing disease in the lung, obstruction score, mucus score, or degree of epithelialization. Example 8. The method of any one of Examples 1-7, wherein the set of pulmonary metrics comprises at least one disease score that characterizes the severity of pulmonary disease in the patient. Example 9. The method of Example 8, wherein the at least one disease score represents a predictor of patient response to treatment for the pulmonary disease. Example 10. The method of Example 8 or 9, wherein the set of pulmonary metrics comprises a plurality of disease scores, each corresponding to a distinct lobe, segment, or subsegmental region of the patient's lung. Example 11. The method of Example 8 or 9, wherein the set of pulmonary metrics comprises a single disease score based on multiple regional disease scores, each corresponding to a distinct lobe, segment, or subsegmental region of the patient's lung. Example 12. The method of Example 11, wherein a single disease score is the average of multiple local disease scores. Example 13 The method of any one of Examples 8-12, wherein the at least one disease score represents the degree of at least one of air trapping or hyperinflation in the patient's lungs. Example 14. The method of any one of Examples 7-13, wherein the set of pulmonary metrics characterizes a change in at least one of the one or more pulmonary parameters across multiple time points. Example 15. The method of Example 14, wherein the multiple 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. Example 16. The method of any one of Examples 1-15, 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 volume, inspiratory volume / total lung volume ratio, functional residual capacity, total lung capacity, diffusing capacity for carbon monoxide, residual volume, residual volume / total lung volume ratio, segmental volume, mMRC score, SGRQ score or a subset thereof, CAT score or a subset thereof, 6-minute walk test results, bicycle dysfunction results, cardiopulmonary exercise testing (CPET) results, patient health metrics, patient exercise metrics, patient encounter metrics, number of implant removals required, time to reintervention, durability of treatment, quality of life score, body mass index, comorbidities, drug regimen, length of hospital stay, healthcare utilization, or costs. Example 17 The method of any one of Examples 1-16, wherein the treatment comprises airway treatment for COPD. Example 18 The method of Example 17, wherein the airway treatment comprises a pharmacological treatment. Example 19 The method of Example 17 or 18, wherein the airway treatment comprises an interventional therapy. Example 20. The method of Example 19, wherein the interventional treatment comprises one or more of the following: steam therapy, administration of a sealant, transbronchial fenestration, placement of an endobronchial coil, placement of an endobronchial valve, or placement of a minimal endobronchial reinforcing implant. Example 21 The method of Example 20, wherein the interventional treatment comprises placement of a minimal endobronchial reinforcement implant. Example 22 The method of any one of Examples 1-21, further comprising generating a treatment plan if the patient is a candidate for treatment associated with pulmonary disease. Example 23. The method of example 22, wherein the plan is generated by inputting one or more of the set of predicted responses or pulmonary metrics into a third machine learning algorithm. Example 24. The method of Example 22 or 23, 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, route to target location, or local treatment solution. Example 25. The method of Example 24, wherein the implant placement location is based, at least in part, on one or more of the following: location of dynamic airway collapse as determined from expiratory CT data, severity of disease in peripheral regions of the lung, location of the patient's pleural wall, or location of lobar, segmental, and / or subsegmental airways. Example 26. The method of any one of Examples 1-25, further comprising generating a report, the report comprising a summary of one or more of the following: at least a portion of the set of pulmonary metrics, a predicted response, an assessment of whether the patient is a candidate for treatment, or a generated plan for treatment. Example 27. The method of any one of Examples 1-26, 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. Example 28 The method of Example 27, wherein the historical or repository patient data comprises data of patients with GOLD III COPD, data of patients with GOLD IV COPD, or a combination thereof. Example 29. The method of Example 27 or 28, wherein the historical or repository patient data comprises data of patients treated with one or more of the following: minimal endobronchial reinforcement implants, endobronchial valves, endobronchial coils, or steam therapy. Example 30. The method of any one of Examples 27-29, wherein the historical or repository patient data comprises data for the patient from an earlier time point. Example 31. A system, comprising: a processor; A memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computing system to perform operations including a method as recited in any one of Examples 1-30; A system comprising: Example 32. 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 including the method of any one of Examples 1-30. Example 33. A method for assessing a treatment outcome in a patient, the method comprising: receiving patient data including computed tomography (CT) data of the patient's lungs after placement of the endobronchial implant in the lungs; generating a set of status metrics by inputting the patient data into a first machine learning algorithm, the set of status metrics comprising: a set of pulmonary metrics describing the patient's pulmonary condition following placement of the endobronchial implant; a set of implant metrics that describe the status of the endobronchial implant after placement in the lung; and determining the patient's response to the endobronchial implant by inputting the set of status metrics into a second machine learning algorithm; and predicting a patient outcome after placement of the endobronchial implant by inputting one or more of the set of status metrics or the determined responses into a third machine learning algorithm. A method comprising: Example 34. The method of Example 33, wherein the patient data comprises one or more of the following: interview information, medical record information, magnetic resonance imaging (MRI) data, single photon emission computed tomography (SPECT) data, bronchoscopy data, ventilation-perfusion ratio data, pulmonary function test data, chest radiography data, fluoroscopy data, photographs, or sensor data. Example 35. The method of example 33 or 34, wherein the CT data comprises exhaled CT data. Example 36. The method of any one of Examples 33-35, wherein the CT data comprises inhalation CT data. Example 37. The method of any one of Examples 33-36, wherein the patient data comprises data obtained at multiple different time points. Example 38. A set of pulmonary metrics characterizes one or more pulmonary parameters, the one or more pulmonary parameters being: forced expiratory volume in 1 second, forced vital capacity, vital capacity, inspiratory volume, inspiratory volume / total lung volume ratio, functional residual capacity, total lung capacity, diffusing volume for carbon monoxide, residual volume, residual volume / total lung volume ratio, occlusion volume, lobar and / or segmental tissue destruction, lobar and / or segmental air capture, lobar and / or segmental fissure status, lobar and / or segmental fissure completeness, lobar and / or segmental ventilation, lung function, lobar and / or segmental emphysema homogeneity / heterogeneity, emphysema type, location of affected portions of the lung. The method of any one of Examples 33-37, wherein the method characterizes any of the following: lobe volume, segment volume, segment location, diaphragm shape, tissue density, opacity, proximity of diseased area to anatomical structures, proximity of diseased area to other medical devices, luminal diameter of bronchial segment, airflow mapping, collapsed airway, airway pressure, airway compliance, intrathoracic pressure, airway diameter vs. wall thickness, airway wall deformation, vascular perfusion, parenchymal density, blood vessels, information localizing disease within the lung, obstruction score, mucus score, degree of epithelialization, granulation tissue, implant-induced airway deformation, or airway tissue invagination into the lumen of the implant. Example 39. The method of any one of Examples 33-38, wherein the set of pulmonary metrics comprises a disease score that characterizes the severity of pulmonary disease in the patient. Example 40. The method of example 38 or 39, wherein the set of pulmonary metrics characterizes the change in at least one of the one or more pulmonary parameters across multiple time points. Example 41 The method of Example 40, wherein the plurality of time points comprises 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. Example 42 The method of any one of Examples 33-41, wherein the endobronchial implant comprises a minimal endobronchial reinforcement implant. Example 43. The method of any one of Examples 33-42, wherein the set of implant metrics characterizes one or more of the following: implant location, distance between the distal end of the implant and the pleura, implant length, implant diameter at any one or more locations along the length of the implant, implant cross-sectional profile at any one or more locations along the length of the implant, implant integrity, implant loop pitch, implant loop profile angle relative to the longitudinal axis of the implant, implant position relative to one or more additional implants, implant movement between inspiration and expiration, implant obstruction, or implant dislodgement. Example 44. The method of any one of Examples 33-43, 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. Example 45. The method of any one of Examples 33-44, 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 volume, inspiratory volume / total lung volume ratio, functional residual capacity, total lung capacity, diffusing capacity for carbon monoxide, residual volume, residual volume / total lung volume ratio, segmental volume, mMRC score, SGRQ score or a subset thereof, CAT score or a subset thereof, 6-minute walk test results, bicycle dysfunction results, cardiopulmonary exercise testing (CPET) results, patient health metrics, patient exercise metrics, patient encounter metrics, number of implant removals required, time to reintervention, durability of treatment, quality of life score, body mass index, comorbidities, drug regimen, length of hospital stay, healthcare utilization, or costs. Example 46 The method of any one of Examples 33-45, wherein the predicted outcome comprises prediction of post-procedure problems following placement of an endobronchial implant. Example 47. The method of Example 46, wherein the post-procedure problem comprises one or more of the following: excessive mucus, excessive granulation tissue, excessive fibrosis, implant collapse, implant failure, implant migration, implant expectoration, inadequate lung function, pneumothorax, infection, pneumonia, or hospitalization. Example 48 The method of Example 46 or 47, further comprising determining an intervention to address post-procedure issues. Example 49. The method of Example 48, wherein the determined intervention comprises one or more of the following: clean-up bronchoscopy, retrieval or removal of the endobronchial implant, repositioning the endobronchial implant, replacing the endobronchial implant, enlarging the endobronchial implant, placing an additional endobronchial implant, or consultation with a health care professional. Example 50. The method of any one of Examples 33-49, further comprising generating a report, the report comprising a summary of one or more of the following: at least a portion of the lung metrics, at least a portion of the implant metrics, the patient's determined response to the endobronchial implant, the patient's predicted outcome following placement of the endobronchial implant, or a determined intervention to address post-procedure issues. Example 51. The method of any one of Examples 33-50, 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. Example 52 The method of Example 51, wherein the historical or repository patient data comprises data of patients with GOLD III COPD, data of patients with GOLD IV COPD, or a combination thereof. Example 53. The method of Example 51 or 52, wherein the historical or repository patient data comprises data of patients treated with one or more of the following: minimal endobronchial reinforcement implants, endobronchial valves, endobronchial coils, or steam therapy. Example 54. The method of any one of Examples 51-53, wherein the historical or repository patient data comprises data for the patient from an earlier time point. Example 55. The method of any one of Examples 33-54, further comprising comparing the set of pulmonary metrics to a second set of pulmonary metrics, the second set of pulmonary metrics being determined from one or more of the following: lung image data before placement of the endobronchial implant, lung image data at an earlier time point after placement of the endobronchial implant, lung image data after placement of another endobronchial implant at a location different from the location of the endobronchial implant, or image data from another patient with COPD. Example 56. A system, comprising: a processor; A memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computing system to perform operations including the method described in any one of Examples 33-55; A system comprising: Example 57. 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 including the method described in any one of Examples 33-55. Example 58. A method for assessing a patient having or suspected of having a pulmonary disease, comprising: receiving patient data, including computed tomography (CT) data of the patient's lungs; generating a pulmonary disease score for a region of interest in the lung by inputting patient data into a machine learning algorithm, the pulmonary disease score characterizing the severity of pulmonary disease in the region of interest in the patient's lung, the region of interest being a segmental or sub-segmental region of the lung; A method comprising: Example 59. The method of Example 58, wherein the machine learning algorithm evaluates voxel density in the CT data associated with a region of interest in the patient's lungs. Example 60 The method of Example 58 or 59, wherein the pulmonary disease score is based on multiple regional disease scores, each corresponding to a distinct lobe, segment, or subsegmental region of the patient's lung. Example 61 The method of Example 60, wherein the pulmonary disease score is the average of multiple local disease scores. Example 62 The method of Example 58 or 59, wherein the pulmonary disease score is a first pulmonary disease score, and the method further comprises generating a plurality of pulmonary disease scores comprising the first pulmonary disease score, each of the plurality of pulmonary disease scores corresponding to a distinct lobe, segment, or subsegmental region of the patient's lung. Example 63 The method of any one of Examples 58-62, wherein the pulmonary disease score represents the degree of at least one of air trapping or hyperinflation in the patient's lungs. Example 64. The method of any one of Examples 58-63, wherein the CT data comprises exhalation CT data. Example 65. The method of any one of Examples 58-64, wherein the CT data comprises inhalation CT data. Example 66. The method of any one of Examples 58-65, wherein the CT data is generated prior to a treatment being administered to the patient to treat the pulmonary disease. Example 67. The method of any one of Examples 58-65, wherein the CT data is generated subsequent to a treatment administered to the patient to treat the pulmonary disease. Example 68 The method of example 66 or 67, wherein the treatment comprises placement of an intrabronchial implant. Example 69. A system, comprising: a processor; A memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computing system to perform operations including the method of any one of Examples 58-68; A system comprising: Example 70. A computer tomography (CT) scanning device, comprising: a processor; a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the CT scanning device to perform operations including the method of any one of Examples 58-68; A computer tomography (CT) scanning device comprising: Example 71. 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 including the methods described in any one of Examples 58-68. Example 72. A method for normalizing quantitative computed tomography (CT) results for a patient, the method comprising: receiving first CT data related to a patient, the first CT data being generated under predetermined imaging conditions; converting the first CT data into second CT data by applying at least one correction factor associated with a predetermined imaging condition to the first CT data; A method comprising: Example 73. The method of Example 72, wherein at least one correction factor maps voxel densities in the first CT data to normalized voxel densities. Example 74. The method of example 72 or 73, wherein 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. Example 75. The method of any one of Examples 72-74, 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 spacing. Example 76. The method of any one of Examples 72-75, wherein at least one correction factor compensates for voxel density in the first CT data that is affected by a reconstruction algorithm for determining image sharpness or smoothness in an axial plane. Example 77. The method of any one of Examples 72-76, 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 the CT data, and the at least one correction factor compensates for voxel density in the first CT data affected by the provider-specific machine learning algorithm. Example 78. The method of any one of Examples 72-77, wherein at least one correction factor compensates for voxel densities in the first CT data that are affected by administration of a contrast agent in the patient before the first CT data is generated. Example 79. The method of any one of Examples 72-78, wherein the second CT data is normalized with respect to a CT scanning parameter. Example 80. The method of any one of Examples 72-79, wherein the first CT data is acquired during a pre-procedural phase prior to placement of the endobronchial implant in the patient. Example 81. The method of any one of Examples 72-80, further comprising generating a set of pulmonary metrics associated with the patient based on the second CT data. Example 82 The method of any one of Examples 72-79, wherein the first CT data is acquired during an intraprocedural stage during placement of an endobronchial implant in the patient. Example 83 The method of any one of Examples 72-79, wherein the first CT data is acquired during a post-procedure phase following placement of an endobronchial implant in the patient. Example 84. The method of Example 82 or 83, 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. Example 85. A system, comprising: a processor; A memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computing system to perform operations including the method described in any one of Examples 72-84; A system comprising: Example 86. A computer tomography (CT) scanning device, comprising: a processor; a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the CT scanning device to perform operations including the method of any one of Examples 72-84; A computer tomography (CT) scanning device comprising: Example 87. 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 including the method described in any one of Examples 72-84. Example 88. A method for planning a treatment for a patient with a pulmonary disease, comprising: receiving patient data including computed tomography (CT) data of the patient's lungs; generating a set of pulmonary metrics by inputting patient data into a first machine learning algorithm, the set of pulmonary metrics representing a pulmonary condition of the patient; identifying potential target regions within the lung for treatment for the pulmonary disease based at least in part on the generated pulmonary metrics; A method comprising: Example 89. The method of Example 88, wherein the patient data comprises one or more of the following: interview information, medical record information, magnetic resonance imaging (MRI) data, single photon emission computed tomography (SPECT) data, bronchoscopy data, ventilation-perfusion ratio data, pulmonary function test data, chest radiography data, fluoroscopy data, photographs, or sensor data. Example 90. The method of example 88 or 89, wherein the CT data comprises exhalation CT data. Example 91. The method of any one of Examples 88-90, wherein the CT data comprises inhalation CT data. Example 92. The method of any one of Examples 88-91, wherein the patient data comprises data obtained at multiple different time points. Example 93. The method of any one of Examples 88-92, wherein the set of pulmonary metrics correlates with whether the patient has at least one of chronic obstructive pulmonary disease (COPD), severe emphysema, or severe emphysema with hyperinflation. Example 94. A set of pulmonary metrics characterizes one or more pulmonary parameters, the one or more pulmonary parameters being: forced expiratory volume in 1 second, forced vital capacity, vital capacity, inspiratory volume, inspiratory volume / total lung volume ratio, functional residual capacity, total lung capacity, diffusing volume for carbon monoxide, residual volume, residual volume / total lung volume ratio, occlusion volume, lobar and / or segmental tissue destruction, lobar and / or segmental air capture, lobar and / or segmental fissure status, lobar and / or segmental fissure completeness, lobar and / or segmental ventilation, lung function, homogeneity / heterogeneity of lobar and / or segmental emphysema. The method of any one of Examples 88-93, comprising one or more of the following: type, emphysema type, location of diseased portion of lung, lobe volume, segment volume, segment location, diaphragm shape, tissue density, opacity, proximity of diseased portion to anatomical structures, proximity of diseased portion to other medical devices, luminal diameter of bronchial segment, airflow mapping, collapsed airway, airway pressure, airway compliance, intrathoracic pressure, airway diameter vs. wall thickness, airway wall deformation, vascular perfusion, parenchymal density, bullae, information localizing disease within the lung, obstruction score, mucus score, or degree of epithelialization. Example 95. The method of any one of Examples 88-94, wherein the set of pulmonary metrics comprises at least one disease score that characterizes the severity of pulmonary disease in the patient. Example 96 The method of Example 95, wherein at least one disease score represents a predictor of patient response to treatment for a pulmonary disease. Example 97. The method of Example 95 or 96, wherein the set of pulmonary metrics comprises a plurality of disease scores, each corresponding to a distinct lobe, segment, or subsegmental region of the patient's lung. Example 98. The method of Example 95 or 96, wherein the set of pulmonary metrics comprises a single disease score based on multiple regional disease scores, each corresponding to a distinct lobe, segment, or subsegmental region of the patient's lung. Example 99 The method of Example 98, wherein a single disease score is the average of multiple local disease scores. Example 100 The method of any one of Examples 95-99, wherein the at least one disease score represents the degree of at least one of air trapping or hyperinflation in the patient's lungs. Example 101. The method of any one of Examples 94-100, wherein the set of pulmonary metrics characterizes the change in at least one of the one or more pulmonary parameters across multiple time points. Example 102. The method of Example 101, wherein the plurality of time points comprises two or more of the following: before intrabronchial implant therapy, after intrabronchial implant therapy, before administration of a bronchodilator, after administration of a bronchodilator, before exercise, or during exercise. Example 103 The method of any one of Examples 88-102, wherein the treatment comprises airway treatment for COPD. Example 104 The method of Example 103, wherein the airway treatment comprises a pharmacological treatment. Example 105. The method of example 103 or 104, wherein the airway treatment comprises an interventional therapy. Example 106. The method of Example 105, wherein the interventional treatment comprises one or more of the following: steam therapy, administration of a sealant, transbronchial fenestration, placement of an endobronchial coil, placement of an endobronchial valve, or placement of a minimal endobronchial reinforcing implant. Example 107. The method of example 106, wherein the interventional treatment comprises placement of a minimal endobronchial reinforcement implant. Example 108 The method of any one of Examples 88-107, further comprising generating a treatment regimen. Example 109. The method of example 108, wherein the plan is generated by inputting a set of pulmonary metrics into a second machine learning algorithm. Example 110. The method of example 108 or 109, 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, route to target location, or local treatment solution. Example 111. The method of Example 110, wherein the implant placement location is based, at least in part, on one or more of the following: location of dynamic airway collapse as determined from expiratory CT data, severity of disease in peripheral regions of the lung, location of the patient's pleural wall, or location of lobar, segmental, and / or subsegmental airways. Example 112. A system, comprising: a processor; A memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computing system to perform operations including a method as recited in any one of Examples 88-111; A system comprising: Example 113. A computer tomography (CT) scanning device, comprising: a processor; a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the CT scanning device to perform operations including a method according to any one of Examples 88-111; A computer tomography (CT) scanning device comprising: Example 114. A non-transitory computer-readable storage medium, the 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 including the method described in any one of Examples 88-111. [Brief explanation of the drawings]

[0005] 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, emphasis instead being placed upon clearly illustrating the principles of the present disclosure.

[0006] [Figure 1] FIG. 1 is a schematic representation of the bronchial tree of a human subject within the subject's thoracic cavity.

[0007] [Figure 2] FIG. 2 is a schematic representation of the bronchial tree of a human subject in isolation.

[0008] [Figure 3] FIG. 3 is an enlarged view of the terminal portion of the bronchial tree shown in FIG.

[0009] [Figure 4] FIG. 4 is a table showing examples of dimensions and generation numbers for different parts of the bronchial tree of a human subject.

[0010] [Figure 5] FIG. 5 is a diagram showing lung volumes during normal lung function.

[0011] [Figure 6] FIG. 6 is a table showing airway wall composition in different parts of the bronchial tree of a human subject.

[0012] [Figure 7] FIG. 7 is an anatomical illustration of the airway wall composition in different parts of the bronchial tree of a human subject.

[0013] [Figure 8] FIG. 8 is an anatomical illustration showing small airway narrowing in emphysematous lung tissue.

[0014] [Figure 9] FIG. 9 is an anatomical illustration showing alveolar wall damage in emphysematous lung tissue.

[0015] [Figure 10] FIG. 10 is an anatomical illustration showing normal airway patency during exhalation in healthy lung tissue.

[0016] [Figure 11] FIG. 11 is an anatomical illustration showing airway collapse during exhalation in emphysematous lung tissue.

[0017] [Figure 12] FIG. 12 is an anatomical diagram showing a normal acinar compartment.

[0018] [Figure 13] FIG. 13 is an anatomical diagram showing centrilobular emphysema.

[0019] [Figure 14] FIG. 14 is an anatomical diagram showing panlobular emphysema.

[0020] [Figure 15] FIG. 15 is an anatomical diagram showing perilobular emphysema.

[0021] [Figure 16] FIG. 16 is a side view of an implant in accordance with at least some embodiments of the present technology.

[0022] [Figure 17] FIG. 17 is a schematic end view of the implant shown in FIG.

[0023] [Figure 18] FIG. 18 is a side view of a portion of an implant in an airway, in accordance with at least some embodiments of the present technology.

[0024] [Figure 19] FIG. 19 is a block diagram providing a general overview of the workflow for selecting a patient for treatment and planning and monitoring a treatment procedure, according to an embodiment of the present technology.

[0025] [Figure 20] FIG. 20 is a flow diagram illustrating a method for planning treatment for a patient in accordance with an embodiment of the present technology.

[0026] [Figure 21]FIG. 21 is a flow diagram illustrating a method for assessing a patient's treatment outcome in accordance with an embodiment of the present technology.

[0027] [Figure 22] FIG. 22 is a flow diagram illustrating a method for updating the software algorithms of FIGS. 20 and 21 in accordance with an embodiment of the present technology.

[0028] [Figure 23] 23A and 24B are flow diagrams illustrating examples of generating various pulmonary metrics based on inspiratory and expiratory CT scans, respectively.

[0029] [Figure 24] FIG. 24 is a flow diagram illustrating an example of assessing treatment effect and / or efficacy based on CT scans.

[0030] [Figure 25] Figure 25A is an anatomical illustration showing a coronal view of the fissure in the right and left lungs, Figure 25B is an anatomical illustration showing a sagittal view of the fissure in the right lung, and Figure 25C is an anatomical illustration showing a sagittal view of the fissure in the left lung.

[0031] [Figure 26] FIG. 26 is a flow diagram illustrating a method for normalizing quantitative CT results.

[0032] [Figure 27] FIG. 27 is a flow diagram illustrating a method for evaluating a patient having or suspected of having a pulmonary disease.

[0033] [Figure 28] FIG. 28 is a flow diagram illustrating a method for planning therapy for a patient with a pulmonary disease. DETAILED DESCRIPTION OF THE INVENTION

[0034] Detailed Description The present technology relates to methods for planning, predicting, and / or monitoring a treatment procedure for a patient with 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 the patient's lungs. The method can include generating a set of pulmonary metrics by inputting the patient data into a first machine learning algorithm. The method can also include predicting the patient's response to a treatment for the pulmonary disease (e.g., treatment with an endobronchial implant) by inputting the set of pulmonary 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.

[0035] As another example, a method for assessing a patient's treatment outcome includes receiving patient data including CT data of the patient's lungs after placement of an endobronchial implant in the lungs. The method may 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 may also include determining the patient's response to the endobronchial implant by inputting the set of status metrics into a second machine learning algorithm. The method may further include predicting the patient's outcome after placement of the endobronchial implant by inputting the set of status metrics and / or the determined response into a third machine learning algorithm.

[0036] The present technology can provide numerous benefits for treating patients with pulmonary diseases. For example, the methods described herein can be used to diagnose and treat patients at earlier stages of the disease (e.g., stage 2 COPD), which can improve therapeutic efficacy and lead to better outcomes. In addition, the methods herein can provide patients with personalized, evidence-based treatment recommendations that are more likely to lead to successful outcomes. The methods herein can also improve planning of therapeutic procedures, which can reduce procedure time, improve patient safety, and lead to improved outcomes. The methods of the present technology can also monitor patients after procedures and predict problems before they arise, thus reducing the frequency of additional hospitalizations and doctor visits after therapeutic procedures.

[0037] 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 views and in which exemplary embodiments are shown. However, claimed embodiments may 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.

[0038] The headings provided herein are for convenience only and do not interpret the scope or meaning of the claimed technology. Embodiments under any one heading may be used in conjunction with embodiments under any other heading. I. Anatomy and Physiology

[0039] In normal breathing, the act of inhalation draws air into the lungs through the nose or mouth and trachea. Within each lung, inhaled air travels into a branching network of progressively smaller airways called bronchi, then into the smallest airways called bronchioles. The bronchioles terminate in numerous tiny, rounded structures called alveoli. Microscopic blood vessels called capillaries extend through the walls of the alveoli. When inhaled air reaches the alveoli, oxygen moves from the alveoli into the blood within the capillaries. Simultaneously, carbon dioxide moves in the opposite direction—from the blood within the capillaries into the alveoli. This process is called gas exchange. In healthy lungs, the airways and alveoli are elastic and stretch to accommodate the intake of air. When air is inhaled, the alveoli fill with air like tiny balloons. When air is exhaled, the alveoli contract. This expansion of the alveoli is an important part of effective gas exchange. Freely expanding alveoli exchange more gas than alveoli that are prevented from expanding.

[0040] Figure 1 is a schematic illustration of the bronchial tree of a human subject within the subject's thoracic cavity. As shown in Figure 1, the bronchial tree includes a trachea T, which extends downward from the nose and mouth and divides into a left main bronchus LMB and a right main bronchus RMB. The left and right main bronchi each branch to form lobar bronchi LB, segmental bronchi SB, and subsegmental bronchi SSB, which have successively smaller diameters and shorter lengths as they extend distally. Figure 2 is a schematic illustration of the bronchial tree in isolation. As shown in Figure 2, the subsegmental bronchi continue to branch to form bronchioli BO, conducting bronchioli CBO, and ultimately terminal bronchioli TBO, the smallest airways that do not contain alveoli. The terminal bronchioli branch into respiratory bronchioli RBO, which divide into alveolar ducts AD. Figure 3 is an enlarged view of the terminal portion of the bronchial tree. As shown in Figure 3, the alveolar duct terminates in a cul-de-sac containing two or more small clusters of alveoli A, called alveolar sacs AS. Various single alveoli may also be located along the length of the respiratory bronchioles.

[0041] The bronchi and bronchioles are conducting airways that transport air to and from the alveoli. They do not participate in gas exchange. Rather, gas exchange occurs within the alveoli, which are found distal to the conducting airways, originating from the respiratory bronchioles. It is common to refer to the various airways of the bronchial tree as "generations" depending on the degree of branching proximally. For example, the trachea is referred to as "generation 0" of the bronchial tree, the bronchi at various levels, 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." Furthermore, it is common to refer to any of the airways extending from the trachea to the terminal bronchioles as "conducting airways." Figure 4 is a table showing examples of the dimensions and generation numbers of different portions of the bronchial tree.

[0042] The respiratory bronchioles, alveoli, and alveolar sacs receive air through more proximal portions of the bronchial tree and participate in gas exchange, oxygenating blood pumped from the heart through the pulmonary arteries, branching vessels, and capillaries to the lungs. A thin, semipermeable membrane separates the oxygen-depleted blood in the capillaries from the oxygen-rich air in the alveoli. The capillaries wrap around and extend between the alveoli. Oxygen from the air diffuses through the membrane into the blood. Carbon dioxide from the blood diffuses through the membrane into the air in the alveoli. The newly oxygenated blood then flows from the alveolar capillaries to the heart through the branching vessels of the pulmonary venous system. The heart pumps the oxygen-rich blood throughout the body. The oxygen-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 state. In this manner, air flows through the branching bronchioles, segmental bronchi, lobar bronchi, main bronchi, and trachea, and is finally expelled through the mouth and nose.

[0043] Figure 5 is a schematic diagram showing lung volume during normal lung function. Approximately one-tenth of the total lung capacity is used at rest. A larger volume is used as needed (e.g., with exercise). The tidal volume (TV) is the volume of air that is inhaled and exhaled without conscious effort. The additional volume of air that can be exhaled with maximal effort after normal inspiration is the inspiratory reserve volume (IRV). The additional volume of air that can be forcibly exhaled after normal expiration is the expiratory reserve volume (ERV). The total volume of air that can be exhaled after maximal inspiration is the vital capacity (VC). VC is equal to the sum of the TV, IRV, and ERV. The residual volume (RV) is the volume of air remaining in the lungs after maximal expiration. The lungs can never be completely emptied. The total lung capacity (TLC) is the sum of the VC and RV. Assessment of pulmonary function can be used to determine patient eligibility for therapy and to evaluate the effectiveness of therapy.

[0044] Figure 6 is a table showing the airway wall composition in different parts of the bronchial tree. Figure 7 is an anatomical illustration of the airway wall composition in different parts of the bronchial tree. As shown in Figures 6 and 7, the walls of bronchi, bronchioles, alveolar ducts, and alveoli contain epithelium, connective tissue, goblet cells, mucus 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 lined within ciliated pseudostratified columnar epithelium, commonly referred to as respiratory epithelium. Cilia located on these epithelia beat unidirectionally, moving mucus and foreign particles, such as dust and bacteria, from more distal airways to more proximal airways and ultimately to the throat, where the mucus and / or foreign particles are cleared by swallowing or expectoration. By moving down the bronchioles, the cells become more cuboidal in shape but are still ciliated.

[0045] The proportions and properties of various components of the airway wall vary depending on the location within the bronchial tree. For example, mucus glands are abundant in the trachea and main bronchi but begin to be absent in the bronchioles (e.g., at approximately generation 10). In the trachea, cartilage is present as C-shaped rings of hyaline cartilage, while in the bronchi, cartilage takes the form of scattered plates. As branching continues through the bronchial tree, the amount of hyaline cartilage within the wall decreases until it is absent in the bronchioles. Smooth muscle originates in the trachea, where it joins the C-shaped rings of cartilage. Smooth muscle continues into the bronchi and bronchioles, which are completely surrounded by smooth muscle. Instead of stiff cartilage, the bronchi and bronchioles are composed of elastic tissue. As cartilage decreases, the amount of smooth muscle increases. The mucosa also undergoes a transition from ciliated pseudostratified columnar epithelium to simple cuboidal epithelium to simple squamous epithelium.

[0046] Figures 25A-25C are anatomical illustrations of the fissures in 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 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 indentations in the visceral pleura and exist between different lobes. In addition, segmental fissures (not shown) exist across the five lobes (RUL, RML, RLL, LUL, LLL) and separate 18 segments. However, the appearance of the fissures can vary greatly from patient to patient and may be incomplete, even absent, and / or distorted (in location, shape, etc.) due to diseases such as COPD. The completeness or completeness of fissures indicates the degree to which lung lobes are clearly separated. Incomplete fissures may indicate, for example, that air from one lobe can flow into another (e.g., collateral ventilation). CT imaging can be used to visualize certain features of pulmonary fissures, but different CT protocols may lead to different appearances of fissures. One or more algorithms can be used to automatically identify and / or characterize pulmonary fissures in CT images, such as fissure segmentation algorithms (e.g., algorithms for performing implicit surface fitting to the surface topography of the lung volume, trained machine learning algorithms such as supervised fissure enhancement filters, algorithms for performing adaptive fissure sweeping and wavelet transforms, etc.), and / or anatomical knowledge-based algorithms (e.g., fuzzy inference systems for locating fissures based, at least in part, on ridgeness image intensity and smoothness, etc.), and / or other suitable algorithms for fissure characterization. II. Pulmonary Disease

[0047] COPD is a major public health problem. In the United States alone, there are over one million patients with severe emphysema and severe hyperinflation. The overwhelming majority of these patients are underserved by currently available treatments. The unmet clinical need worldwide, including in countries with high rates of smoking-related respiratory disease, exceeds that in the United States many times over.

[0048] In lung tissue affected by COPD, 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 produce excess mucus, leading to mucus accumulation and airway obstruction. In typical cases of COPD, the disease does not affect all airways and alveoli equally within the lungs. The lungs may have some areas that are significantly more affected than others. In severe cases, airways and alveoli inadequate for effective gas exchange may comprise 20 to 30 percent or more of the total lung volume.

[0049] The effects of COPD are often most evident when patients engage in exercise or other physical exertion that would cause healthy individuals to breathe heavily. Patients with COPD may not be able to breathe heavily because affected portions of the patient's lungs trap air and are unable to fully exhale it. This, in turn, prevents subsequent expansion of healthy lung tissue. Thus, during exercise or other physical exertion, COPD patients' lungs may operate in a state of dynamic hyperinflation, impairing respiratory mechanics and increasing respiratory workload. Lung hyperinflation may also impede cardiac filling, leading to dyspnea and / or reducing the patient's exercise performance. These and / or other detrimental effects of COPD can ultimately lead to a series of symptoms that impair a patient's quality of life and increase the risk of severe disability and death.

[0050] The term "COPD" includes both chronic bronchitis and emphysema. Approximately 25% of COPD patients have emphysema. Approximately 40% of these emphysema patients have severe emphysema. Furthermore, it is common for COPD patients to have symptoms of both chronic bronchitis and emphysema. In chronic bronchitis, the interior of the airways generally becomes inflamed as a result of continued irritation. This inflammation leads to thickening of the interior of the airways and the production of thick mucus, which can coat and eventually congest the airways. In contrast, emphysema is a pathological diagnosis primarily related to the abnormal, permanent enlargement of the air spaces distal to the terminal bronchioles. In emphysematous lung tissue, small airways and / or alveoli typically lose their structural integrity and / or their ability to maintain optimal shape. For example, damage to or destruction of the alveolar walls can result in fewer but larger alveoli. This can significantly impair normal gas exchange. Within the lungs, lesions or "diseased" areas of emphysematous lung tissue, characterized by the absence of distinct alveolar walls, can be referred to as pulmonary bullae. These relatively inelastic pockets of dead space often exceed 1 cm in diameter and do not significantly contribute to gas exchange. Pulmonary bullae tend to create hyperinflated lung segments that retain air and thereby limit the ability of healthy lung tissue to fully expand in response to inspiration. Thus, in patients with emphysema, not only does the diseased lung tissue no longer significantly contribute to respiratory function, but it also impairs the function of healthy lung tissue.

[0051] Figure 8 is an anatomical illustration showing small airway narrowing in emphysematous lung tissue. Figure 9 is an anatomical illustration showing alveolar wall damage in emphysematous lung tissue. Figure 10 is an anatomical illustration showing normal airway patency during exhalation. Figure 11 is an anatomical illustration showing airway collapse during exhalation in emphysematous lung tissue. COPD, particularly emphysema, is characterized by irreversible destruction of alveolar walls, which contain elastic fibers that maintain radially outward traction on small airways and are useful during inhalation and exhalation. As shown in Figures 8-11, when these elastic fibers are damaged, small airways are no longer under radially outward traction and collapse, particularly during exhalation. Furthermore, emphysema destroys alveolar walls. As shown in Figure 9, this results in a larger air space and reduces the surface area available for gas exchange. The lungs are therefore unable to carry out gas exchange at a satisfactory rate, which causes a reduction in oxygenated blood. In addition, the large air spaces in the affected lungs combined with the collapsed airways result in the lungs becoming overinflated (air trapped) and unable to fully exhale. The overinflated lungs also exert continuous pressure on the chest wall, diaphragm, and surrounding structures, which can cause shortness of breath and prevent patients from walking short distances or performing daily tasks. Both the quality of life and life expectancy for patients with late-stage emphysema are extremely poor, with less than half of patients surviving for another five years.

[0052] There are three types of emphysema: centrilobular, panlobular, and perilobular. Figure 12 is an anatomical diagram showing normal acinar regions. Figure 13 is an anatomical diagram showing centrilobular emphysema, with alveoli and airways within the central acini, including destruction of alveoli within the walls of the respiratory bronchioles and alveolar ducts. Figure 14 is an anatomical diagram showing panlobular emphysema, characterized by destruction of alveoli, alveolar ducts, and respiratory bronchiolar tissue. This produces a very uniform enlargement of air spaces throughout the acini and uniformly distributed emphysematous changes across the acini and secondary lobules. Figure 15 is an anatomical diagram showing perilobular emphysema, characterized by increased air spaces at the periphery of the acini, primarily resulting from destruction of the alveoli and alveolar ducts. Perilobular emphysema is usually limited in distribution and occurs most commonly along the posterior surface of the upper lung. It often coexists with other forms of emphysema.

[0053] Emphysema can also be characterized as heterogeneous or homogeneous.Generally, heterogeneous emphysema in lung (right or left) is characterized by any two or more than two regions (for example, lobe, area) that have the relative difference of emphysema destruction above threshold amount, while homogeneous emphysema in lung is characterized by any two or more than two regions (for example, lobe, area) that have the relative difference of emphysema destruction below threshold amount.In some patients, both right lung and left lung can be heterogeneous or homogeneous, or one lung can be heterogeneous, while the other lung can be homogeneous.

[0054] Pharmacological treatments can be prescribed for COPD. Treatment regimens of bronchodilators, B2 agonists, muscarinic agonists, corticosteroids, or a combination thereof can provide short-term relief of COPD symptoms. 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 the lungs (typically up to 20-25 percent of lung volume), thereby reducing the overall size of the lungs and freeing up more volume in the thoracic cavity available for expansion of relatively healthy lung tissue. With more available volume for expansion, the lung tissue remaining after LVRS has an improved capacity for effective gas exchange.

[0055] There are also procedures for lung volume reduction that do not involve surgical removal of diseased lung tissue. Examples include the use of coils or clips to capture and physically compact diseased lung tissue. These procedures can reduce the overall volume of the lungs, with an effect similar to that of LVRS. Another device-based treatment for COPD involves the placement of one-way stent valves in airways proximal to emphysematous tissue. These valves allow air to flow out of the lung but not into its hyperinflated portion. While not traditionally used to treat COPD, stents are sometimes used in the lumen of central airways (e.g., trachea, main bronchi, lobar bronchi, and / or segmental bronchi) to temporarily improve the patency of these airways. For example, stents can be used to temporarily improve patency in central airways affected by benign or malignant obstructions.

[0056] Water vapor / steam therapy, such as bronchoscopic thermal vapor ablation (BTVA), is another COPD treatment option. BTVA involves the introduction of heated water vapor into affected lung tissue. This procedure produces a thermal reaction that leads to an initial local inflammatory response, followed by permanent fibrosis and atelectasis. Similar to thermal therapies such as BTVA, biochemical therapies also exist, which involve the injection of adhesive glue or sealant into affected lung tissue. Both thermal and biochemical procedures can hasten remodeling, resulting in a reduction of tissue and air volume in targeted areas of hyperinflated lungs.

[0057] Some other known COPD treatments involve bypassing obstructed airways. For example, a puncture through the chest wall into the outer portion of the lung can be used to create a direct connection (e.g., a bypass tract) between the affected alveoli and the outside of the body. If no other steps are taken, these bypass tracts will typically close through normal healing or the formation of granulation tissue. Therefore, placing a tubular prosthesis within the bypass tract can temporarily extend the therapeutic benefit. III. Intrabronchial implants

[0058] In some embodiments, the present technology provides for endobronchial placement of an implant to establish or improve airway patency (also referred to herein as "endobronchial implant therapy"). The implant may 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 a 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. Minimal endobronchial reinforcement implants may be advantageous compared to other types of endobronchial implants (e.g., valves, airway stents) whose therapeutic effects may be compromised due to foreign body reaction (e.g., granulation tissue reaction, mucus plugging, airway narrowing).

[0059] The implant can be placed in a treatment location, including a previously collapsed airway, such as a previously collapsed distal airway. Deployment of the implant can release air trapped within a hyperinflated portion of the lung and / or reduce or prevent subsequent trapping of air within this portion of the lung. In at least some cases, it is desirable for the treatment location in which the implant is deployed to include (distal to proximal) generation 4 or higher / deeper airways, such as respiratory bronchioles, terminal bronchioles, conducting bronchioles, or subsegmental bronchi, and then extend proximally (distal to proximal) to more central, larger airways (e.g., generation 6 or higher / lower), such as subsegmental bronchi, segmental bronchi, lobar bronchi, and main bronchi. A single implant can create a seamless pathway from distal to proximal, ensuring a passageway for trapped air. In an alternative embodiment, multiple individual implants can be used in place of a single, longer implant. Multiple individual implants may be placed in bronchial airways that are collapsed or at risk of collapse. The use of multiple individual implants in selected locations within the bronchial tree may have the advantage of using less material, thereby reducing contact stress and foreign body response, and allowing for more flexibility and customization of therapy. For example, a single implant embodiment may extend from a distal, higher-generation airway to a proximal, lower-generation airway, while a system of multiple individual implants may allow for placement of implants in multiple airways of the same generation.

[0060] The devices, systems, and methods described herein may be applied to different bronchopulmonary segments 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: the upper lobe (superior, i.e., apical-posterior, anterior, lingual, i.e., superior, inferior); and the lower lobe, i.e., superior, anterior-medial, basal, lateral. Treatment of the right lung may involve one or more of the following segments: the upper lobe, i.e., apical, anterior, posterior; the middle lobe, i.e., medial, lateral; and the lower lobe, i.e., superior, anterior-medial, basal. 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 particular lobe (e.g., the upper lobe) and in a particular segment within such lobe, or it may involve placing at least one implant in multiple lobes, segments within a lobe, or subsegments within a segment. The determination of the portion of the lung to treat can be made by a clinical operator (e.g., a pulmonologist or surgeon) with the aid of imaging (e.g., CT, ultrasound, radiography, or bronchoscopy) to assess the presence and pathology of disease and its effect on lung function and airflow dynamics.

[0061] 16 and 17 illustrate an example of a minimal endobronchial reinforcement implant configured as an expandable device 100 for placement within an airway lumen. In particular, FIG. 16 is a side view of expandable device 100 in an expanded, unconstrained state, and FIG. 17 is an end view of device 100. As shown in FIG. 16, device 100 can comprise a generally tubular structure configured to be positioned within an airway lumen. For example, device 100 can be configured to be implanted within an airway lumen such that device 100 maintains a lumen of a minimum desired diameter within the airway. Device 100 has a first end portion 100a, a second end portion 100b opposite first end portion 100a, and a central longitudinal axis L1 extending between first end portion 100a and second end portion 100b. As used herein, the term "longitudinal" may refer to a direction along an axis extending through the lumen of the device while in the tubular configuration, the term "circumferential" may refer to a direction along an axis that is perpendicular to the longitudinal axis and extends around the circumference of the device when in the tubular configuration, and the term "radial" may refer to a direction along an axis that is perpendicular to the longitudinal axis and extends toward or away from the longitudinal axis.

[0062] The device 100 can include an elongate member 102 wound about a longitudinal axis L1 of the device 100. In some embodiments, the elongate member 102 is heat-set into a novel three-dimensional (3D) configuration such that the elongate member 102 is configured to self-expand into a preset configuration. In some embodiments, the elongate member 102 is not configured to be heat-set and / or self-expanding. For example, the elongate member 102 is balloon-expandable. In some embodiments, the elongate member 102 is both balloon-expandable and self-expanding. The elongate member 102 has a first end 102a and a second end 102b opposite the first end 102a along the longitudinal axis L2 of the elongate member 102. The elongate member 102 may comprise a wire, coil, tube, filament, single woven filament, multiple braided filaments, laser cut sheet, laser cut tube, thin film formed via a deposition process, and other suitable elongate structures and / or methods, such as cold working, bending, EDM, chemical etching, water jetting, etc. The elongate member 102 may be formed using materials such as nitinol, stainless steel, cobalt chromium alloy (e.g., 35N LT®, MP35N (Fort Wayne Metals, Fort Wayne, Indiana)), Elgiloy, magnesium alloy, tungsten, tantalum, platinum, rhodium, palladium, gold, silver, or combinations thereof, or one or more polymers, or combinations of polymers and metals. In some embodiments, the elongate member 102 may include one or more drawn filled tube ("DFT") wires, including an inner material surrounded by a different outer material. The inner material may be, for example, a radiopaque material, and the outer material may be a superelastic material.

[0063] 16 includes a single elongate member 102, device 100 may include any number of elongate members 102. A single elongate member, such as a single wire expandable device, may be easier to remove and / or reposition because an operator can grasp the elongate member on one end and pull it through the working channel of the speculum. The elongate member may extend linearly in either a balloon-expandable or self-expanding configuration.

[0064] 16 , the elongate member 102 may be wound around the longitudinal axis L1 of the device 100 into a series of turns or loops 104, four of which are shown in FIG. 16 and individually labeled 104a-104d. Each of the loops 104 may extend around the longitudinal axis L1 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, for example, such that the second end 108 of the first loop 104a is the first end 106 of the second loop 104b. The second end 108 may be positioned approximately 360 degrees from the first end 106 around the longitudinal axis L1 of the device 100. That is, the first and second ends 106, 108 may be positioned at approximately equal circumferential positions relative to the longitudinal axis L1 of the device 100. In some embodiments, device 100 has a circular cross-sectional shape. In other embodiments, device 100 may have other suitable cross-sectional shapes (e.g., oval, square, triangular, polygonal, irregular, etc.). The cross-sectional shape of device 100 may be generally the same or vary along the length of device 100 and / or from loop to loop.

[0065] The expanded cross-sectional dimension of device 100 may be generally constant or may vary along its length and / or between loops. For example, as discussed herein, device 100 may have various cross-sectional dimensions along its length to accommodate different portions of the airway. For example, device 100 may have a first cross-sectional dimension along a first portion configured to be positioned in a more distal portion of the airway (e.g., within a collapsed and / or collapsed terminal bronchioles and / or emphysematous areas of the airway) and a second cross-sectional dimension along a second portion configured to be positioned more proximally (e.g., within an uncollapsed main bronchus and / or another portion). The second portion may be configured to be positioned in a portion of the airway that is less emphysematous than the collapsed distal portion and / or has cartilage within the airway wall (preferably, rings of cartilage, not plates), which may occur at the lobar (generation 2) or segmental (generation 3) level, for example.

[0066] In some embodiments, the expanded cross-sectional dimension of device 100 in its unconstrained (e.g., removed from a catheter or airway constraint) expanded state is oversized relative to the diameter of the native airway lumen. For example, the expanded unconstrained cross-sectional dimension of device 100 can be at least 1.5 times the original (uncollapsed) diameter of the airway lumen in which it is intended to be positioned. In some embodiments, device 100 has an expanded cross-sectional dimension that is about 1.5 to 6 times, 2 to 5 times, or 2 to 3 times the diameter of the original airway lumen. Without being bound by theory, it is believed that expanding the airway lumen to the largest possible diameter without tearing the airway wall will provide the greatest improvement in lung function (e.g., as measured by outflow, FEV, and the like).

[0067] 16 , the elongate member 102 may undulate along its longitudinal axis L2 as it winds around the longitudinal axis L1 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 may be at a different location along the longitudinal axis L1 of the device 100 than at least some of the peaks 110. Additionally or alternatively, at least some of the valleys 112 may be at a different longitudinal location than at least some others of the valleys 112, and / or at least some of the peaks 110 may be at a different longitudinal location than at least some others of the peaks 110.

[0068] 16 and 17, with respect to the first loop 104a in the direction of winding W, the elongate member 102 extends from the first end 106 of the elongate member 102, including the first valley 112a of the first loop 104a, along a first longitudinal direction toward the second end portion 100b of the device 100, to the first apices 110a of the first loop 104a. The elongated member 102 can then extend along a second longitudinal direction opposite to the first longitudinal direction from the first peak 110a to the second valley 112b, along the first longitudinal direction from the second valley 112b to the second peak 110b, along the second longitudinal direction from the second peak 110b to the third valley 112c, along the first longitudinal direction from the third valley 112c to the third peak 110c, and along the second longitudinal direction from the third peak 110c to the fourth valley 112d (which is also the second end 108 of the first loop 104a). Thus, when progressing in the winding direction W around a given loop 104, the loop 104 does not progress consistently 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 is undulating such that along some portions of the loop's length, the loop 104 is progressively closer to the first end portion 100a of the device 100, and along other portions of the loop's length, the loop is progressively closer to the second end portion 100b of the device 100.

[0069] The first and second ends 106, 108 of one of the loops 104 may be generally circumferentially aligned, but the first and second ends 106, 108 are offset longitudinally. 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 generally 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 generally longitudinally aligned with the second peak 110b.

[0070] 16 and 17 each show device 100 with four loops 104 having four peaks 110 and four valleys 112, although in some embodiments, one or more of the loops 104 have more or fewer peaks 110 and / or more or fewer valleys 112. For example, in some embodiments, one or more of the loops 104 have 1, 2, 3, 4, 5, 6, 7, 8, etc. peaks 110 per loop 104 and 1, 2, 3, 4, 5, 6, 7, 8, etc. valleys 112 per loop 104. The loops 104 may have the same or different numbers of peaks 110, and the loops 104 may have the same or different numbers of valleys 112. The circumferential distance (e.g., angular separation) between adjacent ones of the peaks 110 and valleys 112 can be uniform or non-uniform in a given loop 104. In some embodiments, adjacent ones of the peaks 110 and valleys 112 can be spaced apart 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 around the circumference of the device 100. Additionally, the amplitude of the peaks 110 can be the same or different along and / or between a given loop 104, and the amplitude of the valleys 112 can be the same or different along and / or between a given loop 104. Also, the peaks 110 and valleys 112 can have the same or different amplitudes.

[0071] 16 , the portions of the elongate member 102 between adjacent peaks 110 and valleys 112 can be linear, curved, or both. The adjacent portions of the elongate member 102 between two sets of adjacent peaks 110 and valleys 112 can form V-shaped and / or U-shaped structures. 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.

[0072] In some embodiments, elongate members 102 can extend around the circumference of device 100 and / or along longitudinal axis L1 of device 100 without substantially extending radially away from or toward longitudinal axis L1, as shown in FIG. 16 . Additionally, in some embodiments, device 200 can include elongate members 202 that are undulating radially relative to longitudinal axis L1 of device 200. As shown in FIG. 18 , for example, elongate members 202 can form peaks 204 and / or valleys 204 that are located closer to longitudinal axis L1 than intermediate portions of elongate members 202 between peaks 204 and valleys 204. The apex of each “V” can be bent radially inward toward the center of the lumen such that only the longitudinally extending portions of elongate members 202 touch the bronchial wall. Such a configuration can prevent the stent from obstructing mucus flow along the bronchial wall.

[0073] The radial mechanism of expansion allows the expandable device 200 to be easily designed and delivered by both self-expansion and balloon expansion. The zigzag patterns of the devices disclosed herein, including the example shown in FIG. 16, are configured to conform to different diameter airways using a single design, whereas conventional coils are fixed diameter. This is particularly advantageous for achieving gradual airway expansion over time. The expandable device stores expansion potential within the implant design, achieved through beams that flex and establish elastic potential. When geometrically designed in this manner, the expandable device in its balloon-expandable form also has the unique potential to form a coil by expanding the zigzag in a straight line.

[0074] 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.

[0075] Additional examples of devices suitable for use with 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

[0076] 19 is a block diagram providing a general overview of a workflow 1900 for selecting a patient for treatment and planning and monitoring a treatment procedure, according to an embodiment of the present technology. For example, the treatment procedure may be a procedure for treating a patient with a pulmonary disease (e.g., COPD) by placing one or more implanted devices in one or both of the patient's lungs. The device may be any of the embodiments of an endobronchial implant described herein, such as a minimal endobronchial reinforcement implant.

[0077] 19 , workflow 1900 can be divided into a pre-procedure phase 1902, a mid-procedure phase 1904, and a post-procedure phase 1906. In some embodiments, pre-procedure phase 1902 occurs before a patient is diagnosed with a pulmonary disease. Alternatively, pre-procedure phase 1902 can occur after a patient is diagnosed with a pulmonary disease but before the patient receives treatment for the pulmonary disease (e.g., endobronchial implant therapy and / or other therapy).

[0078] The pre-procedural stage 1902 can involve determining whether the patient is a candidate for treatment for a pulmonary disease (block 1908). For example, the treatment can be or include airway treatment for COPD. The airway treatment can include pharmacological treatment (e.g., bronchodilators), interventional treatment (e.g., implants and / or non-implant procedures), or a combination thereof. In some embodiments, the interventional treatment includes steam therapy (e.g., BTVA), administration of a sealant, transbronchial fenestration (e.g., airway bypass stent), 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).

[0079] The process of block 1908, also referred to herein as "patient selection," may involve analyzing patient data (e.g., CT data and / or other imaging data, medical records, interviews, other diagnoses) and assessing the patient's current condition. For example, the patient selection process may determine whether the patient has or is at risk for developing pulmonary disease, and optionally, the disease profile (e.g., type, location, severity). Optionally, patient data obtained over time may be used to track disease progression. The patient selection process may also involve assessing 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.

[0080] 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, location of diseased areas (e.g., tissue destruction, air entrapment), proximity to anatomical structures (e.g., pleura, heart, nodules), proximity to other medical devices, suitable bronchial pathways to the target, alveolar collapse, diaphragmatic movement, location of airflow in response to breath and / or flow patterns, and / or calculated measures of lung volume and / or health status (e.g., RV, TLC, RV / TLC ratio). Optionally, the patient selection process can involve screening patient data for exclusionary characteristics such as tumors, lesions, large bullae, central airway collapse, etc.

[0081] In some embodiments, the patient selection process involves predicting patient outcomes by comparing the patient's disease profile against aggregated, normalized data from other patients. This approach can be used to predict how a patient's quality of life may decline over time (e.g., generate trend lines) and / or how a patient may respond to different treatments (e.g., pharmacological, valve, coil, water vapor, hydrogel adhesive glue, thermal ablation, non-thermal ablation, etc. interventions, surgery) compared to endobronchial implant therapy using minimal endobronchial reinforcement implants.

[0082] 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 related to symptoms and / or quality of life metrics. Based on the patient's responses, the patient engagement utility can issue instructions for imaging (e.g., CT, X-ray, magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), bronchoscopy) and / or testing (spirometry, arterial blood gases). Based on the imaging data and / or test results, the patient engagement utility can generate an interactive patient report that provides a COPD "risk" assessment (e.g., X% chance the patient has COPD) and a referral to a pulmonologist. The patient report can also include information about available treatments and a prediction of the potential benefit of treatment based on the patient's provisional disease profile. For example, patient reports can provide personalized, evidence-based recommendations that are understandable by the general public (e.g., if a patient receives treatment, their quality of life can be restored by X%, and they will be able to climb two flights of stairs versus one flight before), which can foster patient-driven marketing.

[0083] Optionally, the patient engagement utility can send a detailed physician report with the data, analysis, and risk assessment generated for the patient, along with a referral to a pulmonologist. The physician report can include a preliminary diagnosis, recommendations for additional and / or confirmatory testing, and / or predictive analysis related to disease progression, response to medical and / or pharmacological treatment, interventional therapy, surgery, etc. Optionally, the physician report can also include a referral to an interventionist, if appropriate.

[0084] If the patient is determined to be a good candidate for treatment, the pre-procedure stage 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 for the treatment plan. For example, parameters for endobronchial implant therapy (e.g., using minimal endobronchial reinforcement implants) 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., airway, segment, subsegment, lobe, etc., where the implant should be placed), placement route, and / or other treatment solutions (e.g., prescription medications, surgery, other medical devices) that may be used in combination with endobronchial implant therapy. The selection of implant configuration can be based on factors such as the outer diameter, wall thickness, inner lumen diameter, proximity to anatomical structures, and / or airway age of the target airway. The treatment plan may also include information such as the airway where the implant should be placed, how the implant is expected to supply air to the area of ​​the lung, the airway wall structure and size, how the airway wall structure and size are expected to match with the implant, proximity to structures, the mechanical strength of the airway (e.g., radius, wall thickness), and / or tissue type (e.g., fat, muscle, connective tissue, which may indicate implant properties), etc. In some embodiments, the procedure planning process is part of a software tool used by the interventionalist.

[0085] In some embodiments, the procedure planning process involves modeling the patient's lungs, e.g., before and after implant placement. For example, a 3D model of the bronchial tree can optionally be generated from CT data and / or other image data. In some embodiments, CT data can be useful in identifying the most affected lobes (and / or segments, subsegments, etc.) and identifying incomplete fissures, but due to resolution, can be limited in identifying the exact airways that are most impacted and therefore can be combined with higher-resolution imaging modalities such as MRI to enable assessment of specific airways. Also, determining precise airway targets may not be necessary, as any open airway resulting in a connected pathway from the distal to proximal airways with integrity may be sufficient to release trapped air, especially if collateral ventilation further facilitates air movement through the parenchyma.

[0086] The 3D model can be used to model various treatment options and associated outcomes (e.g., placement of a valve at a target location results in an X% increase in FEV1, while placement of a minimal endobronchial reinforcement implant results in a Y% increase in FEV1). The modeling results can be used to select the appropriate therapy type, target location for implant placement (e.g., target airway, segment, 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 optimal implant plans based on factors such as predicted effectiveness at removing trapped air, areas with trapped air, presence of or proximity to incomplete fissures, safety to avoid iatrogenic injury (e.g., damage to the pleura, blood vessels, organs), safety and durability to reduce implant fatigue, safety and effectiveness to reduce the risk of rubbing against adjacent implants, minimizing the number of implants (e.g., for purposes of safety, ease of use, reduced procedure time), minimizing the amount of foreign body (e.g., for safety purposes), 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 lung segments that will have reduced air trapping and, accordingly, contribute less to residual volume compared to baseline (e.g., pre-treatment) measurements, and / or that may contribute more to improved lung function. It is predicted that lung segments with less disease and adjacent to lung segments with substantial air trapping may be able to expand substantially compared to baseline, and, accordingly, may contribute most to improved lung function. Additionally, modeling can be useful in predicting the risk of pneumothorax. Analysis of inspiratory and expiratory CT scans can detect the presence and location of adhesions at risk of tearing from the pleural wall following treatment.This perforation and pneumothorax risk analysis, coupled with predictive modeling, can inform the location and sequence of treatment.

[0087] The intra-procedural phase 1904 can occur during the therapeutic procedure, immediately prior to the procedure (e.g., when preparing for the procedure), and / or immediately after the procedure (e.g., when assessing the immediate outcome of the procedure). The intra-procedural phase 1904 can involve using data generated during the pre-procedural phase 1902 to assist the interventionalist in performing the therapeutic procedure. For example, a 3D model of the patient anatomy generated during the pre-procedural phase 1902 can be integrated with intra-procedural visualization (e.g., fluoroscopy, bronchoscopy) and navigation techniques (e.g., robotic navigation and delivery systems) to improve delivery and targeting of endobronchial implants to the appropriate locations within the lungs.

[0088] In some embodiments, the mid-procedure phase 1904 involves modifying and / or augmenting the treatment plan during the procedure by assessing the outcome of previous steps in the plan and providing recommendations regarding next steps. For example, real-time data characterizing the patient's response to previously placed implants can be used to verify the success of the procedure, evaluate whether the procedure should continue as planned, and / or determine whether modifications should be made (e.g., repositioning the implant, removing the implant, placing additional implants, administering other therapies). The real-time data can include, for example, image data, AI analysis, physician input, pressure and / or flow measurements, physiological metrics (e.g., O saturation, breath metrics), etc. Optionally, the real-time data can be generated by a ventilator or other device that validates correlations between data such as O saturation and airflow and uses such data as a metric of success. This approach can improve efficacy and reduce procedure time.

[0089] The post-procedure phase 1906 can occur after the treatment procedure, such as at least 24 hours, 48 ​​hours, 1 week, 2 weeks, 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 the patient's treatment outcome (block 1912) at one or more time points after the procedure (e.g., at a single time point 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 imaging data, medical records, interviews, other diagnostics) to evaluate the patient's response to treatment, such as whether the patient's condition is improving, stable, or worsening over time. For example, post-procedure pulmonary function metrics can be compared to pre-procedure pulmonary function metrics, such as airway patency, lung / lobar / segmental volumes during inspiration and expiration, RV, TLC, RV / TLC ratio, flow through the targeted airway, and / or flow through adjacent airways. Optionally, the patient's pulmonary status can be assessed by determining a set of pulmonary metrics from patient data (e.g., CT data of the lungs) and then comparing the set of pulmonary metrics to a second set of pulmonary metrics determined from other types of data, such as image data of the same lungs before treatment (e.g., baseline CT images obtained before placement of an endobronchial implant), image data of the same lungs at an earlier time point after treatment (e.g., comparison of 6-month and 12-month follow-up CT images after placement of an endobronchial implant), image data of the same lungs after placement of another endobronchial implant in a location different from the location of the current endobronchial implant, and / or a library of pulmonary image data of patients with COPD. The outcome assessment process can also involve evaluating the status 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.

[0090] In some embodiments, the process of block 1912 may include converting some or all of the patient data into a “virtual bronchoscopy” with which a user can interact. For example, a 3D model of the lungs (including the airways and the installed implant) may be reconstructed from post-procedure imaging of the lungs (e.g., follow-up CT imaging). In some embodiments, developing the virtual bronchoscopy may include, for example, acquiring CT slice images of at least a portion of the lung where the endobronchial implant will be installed, reconstructing a 3D model of the lung based on the CT slice images, and segmenting the 3D model to distinguish various structures within the lung, including cardiopulmonary structures (e.g., 3D airway structures and / or pulmonary vasculature) and the installed endobronchial implant. Additionally or alternatively, the CT slice images may be segmented to distinguish structures within the lung prior to 3D model construction. Such segmentation may be based, for example, at least in part, on density differences between different cardiopulmonary features and the endobronchial implant itself, as represented by different voxel densities in the CT images.

[0091] The 3D model may be displayed on a suitable display (e.g., on a computing device) and / or be navigable by a user in a virtual bronchoscopy interaction. For example, the 3D model may be displayed on a monitor and / or on a wearable device (e.g., glasses, a headset, goggles, etc.). In some embodiments, the 3D model may additionally or alternatively be displayed within an augmented reality (AR) and / or virtual reality (VR) environment. The 3D model may be navigated using a suitable user interface device (e.g., a mouse, a joystick, a handheld controller, a button, a scroll wheel, a scroll ball, etc.).

[0092] In some embodiments, the display of the 3D model may include highlighting or other emphasis of one or more implant features. For example, an implant may be visually indicated using an outline of the implant itself (e.g., a colored line or a thicker line width). As another example, one or more individual implant features may additionally or alternatively be visually indicated using markers associated with the associated implant feature (e.g., markers corresponding to the proximal and distal ends of the endobronchial implant, an outline of the cross-sectional outline of the endobronchial implant at one or more locations along the airway where the endobronchial implant will be placed). In some embodiments, implant outlines and / or markers associated with individual implant features may be toggled on and / or off for display, such as to allow for clearer visualization of certain features within the 3D model.

[0093] Virtual bronchoscopy can provide more detailed information about the lungs, airways, and / or implants than what might otherwise be visually observed during non-virtual bronchoscopy or existing virtual bronchoscopy techniques. For example, in many cases, the placed implant may be configured to blend into the contours of the airway and minimize the induction of a foreign body reaction; therefore, the implant may not be readily visible on the tissue surface during non-virtual bronchoscopy. In contrast, as described above, virtual bronchoscopy using the present technology, which allows navigation of a reconstructed 3D model of the lungs, airways, and / or placed implant, can enable visualization and investigation of the airway, placed implant, implant-airway tissue interactions, and lung as a whole, even beyond the airway surface. In particular, visualization of the placed implant relative to its surroundings can be useful for assessing the patient's treatment outcome after the procedure. In some embodiments, assessment of treatment outcome using virtual bronchoscopy can be performed manually (e.g., by a user operating and navigating the virtual bronchoscopy) and / or using software algorithms, such as trained machine learning algorithms (e.g., similar to those described herein).

[0094] In some embodiments, the outcome assessment process of block 1912 involves predicting the patient's future outcome, such as expected disease progression, therapeutic benefit, implant status, etc. For example, the outcome assessment process can predict whether any post-procedure problems are likely to occur, such as physiological problems (e.g., excess mucus, granulation tissue, and / or fibrosis), as well as problems with the implant (e.g., collapse, displacement, and / or other failure). If any problems are predicted to occur, the post-procedure phase 1906 can generate recommendations for interventions to prevent, mitigate, or otherwise address such problems (block 1914). The process of block 1914, also referred to herein as "intervention recommendations," can result in recommendations for additional therapeutic procedures, such as clean-up bronchoscopy, implant removal, implant exchange, installation of additional implants, consultation with a health care professional, etc.

[0095] In some embodiments, patient data is collected during the post-procedure phase 1906 using at-home devices such as portable spirometers and / or wearable devices (e.g., smart watches with sensors for blood oxygen levels, heart rate, activity (such as steps), altitude, posture, and / or sleep; wearable stethoscopes that analyze lung sounds to detect early signs of disease worsening). Data generated from such devices can be used to track patients over long periods of time (e.g., weeks or months), allowing for remote monitoring and thus reducing the frequency of doctor visits. Optionally, if the data indicates that a potential health concern exists (e.g., the patient's condition suddenly worsens), the patient can be ordered to see a doctor for follow-up.

[0096] 19 (e.g., the patient selection process, the procedure planning process, the outcome assessment process, and / or the intervention recommendation process) can be implemented using one or more software algorithms, such as rule-based algorithms, machine learning algorithms, or a combination 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, local estimation scatterplot smoothing), instance-based algorithms (e.g., k-nearest neighbors, learning vector quantization, self-organizing maps, local 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 Dichotomizer 3 (ID3), C4.5, C5.0, classification and regression trees, chi-squared automatic interaction detection, decision strains, M5), Bayesian algorithms (e.g., Naive Bayes, Gaussian Naive Bayes, Multinomial Naive Bayes, Mean 1 Dependence Estimator, Bayesian Belief Networks, Bayesian Networks, Hidden Markov Models, Conditional Random Fields), clustering algorithms (e.g., k-means, single-linkage clustering, k-median, expectation-maximization, hierarchical clustering, fuzzy clustering, density-based spatial clustering for applications with noise (DBSCAN), point ordering for identifying cluster structure (OPTICS), non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), Gaussian mixture models (GMM)), association rule learning algorithms (e.g., Apriori algorithm, Equivalence Class Transformation (Eclat) algorithm, Frequent Pattern (FP) growing), artificial neural network algorithms The machine learning algorithms described herein include algorithms (e.g., perceptrons, neural networks, backpropagation, 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 autoencoders), dimensionality reduction algorithms (e.g., principal component analysis (PCA), independent component analysis (ICA), principal component regression (PCR), partial least squares regression (PLSR), Sammon mapping, multidimensional scaling, projection pursuit, linear discriminant analysis, mixed discriminant analysis, quadratic discriminant analysis, flexible discriminant analysis), ensemble algorithms (e.g., boosting, bootstrap aggregation, Adaboost, blending, gradient boosting machines, gradient boosting regression trees, random forests), 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.

[0097] 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 may receive image data as input and generate output data that characterizes one or more objects present in the image data. For example, the computer vision algorithm may receive CT data of one or both of a patient's lungs and may identify objects within the lungs, such as anatomical structures, healthy tissue, diseased tissue, implanted devices (e.g., endobronchial implants), etc.

[0098]

[0023] Figure 20 is a flow diagram illustrating a method 2000 for planning a treatment for a patient, according to an embodiment of the present technology. Method 2000 can be performed as part of the pre-procedure stage 1902 of workflow 1900 of Figure 19. Method 2000 can involve receiving patient data, such as history information, medical record information, image data, pulmonary function test (PFT) data, and / or data from other diagnostic techniques. Method 2000 can implement one or more software algorithms that use the patient data to determine whether the patient is a candidate for endobronchial implant therapy (and / or other therapy for pulmonary disease) and, optionally, assist in planning such therapy.

[0099] The interview information may include the patient's responses to one or more interviews, which may be administered by a patient engagement utility as described above. The medical record information may include information from an electronic health record about the patient, such as the patient's name, date of birth, demographic information, height, weight, medical history, family medication history, symptoms, comorbidities, diagnoses, prescription medications, test results, previous treatments and outcomes, etc. The interview information and / or medical record information may provide any of the following information: whether the patient has a cough, whether the patient is a smoker, whether the patient has difficulty breathing, 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 an exacerbation, whether the patient has significant mucus, whether the patient has had a lung infection, and / or the patient's current and / or past drug regimens.

[0100] Image data may be obtained from CT, X-ray (e.g., chest radiography, fluoroscopy), MRI (e.g., 3 HeMRI, 129The CT data may include data generated by any suitable imaging modality, such as Xe MRI, SPECT, bronchoscopy, ultrasound, ventilation-perfusion scan data, photography (e.g., from multiple external cameras for mapping the surface topography of the chest), etc. For example, the CT data may include inspiratory CT data (e.g., acquired at the end of a full inspiration) and / or expiratory CT data (e.g., acquired at the end of a forced expiration). In some embodiments, the image data may be generated after administration of a contrast agent in the patient (e.g., via intrathoracic injection, inhalation, etc.), which may serve to provide contrast enhancement in CT imaging. For example, for lung CT, suitable contrast agents include iodine compounds (e.g., derivatives of diatrizoic acid), barium, radiolabeled albumin for tracking blood flow within the lungs, and labeled gases (e.g., xenon-133) for tracking ventilation within the lungs. The CT data may include a series of 2D cross-sectional images of the patient's anatomy, each having a defined slice thickness and spaced at specific slice intervals (e.g., 10 mm intervals). For example, the 2D cross-sectional images can have a slice thickness of at least 3 mm (e.g., in the range of 3 mm to 10 mm), or can have a slice thickness of less than 3 mm (e.g., 1 mm), such as in the range of 1 mm to 2 mm. In some embodiments, the 2D cross-sectional images can be combined to generate a 3D volumetric CT model of the imaged lung tissue.

[0101] PFT data can include any data characterizing the function of a patient's lungs, such as spirometry data, plethysmography data, exercise test results (e.g., a 6-minute walk test), etc. PFT data can include measurements of any of the following: tidal volume, minute ventilation, 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 (FEV1)), forced expiratory flow, peak expiratory flow, closure volume (CV), inspiratory volume (IC), IC / TLC ratio, and / or diffusing volume for carbon monoxide.

[0102] Other data from other diagnostic techniques can include data from one or more sensors configured to monitor a patient's condition, which may include implanted sensors, non-invasive sensors, wearable sensors, or a suitable combination 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 within the lungs, such as information regarding flow, pressure, granulation tissue, fibrosis, mucus, epithelialization, and / or obstructions.

[0103] Any of the patient data types described herein can be acquired over multiple time points, such as two, three, four, five, ten, twenty, fifty, or more time points. For example, patient data can be acquired over multiple time points spanning seconds, minutes, hours, days, weeks, months, and / or years. In some embodiments, patient data is acquired at two or more of the following time points: before treatment (e.g., before placement of an intrabronchial implant, before administration of a bronchodilator), after treatment (e.g., after placement of an intrabronchial 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). However, in other embodiments, some or all of the patient data can be acquired at a single time point.

[0104] As shown in Figure 20, the patient data can be provided to a first software algorithm. The first software algorithm can be a first machine learning algorithm trained (e.g., via supervised learning) to synthesize the patient data and calculate metrics that characterize the lung properties and / or disease state of the patient. 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 pulmonary metrics that describe the condition of one or both of the patient's lungs. For example, pulmonary metrics may include the following pulmonary parameters: FEV1 (e.g., FEV1), FVC, VC, IC, IC / TLC ratio, functional residual capacity, TLC, diffusing capacity for carbon monoxide, RV, RV / TLC ratio, CV, lobar and / or segmental tissue destruction, lobar and / or segmental air capture, lobar and / or segmental fissure status, lobar and / or segmental fissure completeness, lobar and / or segmental ventilation, pulmonary function (e.g., regional assessment), disease phenotype (e.g., homogeneity / heterogeneity of lobar and / or segmental emphysema, type of emphysema (centrilobular, panlobular, or perilobular, etc.) , location of diseased portion of lung), lobar volume, segment volume, segment location, diaphragm shape, tissue density, opacity, proximity of diseased portion to anatomical structures (e.g., pleura, heart, nodules), proximity of diseased portion to other medical devices, luminal diameter of bronchial segments, airflow mapping, collapsed airways, airway pressure, airway compliance, intrathoracic pressure, airway diameter vs. wall thickness, airway wall deformation, vascular perfusion, parenchymal density, bullae, information localizing disease within the lung (e.g., at the segmental and / or subsegmental level), obstruction score, mucus score, and / or degree of epithelialization. Pulmonary metrics can characterize any of the above pulmonary parameters at a single time point and / or can characterize changes in any of the above pulmonary parameters across multiple 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). Pulmonary metrics can be correlated to whether a patient has a lung disease such as COPD.Optionally, pulmonary metrics can be used to generate a risk assessment for pulmonary disease (eg, the percent likelihood that a patient has COPD).

[0105] Additionally or alternatively, pulmonary metrics can be used to generate a disease "score" that characterizes the severity of a pulmonary disease and / or represents a predictor of patient response to treatment for the pulmonary disease. For example, a disease score can represent the degree of emphysematous destruction, the degree of hyperinflation, and / or the degree of air trapping in one or more regions of the lung. In some embodiments, a pulmonary metric can include one or more disease scores, each corresponding to a distinct region of the lung (e.g., a particular lung, a particular lobe, a particular section, a particular subsection). In some embodiments, a pulmonary metric can include a single disease score that is based, at least in part, on multiple "regional" disease scores, each corresponding to a distinct region of the lung (e.g., a particular lung, a particular lobe, a particular section, a particular subsection). For example, a single disease score can be an average of multiple regional disease scores or based on any suitable calculation incorporating multiple regional disease scores. FIG. 27 is a flow diagram illustrating a method 2700 for evaluating a patient using such disease score pulmonary metrics, including receiving patient data, including CT data of the patient's lungs (block 2710), and generating a pulmonary disease score for a region of interest in the lungs by inputting the patient data into a machine learning algorithm (block 2720).

[0106] In some embodiments, the pulmonary metrics generated by the first software algorithm, as described above, can include a disease phenotype. For example, the distribution of disease scores can be used to characterize the homogeneity or heterogeneity of emphysema. In some embodiments, for example, a difference in disease scores in ipsilateral lung regions (e.g., in different lobes, sections, and / or subsections within a particular lung) below a threshold difference value can correspond to the characterization of homogeneous emphysema for that lung, while a difference in disease scores in ipsilateral lung regions (e.g., in different lobes, sections, and / or subsections within a particular lung) above the threshold difference value can correspond to the characterization of heterogeneous 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%, about 10% to about 20%, or about 10% to about 15%. Additionally or alternatively, in some embodiments, the distribution of disease scores across lungs or lung regions (e.g., across different lobes and / or different segments and / or different subsegments) can be used to determine emphysema type. In particular, such lung metrics of emphysematous tissue destruction at the segmental and / or subsegmental level may advantageously provide further insight into a patient's disease and lung anatomy at a more granular level than analyses performed solely at the lobar level.

[0107] Additionally or alternatively, in some embodiments as described above, the pulmonary metrics produced by the first software algorithm may include lobar and / or segmental fissure status and / or completion, and / or characterization of lobar and / or segmental collateral ventilation. In particular, such pulmonary metrics relating to fissure status, fissure completion, and / or ventilation at the segmental level may advantageously provide further insight into a patient's disease and pulmonary anatomy at a more granular level than analyses performed solely at the lobar level.

[0108] In some embodiments, the lung metrics generated 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 inspiration and / or expiration CT scans. Generally, voxel density is proportional to the attenuation of an X-ray beam through tissue, which generally corresponds to the physical density of the tissue and other substances (e.g., air) being imaged. Voxel density can be expressed in Hounsfield units (HU), which indicate the amount of X-ray attenuation occurring within the tissue corresponding to a particular voxel along the Hounsfield scale. In some embodiments, the first software algorithm can evaluate voxel density across one or more lung regions against one or more voxel density thresholds to characterize emphysema occurring at the lobar and / or segmental level, air trapping at the lobar and / or segmental level, lobar and / or segmental lung volume, amount of perfusion at the lobar and / or segmental level, and / or airway dimensions (e.g., luminal diameter of segmental airways along the length of the airway pathway, airway wall thickness, etc.). The first software algorithm may incorporate different voxel density thresholds associated with different types of CT scans and / or different lung metrics. In some embodiments, the first software algorithm may output lung metrics based on analysis of both inspiratory and expiratory CT scans. In some embodiments, the first software algorithm may output lung metrics based on expiratory CT scans, but not on expiratory CT scans. In some embodiments, the first software algorithm may output lung metrics based on inspiratory CT scans, but not on expiratory CT scans. Additionally or alternatively, the first software algorithm may include other suitable quantitative CT (QCT) techniques, such as those described in further detail herein.

[0109] For example, as shown in FIG. 23A , the first software algorithm can incorporate one or more voxel density thresholds associated with the inspiratory CT scan. In some embodiments where the CT images from the inspiratory CT scan have a slice thickness of about 3 mm or greater (e.g., 3 mm to 10 mm), emphysema can be quantified or otherwise characterized by assessing lung voxels on the inspiratory CT scan that have attenuation below −910 HU (e.g., the percentage of lung voxels below this threshold, the integral of the density values ​​of all voxels below this threshold, and / or other density-based calculations). Emphysema quantification can characterize emphysema disease states at the lobar level (generation 2), segmental level (generation 3), and / or subsegmental level (generation 4+). Additionally or alternatively, this −910 HU threshold can be used to characterize lobar and / or segmental lung volumes. As described in further detail below, such pulmonary metrics can be analyzed by additional software algorithms to predict optimized treatments for particular lobes and / or regions of the lung.

[0110] As further shown in Figure 23B, in some embodiments where the CT image from the inspiratory CT scan has a slice thickness of less than 3 mm (e.g., 1 mm), emphysema can be quantified or otherwise characterized by assessing lung voxels on the inspiratory CT scan that have attenuation below -950 HU (e.g., the percentage of lung voxels below this threshold, the integral of the density values ​​of all voxels below this threshold, and / or other density-based calculations). Quantifying emphysema can characterize emphysema disease states at the lobar and / or segmental level. Additionally or alternatively, this -950 HU threshold can be used to characterize lobar and / or segmental lung volumes and / or lack of perfusion at the lobar and / or segmental level.

[0111] As another example, as shown in FIG. 23B , the first software algorithm can incorporate one or more voxel density thresholds associated with the expiratory CT scan. In some embodiments, air trapping can be quantified or otherwise characterized by assessing lung voxels on the expiratory CT scan that have attenuation below −856 HU (e.g., the percentage of lung voxels below this threshold, the integral of the density values ​​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 pulmonary metrics that characterize one or more pulmonary anatomical features (e.g., measurements of airways within the lung) from image data from the expiratory CT scan. For example, the first software algorithm can generate pulmonary metrics from the expiratory CT scan that include geometric, physical, and / or mechanical properties of the airways, such as airway compliance, intrathoracic pressure, airway diameter versus wall thickness, and / or airway wall deformation. Such properties, in particular, can be measured at the segmental level, which can provide further insight into patient condition for use in determining patient candidacy for treatment, treatment plans, etc. Additionally or alternatively, the first software algorithm can generate pulmonary metrics from the expiratory CT scan, including characterization of lobar and / or segmental lung volumes, and / or identification of collapsed airways (and the severity of their collapse, such as airway diameter) that may be contributing to disease states (e.g., hyperinflation and impaired breathing).

[0112] It should be understood that the density thresholds described above for analyzing CT scans are examples only, and that other suitable thresholds may additionally or alternatively be used to evaluate voxel density data and generate appropriate lung metrics. For example, the density threshold for an inspiratory CT scan may 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).

[0113] The pulmonary metrics generated by the first software algorithm can be provided to a second software algorithm. The second software algorithm can be a second machine learning algorithm trained (e.g., via supervised learning) to analyze the pulmonary metrics and 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 example, 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., a bronchodilator), an interventional treatment (e.g., steam 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.

[0114] In some embodiments, the output of the second software algorithm is the patient's predicted response to treatment. The predicted response can include a prediction of any of the following: FEV (e.g., FEV1), FVC, VC, IC, IC / TLC, functional residual capacity, TLC, diffusing capacity for carbon monoxide, RV / TLC, RV, segmental volumes, mMRC score, SGRQ score (or score for a subset of SGRQ questions), CAT score (or score for a subset of CAT questions), 6-minute walk test results, bicycle dysfunction results, cardiopulmonary exercise testing (CPET) results, patient health metrics (e.g., heart rate, blood pressure, body mass index), patient exercise metrics (e.g., steps taken), patient consultation metrics, quality of life metrics (e.g., ability to breathe), number of implant removals required, time to reintervention, durability of treatment, comorbidities, drug regimens, length of hospital stay, healthcare utilization, and / or costs, and / or changes (e.g., increases or decreases) in any of the above. Optionally, the predicted response can include a comparison of the response to endobronchial implant therapy using minimal endobronchial reinforcement implants versus other therapeutic procedures, such as pharmacological therapy, interventional therapy (e.g., valves, coils, water vapor, hydrogel adhesive glue, thermal ablation, non-thermal ablation), surgical therapy, etc.

[0115] If the patient is predicted to have a satisfactory response to the treatment (e.g., to endobronchial implant therapy with minimal endobronchial reinforcement implants), 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 trained (e.g., via supervised learning) to analyze pulmonary metrics and / or the predicted response and predict 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 in which 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 type, implant geometry, route to target location for implant placement, and / or local treatment solution. For example, implant placement location may be based, at least in part, on the location of dynamic airway collapse as observed in or otherwise determined from expiratory CT data, the severity of lung disease within the lung periphery, the location of the pleural wall, and / or the location of lobar, segmental, and / or subsegmental airways.

[0116] 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 subsegments for implant placement based on pulmonary metrics (e.g., pulmonary metrics provided by the first software algorithm). For example, in identifying an appropriate implant placement location, the third software algorithm can target lobes with a greater amount of emphysema destruction (e.g., a higher disease score) and a larger lobe volume. Additionally or alternatively, the third software algorithm can target airway segments with a greater amount of emphysema destruction (e.g., a higher disease score) and a larger segment volume.

[0117] Additionally or alternatively, in some embodiments, a third software algorithm can analyze pulmonary metrics associated with one or more fissures (e.g., lobar and / or segmental fissures) within the lung and generate a treatment plan for the patient. The analyzed pulmonary metrics can include, for example, identifying the location, shape, and / or completeness of interlobar and / or intralobar intersegmental fissures. In some embodiments, to optimize placement of a minimal number of endobronchial reinforcement implants, it may be advantageous to place the implant in a target location where the fissures are incomplete. While not being bound by any particular theory, it is believed that incomplete fissures allow for greater airflow communication between adjacent segments and / or adjacent lobes, thereby allowing for the release of trapped air from more segments (and / or lobes) using fewer endobronchial reinforcement implants. Thus, the third software algorithm can be configured to identify endobronchial reinforcement implant placement locations that are proximate to one or more incomplete lobar and / or segmental fissures. In contrast, for some interventional procedures (e.g., placement of a one-way stent valve that allows air to flow out of the lung but not into its hyperinflated portion), it may be advantageous to place the implant at a target location where the fissure is intact.

[0118] 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 entirely. For example, as shown in FIG. 28, method 2800 may be similar to method 2000, except that method 2800 omits at least a second software algorithm (e.g., predicting a patient's response to a treatment). For example, method 2800 may include receiving patient data including CT data of the patient's lungs (block 2810), generating a set of pulmonary metrics by inputting the patient data into a first machine learning algorithm (block 2820), identifying potential target regions within the lungs for treatment for the pulmonary disease based, at least in part, on the generated pulmonary metrics (block 2830), and generating a plan for the treatment of the pulmonary disease (block 2840). In some embodiments, blocks 2830 and 2840 may incorporate or be similar to one or more aspects of a third software algorithm. In some embodiments, the first and second software algorithms are combined with or replaced by a single software algorithm that directly predicts patient response from patient data. Optionally, a third software algorithm can generate a treatment plan directly from patient data without requiring the pulmonary metrics and / or predicted responses generated by the first and second software algorithms, respectively.

[0119] In some embodiments, any of the outputs of the first, second, and / or third software algorithms in 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, the patient response predicted by the second software algorithm, and / or the treatment plan generated by the third software algorithm. The report can include a written summary or description, annotated diagram, annotated image, and / or other media (e.g., video) of any such output of the software algorithms of method 2000. For example, the report can include an anatomical illustration representing at least a portion of the lung and / or an image (e.g., a CT image) of at least a portion of the lung annotated with appropriate information. Suitable annotation information can include, for example, airway measurements (e.g., lumen diameter, wall thickness, etc.) along one or more segments of the airway, disease scores for selected regions such as lobes, segments, and / or subsegments of the lung, lobes, segments, and / or suggested implant placement locations of the lung having disease scores above a predetermined threshold. In some embodiments, such annotations may be descriptive (e.g., using text or numbers) and / or otherwise visualized, such as with color coding or line thickness (e.g., thicker lines to highlight airway walls in a lung segment or subsegment of interest). For example, the lung illustration or image may include a map or other visualization of the airways within the lung and an identification of lung lobes or segments having a disease score above a predetermined threshold (e.g., using color coding or line thickness). As another example, the lung illustration or image may include a map or other visualization of the airways within the lung and an identification of one or more suggested implant placement locations (e.g., using an implant outline, highlighting airway walls in targeted airway segments using color or line thickness, etc.). In some embodiments, the report may be communicated to the patient medical record (e.g., electronic medical record) for reference.

[0120] Method 2000 can be implemented using any suitable system or device, in some embodiments, some or all of the processes of method 2000 are implemented as computer-readable instructions (e.g., program code) configured to be executed by one or more processors of a computing device.

[0121] 21 is a flow diagram illustrating a method 2100 for assessing a patient's treatment outcome in accordance with an embodiment of the present technology. Method 2100 can be implemented as part of the post-procedure stage 1906 of workflow 1900 of FIG. 19. Although method 2100 is described herein in connection with assessing the outcome of endobronchial implant therapy, in other embodiments, method 2100 can be modified for use in assessing the outcome of other types of airway treatments (e.g., pharmacological treatments, non-implant interventional treatments).

[0122] Method 2100 may involve receiving patient data such as interview information, medical record information, imaging 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 techniques, and / or any of the other patient data types described herein. Some or all of the patient data may be obtained at multiple time points, as discussed above with respect to FIG. 20. Method 2100 may implement one or more software algorithms that use the patient data to analyze whether a therapeutic procedure was successful and, optionally, suggest additional procedures that may further improve patient outcomes.

[0123] Patient data (e.g., history information, PFT data, image data, bronchoscopy data, and / or data from other diagnostic techniques) can be provided to a fourth software algorithm. The fourth software algorithm can be a fourth machine learning algorithm trained (e.g., via supervised learning) to synthesize patient data and calculate lung metrics and / or implant metrics. In some embodiments, the fourth software algorithm can additionally or alternatively include other suitable automated processes. For example, pulmonary metrics may include the following: FEV1 (e.g., FEV1), FVC, VC, IC, IC / TLC ratio, functional residual capacity, TLC, diffusing capacity for carbon monoxide, RV, RV / TLC ratio, CV, lobar and / or segmental tissue destruction, lobar and / or segmental air capture, lobar and / or segmental fissure status, lobar and / or segmental fissure completeness, lobar and / or segmental ventilation, pulmonary function, disease phenotype (e.g., emphysema homogeneity / heterogeneity, type of emphysema (e.g., centrilobular, panlobular, or perilobular emphysema), location of affected lung), lobar volume, segmental volume, segmental location, diaphragm shape, tissue density, opacity, anatomical The set of pulmonary metrics can represent the patient's pulmonary condition after placement of one or more endobronchial implants (e.g., minimal endobronchial reinforcement implants), such as any of the following: proximity of diseased segments to structures, proximity of diseased segments to other medical devices, luminal diameter of bronchial segments, airflow mapping, collapsed airways, airway pressure, airway compliance, intrathoracic pressure, airway diameter vs. wall thickness, airway wall deformation, vascular perfusion, parenchymal density, bullae, information localizing disease within 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 the lumen of the implant, etc.). The set of pulmonary metrics can characterize the pulmonary condition at a single time point and / or can characterize changes in the pulmonary condition over multiple time points (e.g., before and after endobronchial implant therapy, before and after administration of bronchodilators, before and during exercise).Additionally or alternatively, pulmonary metrics can be used to generate a disease “score” that characterizes the severity of pulmonary disease that may remain in one or more regions of the lung following treatment and / or represents an estimate of the patient response to treatment for pulmonary disease. In some embodiments, pulmonary metrics can include one or more disease scores, each corresponding to a distinct region of the lung (e.g., a particular lung, a particular lobe, a particular section, a particular subsection). In some embodiments, pulmonary metrics can include a single disease score that is based, at least in part, on multiple “regional” disease scores, each corresponding to a distinct region of the lung (e.g., a particular lung, a particular lobe, a particular section, a particular subsection). For example, the single disease score can be an average of multiple regional disease scores or based on any suitable calculation incorporating multiple regional disease scores. Method 2700 as shown in FIG. 27 for assessing a patient using such disease score pulmonary metrics (described above with respect to the pre-procedure assessment) can additionally or alternatively be performed as a post-procedure assessment.

[0124] Implant metrics can represent the status of each endobronchial implant after placement in the lung, such as any of the following characteristics: implant location, distance between the distal end of the implant and the 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), indications of implant expansion and / or collapse (e.g., implant biasing or “pancaking” in the longitudinal direction), such as implant loop pitch or the angle of the implant loop profile relative to the longitudinal axis of the implant, implant position relative to other placed implants, movement of one or more implants between inspiration and expiration, implant obstruction, and / or implant dislodgement. Implant metrics can represent the status of the implant at a single time point or can represent changes in the status of the implant over multiple time points. For example, a fourth software algorithm that identifies changes in implant properties from CT imaging taken over various time points can be useful for understanding impact on patient benefit and / or implant interactions.

[0125] For example, in some embodiments, the fourth software algorithm can analyze follow-up expiratory CT scans and generate pulmonary metrics including geometric, physical, and / or mechanical properties of the airways, such as airway compliance, intrathoracic pressure, airway diameter versus wall thickness, and / or airway wall deformation. Such properties can be measured, particularly at the segmental level, which can provide further insight into patient condition for use in determining the patient's response to therapy (compared to such information determined only at the lobar level). Additionally or alternatively, the fourth software algorithm can generate pulmonary metrics including identification of any remaining collapsed airways (and the severity of their collapse, such as airway diameter). Further, in some embodiments, the fourth software algorithm can generate implant metrics from the expiratory CT scans that characterize the condition of the installed implant, 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 in lung anatomy such as lobe volume, diaphragm shape, density, opacity, and / or zone location can be extracted to delineate relationships between patient anatomy, implant orientation, and treatment outcome.

[0126] In some embodiments, method 2100 uses a fifth software algorithm that analyzes patient data (e.g., history information, PFT data, image data, bronchoscopy data, medical record information, and / or data from other diagnostic techniques), pulmonary metrics, and / or implant metrics to determine the patient's response to endobronchial implant therapy. The fifth software algorithm may be a fifth machine learning algorithm trained (e.g., via supervised learning) to compare pre- and post-procedure pulmonary metrics, implant metrics, and / or patient data and quantify the benefit to the patient. In some embodiments, the fifth software algorithm may additionally or alternatively include other suitable automated processes. The output of the fifth software algorithm may be FEV1 (e.g., FEV1), FVC, VC, IC, IC / TLC, functional residual capacity, TLC, diffusing capacity for carbon monoxide, RV / TLC, RV, segmental volumes, mMRC score, SGRQ score or a subset thereof, CAT score or a subset thereof, 6-minute walk test results, bicycle dysfunction results, CPET results, patient health metrics (e.g., heart rate, blood pressure, body mass index), patient exercise metrics (e.g., steps walked), patient consultation metrics, quality of life metrics (e.g., ability to take a breath), number of implant removals required, time to reintervention, durability of treatment, comorbidities, drug regimens, length of hospital stay, healthcare utilization, and / or costs, and / or indicators of the degree of patient response such as a change (e.g., increase or decrease) in any of the above.

[0127] In some embodiments, for example, the fifth software algorithm can include analyzing pre- and post-procedure CT scans, which can provide additional insight into the effectiveness and / or efficacy of the treatment. For example, as shown in FIG. 24 , the fifth software algorithm can analyze changes in lung volume, lobar volume, segmental volume, fissure position (e.g., status and / or completeness), diaphragm shape, central airway shape (e.g., collapsed central airways due to hyperinflation compared to normal central airways due to the release of trapped air after the procedure), etc. based on information derived from the CT scans (e.g., voxel density assessment, etc.). Additionally or alternatively, the fifth software algorithm can include other suitable quantitative CT (QCT) techniques, such as those described in further detail herein.

[0128] In some embodiments, method 2100 uses a sixth software algorithm that analyzes patient data (e.g., interview information, PFT data, image data, bronchoscopy data, medical record information, and / or data from other diagnostic techniques), pulmonary metrics, implant metrics, and / or determined responses to predict patient outcomes following endobronchial implant therapy. The sixth software algorithm may be a sixth machine learning algorithm trained (e.g., via supervised learning) to analyze post-procedure data metrics over time and predict future outcomes. In some embodiments, the sixth software algorithm may additionally or alternatively include other suitable automated processes. For example, the predicted outcome may include whether the patient's prognosis is likely to improve, remain stable, or worsen over time. As another example, predicted outcomes can include prediction of post-procedure problems such as excessive mucus, excessive granulation tissue, excessive fibrosis, implant collapse, implant migration, implant failure, implant invagination, implant obstruction, implant expectoration, inadequate lung function, pneumothorax, infection, pneumonia, and / or hospitalization.

[0129] In some embodiments, the sixth software algorithm determines one or more interventions to prevent, mitigate, or otherwise address the predicted problem, such as a clean-up bronchoscopy, retrieval and / or removal of one or more implants, exchange of one or more implants, repositioning of one or more implants, expansion of one or more implants, placement of one or more additional implants (e.g., placing one or more additional implants in other lobes or the contralateral lung), consultation with a health care professional, and / or additional therapeutic procedures (e.g., prescription medications, surgery, other medical devices). For example, if a large amount of mucus is predicted, a clean-up bronchoscopy at a scheduled interval can be recommended. If excessive granulation tissue and / or fibrosis is predicted, implant removal can be recommended. If implant collapse is predicted, balloon dilation can be recommended. If inadequate improvement in FEV1 is predicted, placement of an additional implant can be recommended. If implant failure is predicted, implant removal or replacement can be recommended. If hospitalization is anticipated, a preventative physician visit may be recommended.

[0130] 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 entirely. Optionally, the fifth software algorithm can determine patient response directly from patient data without requiring pulmonary metrics and / or implant metrics generated by the fourth software algorithm, respectively. Similarly, the sixth software algorithm can predict patient outcome directly from patient data without requiring pulmonary metrics and / or implant metrics generated by the fourth software algorithm and / or without patient response determined by the fifth software algorithm.

[0131] Similar to that described with respect to method 2000, in some embodiments, any of the outputs of the fourth, fifth, and / or sixth software algorithms of 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, the patient response to endobronchial implant therapy (e.g., quantified patient benefit) generated by the fifth software algorithm, and / or the predicted future patient outcomes and / or suggested interventions generated by the third software algorithm. The report can include a written summary or description, annotated diagram, annotated image, and / or other media (e.g., video) of any such output of the software algorithms of method 2100. For example, the report can include an anatomical illustration representing at least a portion of a lung and / or images (e.g., CT images) of at least a portion of the treated lung and / or implant placed within the treated lung, annotated with appropriate information. Suitable annotation information may include, for example, airway measurements (e.g., lumen diameter, wall thickness, etc.) along one or more segments of the airway; disease scores for selected regions, such as lobes, segments, and / or subsegments of the lung; and lobes, segments, and / or implant locations of the lung that have disease scores above a predetermined threshold. In some embodiments, such annotations may be descriptive (e.g., using text or numbers) and / or otherwise visualized, such as with color coding or line thickness (e.g., thicker lines to highlight airway walls in the lung segment of interest). For example, a lung illustration or image may include a map or other visualization of the airways within the lung and an identification of lobes, segments, or subsegments that have disease scores above a predetermined threshold (e.g., using color coding or line thickness). As another example, a lung illustration or image may include a map or other visualization of the airways within the lung and an identification of one or more implant locations (e.g., using an implant outline, highlighting airway walls in the airway segment using color or line thickness, etc.).As another example, the report may include access to a virtual bronchoscopy based on a reconstructed 3D model of the treated lung and the installed implant, as described herein. In some embodiments, the report may be communicated to the patient medical record (e.g., electronic medical record) for reference.

[0132] Method 2100 can be implemented using any suitable system or device. In some embodiments, some or all of the processes of method 2100 are implemented as computer-readable instructions (e.g., program code) configured to be executed by one or more processors of a computing device.

[0133] FIG. 22 is a flow diagram illustrating a method 2200 for updating the software algorithms of FIGS. 20 and 21 in accordance with an embodiment of the present technology. Method 2200 can involve obtaining data regarding patient response to endobronchial implant therapy from a plurality of different patients. The data may be generated by the fifth software algorithm of 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 patient responses to treatment into various categories, such as “best case,” “worst case,” “intermediate case,” etc. In some embodiments, the seventh software algorithm can additionally or alternatively include other suitable automated processes. The output of the seventh software algorithm can be correlated to other types of data, such as indicators of treatment success (e.g., pulmonary 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.

[0134] For example, the correlation between patient response and pulmonary metrics (and / or other indicators of treatment success) can be used to update the second software algorithm (FIG. 20), the third software algorithm (FIG. 20), the fifth software algorithm (FIG. 21), and / or the sixth software algorithm (FIG. 21). In embodiments in which these software algorithms are or include machine learning algorithms, the correlation can be used as training data for the machine algorithms (e.g., for unsupervised learning). The correlation can provide information to the algorithms regarding pulmonary metrics and / or other indicators of treatment success that are associated with treatment procedure success versus procedure failure.

[0135] 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 ), the fourth software algorithm ( FIG. 21 ), the fifth software algorithm ( FIG. 21 ), and / or the sixth software algorithm ( FIG. 21 ). In embodiments in which 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 provide information to the algorithms regarding observations from the CT data and / or other image data associated with therapeutic procedure success versus procedure failure. Optionally, observations from the CT data and / or other image data associated with therapeutic procedure success and / or failure can be used to update (e.g., train) the second software algorithm ( FIG. 20 ), the third software algorithm ( FIG. 20 ), the fifth software algorithm ( FIG. 21 ), and / or the sixth software algorithm ( FIG. 21 ) to determine new pulmonary metrics and / or other indicators of therapeutic success that may be useful in predicting therapeutic procedure success or failure.

[0136] In further examples, the correlation between the patient response and the medical record information can be used to update the second software algorithm (FIG. 20), the fifth software algorithm (FIG. 21), and / or the sixth software algorithm (FIG. 21). In embodiments in which these software algorithms are or include machine learning algorithms, the correlation can be used as training data for the machine algorithms (e.g., for unsupervised learning). The correlation can provide information to the algorithms regarding information from the patient medical record that is associated with therapeutic procedure success versus procedure failure.

[0137] Any of the software algorithms described herein (e.g., Algorithms 1-7 in Figures 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 prior patients, which may include data from the same patient at an earlier time point as well as data from other patients. This historical patient data may also be sourced from a repository, which may include patients with pulmonary 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), steam therapy, or pharmacological treatments (e.g., inhaled bronchodilators). For example, the software algorithms may be updated for predictive power using hierarchical Bayesian modeling, where prior probabilities in the Bayesian scheme can be derived from historical or repository patient data. V. Platform-independent quantitative CT (QCT)

[0138] As described in further detail herein, imaging data from a CT scan 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 according to the present technology may include one or more software algorithms (e.g., machine learning algorithms or other automated algorithms) for identifying and characterizing quantitative image features. For quantitative image features to effectively serve as biomarkers for disease diagnosis and / or assessment of implant therapy (e.g., placement of an endobronchial reinforcement implant), the quantitative image features must be reproducible. However, scanning techniques, parameters, and other scanning device algorithms (collectively also referred to herein as "CT scanning parameters") can have a significant impact on the reproducibility of QCT output. Different CT scanning devices (e.g., different scanning device manufacturers) may vary, and any particular CT scanning device may also be operated using different scanning parameters, thereby affecting the results of the QCT analysis. For example, the radiation dose applied during the scan and / or the reconstruction algorithm used to generate the tomographic image from the acquired x-ray projection data can affect the results of the QCT analysis. These and further examples of CT scanning parameters that can affect the QCT analysis are listed below in Table 1. [Table 1-1] [Table 1-2] [Table 1-3]

[0139] In some embodiments, it may be advantageous to compensate for variance in QCT results due to CT scan parameters, such as by applying at least one correction factor. The correction factor can function to quantify the impact of differences in one or more CT scan parameters on the resulting QCT analysis. Generally, such correction factors can be used to obtain normalized (e.g., standardized) results to more effectively assess lung metrics and / or implant metrics, such as in a more objective manner that is CT platform independent. For example, a correction factor can be used to normalize the density of voxels associated with a particular x-ray attenuation threshold (e.g., −950 HU, −910 HU, etc.) in the CT data. A single correction factor can be associated with a single individual CT scan parameter or with multiple CT scan parameters used in combination during the CT scan. Furthermore, compensating 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.

[0140] In some embodiments, the correction factor associated with one or more particular CT scanning parameters may be empirically determined. For example, CT scans of a phantom (or multiple phantoms) of known density may be repeatedly obtained under different predetermined imaging conditions (e.g., known sets of CT scanning parameters). The effect on voxel density (and / or QCT analysis results) caused by changes in any one particular CT scanning parameter may be empirically determined by repeatedly obtaining CT scans of the phantom while modulating the CT scanning parameter in a known manner across different CT scans. For example, to assess the effect on voxel density caused by changes in tube voltage, the tube voltage may be incrementally adjusted by a known amount between successive CT scans of the phantom. The relationship between the CT scanning parameter (and / or its changes) and the resulting voxel density may be empirically determined (e.g., described by a mathematical formula). Additional CT scanning parameters may similarly be individually modulated in a known manner to determine the relationship between other CT scanning parameters (and / or their changes) and the resulting voxel density.

[0141] 26 is a flow diagram illustrating a method 2600 for normalizing one or more QCT results, such as pulmonary metrics and / or implant metrics, for a patient (e.g., a patient having or suspected of having a pulmonary disease). Method 2600 can be incorporated into other methods described herein, such as method 1900 (e.g., for generating pulmonary metrics and / or implant metrics of interest in pre-procedural, intra-procedural, and / or post-procedural phases), method 2000 (e.g., for generating pulmonary metrics of interest), method 2100 (e.g., for generating pulmonary metrics and / or implant metrics of interest), and / or method 2200. 26 , method 2600 may include receiving first CT data related to a patient, the first CT data being acquired under predetermined imaging conditions (block 2610), converting 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 the CT data may function to quantify the effects of known imaging conditions, such as CT scanning parameters. Thus, in some variations, the metrics (e.g., lung metrics and / or implant metrics) generated in block 2630 may be more objective and CT platform independent.

[0142] Receiving first CT data for a patient in block 2610 serves to acquire initial image data for the patient obtained under predetermined and / or otherwise known imaging conditions. The CT data may include inspiratory CT data (e.g., acquired at the end of a full inspiration) and / or expiratory CT data (e.g., acquired at the end of a forced exhalation). The CT data may be generated through a CT scan acquisition process at pre-procedure, intra-procedure, and / or post-procedure stages. The predetermined imaging conditions may include any one or more of the CT scan parameters listed in Table 1 (e.g., slice thickness, slice spacing, tube potential, pitch, tube current, reconstruction algorithm, the presence of any contrast agent administered to the patient, contrast agent type / dosage, contrast agent administration modality, etc.). While these imaging conditions (under which the scan is acquired) are predetermined, each CT scan parameter may not necessarily be specifically known or identifiable. For example, some CT scanning parameters (e.g., reconstruction algorithms and / or other algorithms used to determine fissure integrity) may simply be associated with a particular CT scanning machine design or CT vendor that operates in a manner typical of that machine design or vendor, while other CT scanning parameters (e.g., slice thickness, slice spacing) may be both predetermined and known.

[0143] Transforming the first CT data to the second CT data in block 2620 serves to convert the first CT data into a normalized (e.g., standardized) data set from which more objective metrics can be analyzed (e.g., in block 2630). For example, the second CT data may include other variations, such as voxel density and / or CT scanning device model that are independent of differences in CT scan parameters (e.g., scan acquisition parameters, dose modulation parameters, image reconstruction algorithms, contrast administration, etc.), and / or the particular CT vendor providing the CT imaging services. One or more correction factors can be applied to the first CT data to obtain second, more normalized CT data. As described above, a single correction factor can be associated with a single individual CT scan parameter or can be associated with multiple CT scan parameters used in combination during the CT scan.

[0144] In some embodiments, the correction factor can be associated with multiple groups of related CT scan parameters. For example, the correction factor can be associated with some or all of the CT scan parameters related to scan acquisition as listed in Table 1, or include some or all of the CT scan parameters related to dose modulation as listed in Table 1, or include some or all of the CT scan parameters related to image reconstruction as listed in Table 1, or include some or all of the CT scan parameters related to contrast agents as listed in Table 1. In some embodiments, the correction factor can be associated with multiple groups of related CT scan parameters. Additionally or alternatively, in some embodiments, the correction factor can be associated with a particular CT scan machine design (e.g., scanner model) and / or a particular supplier providing CT imaging services.

[0145] Generating metrics associated with the patient based on the second CT data in block 2630 serves 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 algorithms. 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.).

[0146] Pulmonary metrics can include any of the pulmonary metrics described herein, such as those described with respect to methods 1900, 2000, 2100, and / or 2200. For example, pulmonary metrics can include the following: FEV (e.g., FEV1), FVC, VC, IC, IC / TLC ratio, functional residual capacity, TLC, diffusing capacity for carbon monoxide, RV, RV / TLC ratio, CV, lobar and / or segmental tissue destruction, lobar and / or segmental air capture, lobar and / or segmental fissure status, lobar and / or segmental fissure completeness, lobar and / or segmental ventilation, lung function, disease phenotype (e.g., emphysema homogeneity / heterogeneity, type of emphysema (such as centrilobular, panlobular, or perilobular emphysema), location of affected portions of lung), lobar volume, segmental volume, segmental location, diaphragm shape, tissue density, opacity, anatomical The set of pulmonary metrics can represent the patient's pulmonary condition after placement of one or more endobronchial implants (e.g., minimal endobronchial reinforcement implants), such as any of the following: proximity of diseased segments to structures, proximity of diseased segments to other medical devices, luminal diameter of bronchial segments, airflow mapping, collapsed airways, airway pressure, airway compliance, intrathoracic pressure, airway diameter vs. wall thickness, airway wall deformation, vascular perfusion, parenchymal density, bullae, information localizing disease within 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 the lumen of the implant, etc.). The set of pulmonary metrics can characterize the pulmonary condition at a single time point and / or can characterize changes in the pulmonary condition over multiple time points (e.g., before and after endobronchial implant therapy, before and after administration of bronchodilators, before and during exercise). Additionally or alternatively, pulmonary metrics can be used to generate a disease "score" that characterizes the severity of pulmonary disease that may remain in one or more regions of the lung following treatment. Pulmonary metrics can include one or more disease scores, each characterizing the severity of pulmonary disease in a distinct region of the lung (e.g., a particular lung, a particular lobe, a particular segment, a particular subsegment).

[0147] 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 the status of each endobronchial implant after placement in the lung, such as any of the following characteristics: implant location, distance between the distal end of the implant and the 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), indications of implant expansion and / or collapse (e.g., biasing or "pancaking" of the implant in the longitudinal direction), such as the pitch of the implant loop or the angle of the implant loop profile relative to the longitudinal axis of the implant, implant position relative to other placed implants, movement of one or more implants between inspiration and expiration, implant occlusion, invagination, and / or implant dislodgement. The implant metrics can represent the status of the implant at a single time point, or can represent changes in the status of the implant over multiple time points.

[0148] In some embodiments, various methods according to the present technology may further include analyzing generated metrics associated with the patient based on the second CT data. Such analysis may be performed using one or more software algorithms, such as trained machine learning algorithms or other automated algorithms. For example, lung metrics based on the second CT data may 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 data may be analyzed in an outcome assessment process and / or an intervention recommendation process.

[0149] 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 metrics generated based on the second CT data. For example, pulmonary 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, pulmonary 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

[0150] 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 uses and / or approaches, such as identifying and / or treating tracheobronchomalacia (TBM), excessive dynamic airway collapse (EDAC), or benign prostatic hyperplasia (BPH). Other embodiments in addition to those described herein are also within the scope of the technology. Additionally, some other embodiments of the technology may have different configurations, components, or procedures than those described herein. Those skilled in the art will therefore understand accordingly that the technology may have other embodiments with additional elements, or may have other embodiments without some of the features shown and described above with reference to FIGS. 1-28.

[0151] The various processes described herein may be implemented, partially or completely, using program code, which includes instructions executable by one or more processors of a computing system to implement specific logical functions or steps in the process. The program code may be stored on any type of computer-readable medium, such as a storage device, including a disk or hard drive. The computer-readable medium containing the code or portions of the code may include any suitable medium known in the art, such as a non-transitory computer-readable storage medium. The computer-readable medium may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for information storage and / or transmission, 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 disk (DVD), or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage, or other magnetic storage device, solid-state drive (SSD), or other solid-state storage device, or any other medium that can be used to store desired information and that can be accessed by a system device.

[0152] The description of embodiments of the present technology is not intended to be exhaustive or to limit the present technology to the precise form disclosed above. Where the context allows, singular or plural terms may also include plural or singular terms, respectively. Specific embodiments of the present technology and examples thereof are described above for illustrative purposes, but as one skilled in the art will recognize, various equivalent modifications are possible within the scope of the present technology. 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.

[0153] As used herein, the terms "generally," "substantially," "about," and similar terms are used as terms of approximation, not degree, and are intended to account for inherent variations in measurements or calculations that will be recognized by one of ordinary skill in the art.

[0154] Also, unless the word "or," in reference to a list of two or more items, is expressly limited to mean only a single item exclusively from the other items, the use of "or" in such a list is to be construed as including (a) any single item in the list, (b) all of the items in the list, or (c) any combination of items in the list. As used herein, the phrase "and / or," such as in "A and / or B," refers to A only, B only, and A and B. Additionally, the term "comprising" is used throughout to mean the inclusion of at least the recited features, so as not to exclude any greater number of the same features and / or other features of additional types.

[0155] To the extent that any material incorporated by reference herein conflicts with the present disclosure, the present disclosure will control.

[0156] Also, while specific embodiments have been described herein for illustrative purposes, it should be understood that various modifications can be made without departing from the present technology. Furthermore, while advantages associated with certain embodiments of the present technology have been described in the context of those embodiments, other embodiments may also exhibit such advantages, and not all embodiments necessarily need to exhibit such advantages to fall within the scope of the present technology. Thus, the present disclosure and associated technology can encompass other embodiments not expressly shown or described herein.

Claims

1. 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 the patient's lungs; generating a set of pulmonary metrics by inputting the patient data into a first machine learning algorithm, the set of pulmonary metrics representing the pulmonary condition of the patient; predicting the patient's response to a treatment for the pulmonary disease by inputting the set of pulmonary metrics into a second machine learning algorithm; assessing whether the patient is a candidate for the treatment for the pulmonary disease based on the predicted response; A method comprising:

2. 10. The method of claim 1, wherein the patient data comprises one or more of the following: interview information, medical record information, magnetic resonance imaging (MRI) data, single photon emission computed tomography (SPECT) data, bronchoscopy data, ventilation-perfusion ratio data, pulmonary function test data, chest radiography data, fluoroscopy data, photographs, or sensor data.

3. The method of claim 1 or 2, wherein the CT data comprises expiratory CT data.

4. The method of claim 3 , wherein the CT data comprises inhalation CT data.

5. The method of any one of claims 1-4, wherein the patient data comprises data acquired at multiple different time points.

6. 6. The method of any one of claims 1-5, wherein the set of pulmonary metrics correlates to whether the patient has at least one of chronic obstructive pulmonary disease (COPD), severe emphysema, or severe emphysema with hyperinflation.

7. The set of pulmonary metrics characterizes one or more pulmonary parameters, the one or more pulmonary parameters being the following: forced expiratory volume in 1 second, forced vital capacity, vital capacity, inspiratory volume, inspiratory volume / total lung volume ratio, functional residual capacity, total lung capacity, diffusing volume for carbon monoxide, residual volume, residual volume / total lung volume ratio, occlusion volume, lobar and / or segmental tissue destruction, lobar and / or segmental air capture, lobar and / or segmental fissure status, lobar and / or segmental fissure completeness, lobar and / or segmental ventilation, lung function, lobar and / or segmental emphysema homogeneity / heterogeneity, 7. The method of any one of claims 1-6, comprising one or more of emphysema type, location of diseased portion of the lung, lobar volume, segment volume, segment location, diaphragm shape, tissue density, opacity, proximity of diseased portion to anatomical structures, proximity of diseased portion to other medical devices, luminal diameter of bronchial segments, airflow mapping, collapsed airways, airway pressure, airway compliance, intrathoracic pressure, airway diameter vs. wall thickness, airway wall deformation, vascular perfusion, parenchymal density, bullae, information locating disease in the lung, obstruction score, mucus score, or degree of epithelialization.

8. The method of any one of claims 1-7, wherein the set of pulmonary metrics comprises at least one disease score characterizing the severity of pulmonary disease in the patient.

9. 10. The method of claim 8, wherein the at least one disease score represents a predictor of patient response to the treatment for the pulmonary disease.

10. 10. The method of claim 8 or 9, wherein the set of pulmonary metrics comprises a plurality of disease scores, each corresponding to a distinct lobe, segment, or sub-segmental region of the patient's lung.

11. 10. The method of claim 8 or 9, wherein the set of pulmonary metrics comprises a single disease score based on multiple regional disease scores, each corresponding to a distinct lobe, segment, or sub-segmental region of the patient's lung.

12. The method of claim 11 , wherein the single disease score is an average of the multiple local disease scores.

13. The method of any one of claims 8-12, wherein the at least one disease score represents the degree of at least one of air trapping or hyperinflation in the patient's lungs.

14. The method of any one of claims 7-13, wherein the set of pulmonary metrics characterizes the change in at least one of the one or more pulmonary parameters across multiple time points.

15. 15. The method of claim 14, wherein the plurality of time points comprises two or more of the following: before intrabronchial implant therapy, after intrabronchial implant therapy, before administration of a bronchodilator, after administration of a bronchodilator, before exercise, or during exercise.

16. 16. The method of any one of claims 1-15, 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 volume, inspiratory volume / total lung volume ratio, functional residual capacity, total lung capacity, diffusing capacity for carbon monoxide, residual volume, residual volume / total lung volume ratio, segmental volume, mMRC score, SGRQ score or a subset thereof, CAT score or a subset thereof, 6-minute walk test results, bicycle exercise dysfunction results, cardiopulmonary exercise testing (CPET) results, patient health metrics, patient exercise metrics, patient encounter metrics, number of implant removals required, time to reintervention, durability of treatment, quality of life score, body mass index, comorbidities, drug regimen, length of hospital stay, healthcare utilization, or costs.

17. 17. The method of any one of claims 1-16, wherein the treatment comprises airway treatment for COPD.

18. 20. The method of claim 17, wherein the airway treatment comprises a pharmacological treatment.

19. 19. The method of claim 17 or 18, wherein the airway treatment comprises an interventional treatment.

20. 20. The method of claim 19, wherein the interventional treatment comprises one or more of the following: steam therapy, administration of a sealant, transbronchial fenestration, placement of an endobronchial coil, placement of an endobronchial valve, or placement of a minimal endobronchial reinforcing implant.

21. 21. The method of claim 20, wherein the interventional procedure comprises the placement of the minimal endobronchial reinforcement implant.

22. The method of any one of claims 1-21, further comprising generating a plan for said treatment if said patient is a candidate for said treatment associated with said lung disease.

23. 23. The method of claim 22, wherein the plan is generated by inputting one or more of the predicted response or the set of pulmonary metrics into a third machine learning algorithm.

24. 24. The method of claim 22 or 23, 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, route to target location, or local treatment solution.

25. 25. The method of claim 24, wherein the implant placement location is based, at least in part, on one or more of the following: location of dynamic airway collapse as determined from expiratory CT data, severity of disease in peripheral regions of the lung, location of the patient's pleural wall, or location of lobar, segmental, and / or subsegmental airways.

26. 26. The method of any one of claims 1-25, further comprising generating a report, the report comprising a summary of one or more of the following: at least a portion of the set of pulmonary metrics, the predicted response, the assessment of whether the patient is a candidate for the treatment, or the generated plan for the treatment.

27. 27. The method of any one of claims 1-26, 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.

28. 28. The method of claim 27, wherein the historical or repository patient data comprises data of patients with GOLD III COPD, data of patients with GOLD IV COPD, or a combination thereof.

29. 29. The method of claim 27 or 28, wherein the historical or repository patient data comprises data of patients treated with one or more of the following: minimal endobronchial reinforcement implants, endobronchial valves, endobronchial coils, or steam therapy.

30. 30. The method of any one of claims 27-29, wherein the historical or repository patient data comprises data for the patient from an earlier time point.

31. 1. A system comprising: a processor; a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computing system to perform operations including the method of any one of claims 1-30; A system comprising:

32. 31. 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 including the method of any one of claims 1-30.

33. 1. A method for assessing a treatment outcome in a patient, the method comprising: receiving patient data including computed tomography (CT) data of the patient's lungs after placement of an endobronchial implant in the lungs; generating a set of status metrics by inputting the patient data into a first machine learning algorithm, the set of status metrics comprising: a set of pulmonary metrics representative of the patient's pulmonary condition after the placement of the endobronchial implant; a set of implant metrics representative of the condition of the endobronchial implant after placement in the lung; and determining the patient's response to the intrabronchial implant by inputting the set of status metrics into a second machine learning algorithm; predicting an outcome for the patient following the placement of the endobronchial implant by inputting one or more of the set of status metrics or the determined responses into a third machine learning algorithm; and A method comprising:

34. 34. The method of claim 33, wherein the patient data comprises one or more of the following: interview information, medical record information, magnetic resonance imaging (MRI) data, single photon emission computed tomography (SPECT) data, bronchoscopy data, ventilation-perfusion ratio data, pulmonary function test data, chest radiography data, fluoroscopy data, photographs, or sensor data.

35. 35. The method of claim 33 or 34, wherein the CT data comprises expiratory CT data.

36. The method of any one of claims 33-35, wherein the CT data comprises inhalation CT data.

37. The method of any one of claims 33-36, wherein the patient data comprises data obtained at a plurality of different time points.

38. The set of pulmonary metrics characterizes one or more pulmonary parameters, the one or more pulmonary parameters being: forced expiratory volume in 1 second, forced vital capacity, vital capacity, inspiratory volume, inspiratory volume / total lung volume ratio, functional residual capacity, total lung capacity, diffusing volume for carbon monoxide, residual volume, residual volume / total lung volume ratio, occlusion volume, lobar and / or segmental tissue destruction, lobar and / or segmental air capture, lobar and / or segmental fissure status, lobar and / or segmental fissure completeness, lobar and / or segmental ventilation, lung function, homogeneity / heterogeneity of lobar and / or segmental emphysema, emphysema type, location of affected portions of the lung, lobar volume , segment volume, segment location, diaphragm shape, tissue density, opacity, proximity of diseased segment to anatomical structures, proximity of diseased segment to other medical devices, bronchial segment luminal diameter, airflow mapping, collapsed airways, airway pressure, airway compliance, intrathoracic pressure, airway diameter vs. wall thickness, airway wall deformation, vascular perfusion, parenchymal density, bullae, information localizing disease within the lung, obstruction score, mucus score, degree of epithelialization, granulation tissue, implant-induced airway deformation, or airway tissue invagination into the lumen of the implant.

39. 39. The method of any one of claims 33-38, wherein the set of pulmonary metrics comprises a disease score characterizing the severity of pulmonary disease in the patient.

40. 40. The method of claim 38 or 39, wherein the set of pulmonary metrics characterizes change in at least one of the one or more pulmonary parameters across multiple time points.

41. 41. The method of claim 40, wherein the multiple time points comprise two or more of the following: before intrabronchial implant therapy, after intrabronchial implant therapy, before administration of a bronchodilator, after administration of a bronchodilator, before exercise, or during exercise.

42. 42. The method of any one of claims 33-41, wherein the endobronchial implant comprises a minimal endobronchial reinforcement implant.

43. 43. The method of any one of claims 33-42, wherein the set of implant metrics characterize one or more of the following: implant location, distance between the distal end of the implant and the pleura, implant length, implant diameter at any one or more locations along the length of the implant, implant cross-sectional profile at any one or more locations along the length of the implant, implant integrity, implant loop pitch, angle of implant loop profile relative to the longitudinal axis of the implant, implant position relative to one or more additional implants, movement of the implant between inspiration and expiration, implant occlusion, or implant dislodgement.

44. 44. The method of any one of claims 33-43, further comprising generating and displaying a virtual bronchoscopy depicting a model incorporating one or more of at least a portion of said lung metrics or at least a portion of said implant metrics.

45. 45. The method of any one of claims 33-44, 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 volume, inspiratory volume / total lung capacity ratio, functional residual capacity, total lung capacity, diffusing capacity for carbon monoxide, residual volume, residual volume / total lung capacity ratio, segmental volumes, mMRC score, SGRQ score or a subset thereof, CAT score or a subset thereof, 6-minute walk test results, bicycle exercise dysfunction results, cardiopulmonary exercise testing (CPET) results, patient health metrics, patient exercise metrics, patient encounter metrics, number of implant removals required, time to reintervention, durability of treatment, quality of life score, body mass index, comorbidities, drug regimen, length of hospital stay, healthcare utilization, or costs.

46. 46. ​​The method of any one of claims 33-45, wherein the predicted outcome comprises a prediction of a post-procedure problem following the placement of the endobronchial implant.

47. 47. The method of claim 46, wherein the post-procedure problem comprises one or more of the following: excessive mucus, excessive granulation tissue, excessive fibrosis, implant collapse, implant failure, implant migration, implant expectoration, inadequate lung function, pneumothorax, infection, pneumonia, or hospitalization.

48. 48. The method of claim 46 or 47, further comprising determining an intervention to address the post-procedure problem.

49. 49. The method of claim 48, wherein the determined intervention comprises one or more of the following: a clean-up bronchoscopy, retrieval or removal of the endobronchial implant, repositioning the endobronchial implant, replacing the endobronchial implant, enlarging the endobronchial implant, placing an additional endobronchial implant, or consultation with a health care professional.

50. 50. The method of any one of claims 33-49, further comprising generating a report comprising a summary of one or more of the following: at least a portion of the lung metrics, at least a portion of the implant metrics, a determined response of the patient to the endobronchial implant, a predicted outcome of the patient after the placement of the endobronchial implant, or the determined intervention to address any post-procedure issues.

51. 51. The method of any one of claims 33-50, 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.

52. 52. The method of claim 51, wherein the historical or repository patient data comprises data of patients with GOLD III COPD, data of patients with GOLD IV COPD, or a combination thereof.

53. 53. The method of claim 51 or 52, wherein the historical or repository patient data comprises data of patients treated with one or more of the following: minimal endobronchial reinforcement implants, endobronchial valves, endobronchial coils, or steam therapy.

54. 54. The method of any one of claims 51-53, wherein the historical or repository patient data comprises data for the patient from an earlier time point.

55. 55. The method of any one of claims 33-54, further comprising comparing the set of pulmonary metrics to a second set of pulmonary metrics determined from one or more of the following: image data of the lungs before the placement of the endobronchial implant, image data of the lungs at an earlier time point after the placement of the endobronchial implant, image data of the lungs after placement of another endobronchial implant at a location different from the location of the endobronchial implant, or image data from another patient with COPD.

56. 1. A system comprising: a processor; a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computing system to perform operations including the method of any one of claims 33-55; A system comprising:

57. 56. 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 including the method of any one of claims 33-55.

58. 1. A method for assessing a patient having or suspected of having a pulmonary disease, the method comprising: receiving patient data including computed tomography (CT) data of the patient's lungs; generating a pulmonary disease score for a region of interest in the lung by inputting the patient data into a machine learning algorithm, the pulmonary disease score characterizing the severity of pulmonary disease in the region of interest in the patient's lung, the region of interest being a segmental or sub-segmental region of the lung; A method comprising:

59. 59. The method of claim 58, wherein the machine learning algorithm evaluates voxel density in the CT data associated with the region of interest of the lungs of the patient.

60. 60. The method of claim 58 or 59, wherein the pulmonary disease score is based on a plurality of regional disease scores, each corresponding to a distinct lobe, segment, or sub-segmental region of the patient's lung.

61. 61. The method of claim 60, wherein the pulmonary disease score is an average of the multiple local disease scores.

62. 60. The method of claim 58 or 59, wherein the pulmonary disease score is a first pulmonary disease score, the method further comprising generating a plurality of pulmonary disease scores comprising the first pulmonary disease score, each of the plurality of pulmonary disease scores corresponding to a distinct lobe, segment, or subsegmental region of the patient's lung.

63. 63. The method of any one of claims 58-62, wherein the pulmonary disease score represents the degree of at least one of air trapping or hyperinflation in the patient's lungs.

64. 64. The method of any one of claims 58-63, wherein the CT data comprises expiratory CT data.

65. The method of any one of claims 58-64, wherein the CT data comprises inhalation CT data.

66. 66. The method of any one of claims 58-65, wherein the CT data is generated prior to a treatment being administered to the patient to treat the pulmonary disorder.

67. 66. The method of any one of claims 58-65, wherein the CT data is generated subsequent to a treatment being administered to the patient to treat the pulmonary disease.

68. 68. The method of claim 66 or 67, wherein the treatment comprises placement of an intrabronchial implant.

69. 1. A system comprising: a processor; a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computing system to perform operations including the method of any one of claims 58-68; A system comprising:

70. 1. A computed tomography (CT) scanning device, comprising: a processor; a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the CT scanning device to perform operations including a method according to any one of claims 58-68; 1. A computer tomography (CT) scanning device comprising:

71. 69. 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 including the method of any one of claims 58-68.

72. 1. A method for normalizing quantitative computed tomography (CT) results for a patient, the method comprising: receiving first CT data relating to the patient, the first CT data being generated under predetermined imaging conditions; converting the first CT data into second CT data by applying at least one correction factor associated with the predetermined imaging condition to the first CT data; A method comprising:

73. 73. The method of claim 72, wherein the at least one correction factor maps voxel densities in the first CT data to normalized voxel densities.

74. 74. The method of claim 72 or 73, 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.

75. The method of any one of claims 72-74, 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 spacing.

76. A method according to any one of claims 72 to 75, wherein the at least one correction factor compensates for voxel density in the first CT data as affected by a reconstruction algorithm for determining image sharpness or smoothness in an axial plane.

77. 77. The method of any one of claims 72-76, wherein the first CT data is obtained from a CT scan provider having a provider-specific machine learning algorithm for reconstructing CT images from CT data, and the at least one correction factor compensates for voxel density in the first CT data as affected by the provider-specific machine learning algorithm.

78. 78. The method of any one of claims 72-77, wherein the at least one correction factor compensates for voxel density in the first CT data affected by administration of a contrast agent to the patient before the first CT data is generated.

79. The method of any one of claims 72-78, wherein the second CT data is normalized with respect to a CT scanning parameter.

80. 80. The method of any one of claims 72-79, wherein the first CT data is acquired during a pre-procedural stage prior to placement of an endobronchial implant in the patient.

81. The method of any one of claims 72-80, further comprising generating a set of pulmonary metrics associated with the patient based on the second CT data.

82. 80. The method of any one of claims 72-79, wherein the first CT data is acquired during an intra-procedural stage during placement of an endobronchial implant in the patient.

83. 80. The method of any one of claims 72-79, wherein the first CT data is acquired during a post-procedure stage following placement of an endobronchial implant in the patient.

84. 84. The method of claim 82 or 83, 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.

85. 1. A system comprising: a processor; a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computing system to perform operations including the method of any one of claims 72-84; A system comprising:

86. 1. A computed tomography (CT) scanning device, comprising: a processor; a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the CT scanning device to perform operations including a method according to any one of claims 72-84; 1. A computer tomography (CT) scanning device comprising:

87. 85. 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 including the method of any one of claims 72-84.

88. 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 the patient's lungs; generating a set of pulmonary metrics by inputting the patient data into a first machine learning algorithm, the set of pulmonary metrics representing the pulmonary condition of the patient; identifying potential target regions within the lung for treatment for the pulmonary disease based at least in part on the generated pulmonary metrics; and A method comprising:

89. 90. The method of claim 88, wherein the patient data comprises one or more of the following: interview information, medical record information, magnetic resonance imaging (MRI) data, single photon emission computed tomography (SPECT) data, bronchoscopy data, ventilation-perfusion ratio data, pulmonary function test data, chest radiography data, fluoroscopy data, photographs, or sensor data.

90. 90. The method of claim 88 or 89, wherein the CT data comprises expiratory CT data.

91. 91. The method of any one of claims 88-90, wherein the CT data comprises inhalation CT data.

92. 92. The method of any one of claims 88-91, wherein the patient data comprises data obtained at a plurality of different time points.

93. 93. The method of any one of claims 88-92, wherein the set of pulmonary metrics correlates to whether the patient has at least one of chronic obstructive pulmonary disease (COPD), severe emphysema, or severe emphysema with hyperinflation.

94. The set of pulmonary metrics characterizes one or more pulmonary parameters, the one or more pulmonary parameters being one or more of the following: forced expiratory volume in 1 second, forced vital capacity, vital capacity, inspiratory volume, inspiratory volume / total lung volume ratio, functional residual capacity, total lung capacity, diffusing volume for carbon monoxide, residual volume, residual volume / total lung volume ratio, occlusion volume, lobar and / or segmental tissue destruction, lobar and / or segmental air capture, lobar and / or segmental fissure status, lobar and / or segmental fissure completeness, lobar and / or segmental ventilation, lung function, lobar and / or segmental emphysema homogeneity / heterogeneity, pulmonary function, 94. The method of any one of claims 88-93, comprising one or more of the following: emphysema type, location of diseased portion of the lung, lobar volume, segmental volume, segmental location, diaphragm shape, tissue density, opacity, proximity of diseased portion to anatomical structures, proximity of diseased portion to other medical devices, luminal diameter of bronchial segments, airflow mapping, collapsed airways, airway pressure, airway compliance, intrathoracic pressure, airway diameter vs. wall thickness, airway wall deformation, vascular perfusion, parenchymal density, bullae, information localizing disease within the lung, obstruction score, mucus score, or degree of epithelialization.

95. 95. The method of any one of claims 88-94, wherein the set of pulmonary metrics comprises at least one disease score characterizing the severity of pulmonary disease in the patient.

96. 96. The method of claim 95, wherein the at least one disease score represents a predictor of patient response to the treatment for the pulmonary disease.

97. 97. The method of claim 95 or 96, wherein the set of pulmonary metrics comprises a plurality of disease scores, each corresponding to a distinct lobe, segment, or sub-segmental region of the patient's lung.

98. 97. The method of claim 95 or 96, wherein the set of pulmonary metrics comprises a single disease score based on multiple regional disease scores, each corresponding to a distinct lobe, segment, or sub-segmental region of the patient's lung.

99. 99. The method of claim 98, wherein the single disease score is an average of the multiple local disease scores.

100. 100. The method of any one of claims 95-99, wherein the at least one disease score represents the degree of at least one of air trapping or hyperinflation in the patient's lungs.

101. 101. The method of any one of claims 94-100, wherein the set of pulmonary metrics characterizes change in at least one of the one or more pulmonary parameters across multiple time points.

102. 102. The method of claim 101, wherein the multiple time points comprise two or more of the following: before intrabronchial implant therapy, after intrabronchial implant therapy, before administration of a bronchodilator, after administration of a bronchodilator, before exercise, or during exercise.

103. 103. The method of any one of claims 88-102, wherein the treatment comprises airway treatment for COPD.

104. 104. The method of claim 103, wherein the airway treatment comprises a pharmacological treatment.

105. 105. The method of claim 103 or 104, wherein the airway treatment comprises an interventional treatment.

106. 106. The method of claim 105, wherein the interventional treatment comprises one or more of the following: steam therapy, administration of a sealant, transbronchial fenestration, placement of an endobronchial coil, placement of an endobronchial valve, or placement of a minimal endobronchial reinforcing implant.

107. 107. The method of claim 106, wherein the interventional treatment comprises the placement of the minimal endobronchial reinforcement implant.

108. 108. The method of any one of claims 88-107, further comprising generating a plan for said treatment.

109. 109. The method of claim 108, wherein the plan is generated by inputting the set of pulmonary metrics into a second machine learning algorithm.

110. 110. The method of claim 108 or 109, 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, route to target location, or local treatment solution.

111. 111. The method of claim 110, wherein the implant placement location is based, at least in part, on one or more of the following: location of dynamic airway collapse as determined from expiratory CT data, severity of disease in peripheral regions of the lung, location of the patient's pleural wall, or location of lobar, segmental, and / or subsegmental airways.

112. 1. A system comprising: a processor; a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the computing system to perform operations including the method of any one of claims 88-111; A system comprising:

113. 1. A computed tomography (CT) scanning device, comprising: a processor; a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the CT scanning device to perform operations including a method according to any one of claims 88-111; 1. A computer tomography (CT) scanning device comprising:

114. 112. 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 including the method of any one of claims 88-111.

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