Systems and methods for selecting, reviewing, modifying and / or approving surgical plans - Patents.com

Abstract

Disclosed herein are systems and methods for reviewing and analyzing proposed patient-specific surgical plans. In some embodiments, a surgical plan review program provides an easy-to-use interface for the surgeon to review various aspects of the proposed surgical plan. The program allows the surgeon to approve the surgical plan and / or provide feedback on the surgical plan to prompt refinement of the surgical plan.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 63 / 420,279, filed October 28, 2022, the disclosure of which is incorporated herein by reference in its entirety.

[0002] The present disclosure relates generally to medical design and implementation, and more particularly to systems and methods for designing and implementing patient-specific surgical procedures and / or medical devices. [Background technology]

[0003] Surgical procedures for implanting orthopedic implants are used to correct many different conditions, including spine surgery, hand surgery, shoulder and elbow surgery, total joint reconstruction (arthroplasty), cranial reconstruction, pediatric orthopedic surgery, foot and heel surgery, musculoskeletal tumors, surgical sports medicine, and orthopedic trauma. Spinal surgery itself encompasses a variety of procedures and targets, such as one or more of the cervical, thoracic, lumbar, or sacrum, and may be performed to treat spinal deformity or degeneration and / or associated back pain, leg pain, or other bodily pain. Common spinal deformities that may be treated using orthopedic implants include irregular spinal curvatures, such as scoliosis, lordosis or kyphosis (high or low), and irregular spinal displacements (e.g., spondylolisthesis). Other spinal disorders that can be treated using orthopedic implants include osteoarthritis, lumbar or cervical degenerative disc disease, lumbar spinal stenosis, and cervical spinal stenosis. Summary of the Invention

[0004] The accompanying drawings illustrate various embodiments of the systems, methods, and various other aspects of the present disclosure. Those skilled in the art will recognize that the illustrated element boundaries (e.g., boxes, groups of boxes, or other shapes) in the figures represent exemplary boundaries. In some examples, one element may be designed as multiple elements, and multiple elements may be designed as one element. In some examples, an element shown as an internal component of one element may be implemented as an external component of another element, and vice versa. Also, elements may not be drawn to scale. A non-limiting and non-exhaustive description is provided with reference to the following drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating principles. [Brief explanation of the drawings]

[0005] [Figure 1] FIG. 1 is a network connectivity diagram illustrating a system for providing patient-specific medical care, in accordance with an embodiment of the present technology. [Figure 2] 2 illustrates a computing device suitable for use in connection with the system of FIG. 1, in accordance with an embodiment of the present technology. [Figure 3] 1 is a flow chart illustrating a method for providing patient-specific medical care, in accordance with an embodiment of the present technology. [Figure 4] 1 is a flow chart illustrating another method for providing patient-specific medical care, in accordance with an embodiment of the present technology. [Figure 5A] 10A-10C illustrate various aspects of a case view dashboard generated using a surgical plan review program, in accordance with an embodiment of the present technology. [Figure 5B] 10A-10C illustrate various aspects of a case view dashboard generated using a surgical plan review program, in accordance with an embodiment of the present technology. [Figure 5C] 10A-10C illustrate various aspects of a case view dashboard generated using a surgical plan review program, in accordance with an embodiment of the present technology. [Figure 5D]10A-10C illustrate various aspects of a case view dashboard generated using a surgical plan review program, in accordance with an embodiment of the present technology. [Figure 5E] 10A-10C illustrate various aspects of a case view dashboard generated using a surgical plan review program, in accordance with an embodiment of the present technology. [Figure 6A] 10A-10C illustrate various aspects of a surgical plan review dashboard generated using a surgical plan review program in accordance with embodiments of the present technology. [Figure 6B] 10A-10C illustrate various aspects of a surgical plan review dashboard generated using a surgical plan review program in accordance with embodiments of the present technology. [Figure 6C] 10A-10C illustrate various aspects of a surgical plan review dashboard generated using a surgical plan review program in accordance with embodiments of the present technology. [Figure 7A] 10A-10C illustrate various aspects of a surgical plan review dashboard generated using a surgical plan review program in accordance with embodiments of the present technology. [Figure 7B] 10A-10C illustrate various aspects of a surgical plan review dashboard generated using a surgical plan review program in accordance with embodiments of the present technology. [Figure 8] 10A-10C illustrate various aspects of a surgical plan review dashboard generated using a surgical plan review program in accordance with embodiments of the present technology. [Figure 9A] 10A-10C illustrate various aspects of a surgical plan review dashboard generated using a surgical plan review program in accordance with embodiments of the present technology. [Figure 9B] 10A-10C illustrate various aspects of a surgical plan review dashboard generated using a surgical plan review program in accordance with embodiments of the present technology. [Figure 9C] 10A-10C illustrate various aspects of a surgical plan review dashboard generated using a surgical plan review program in accordance with embodiments of the present technology. [Figure 10A]10A-10C illustrate various aspects of a surgical plan review dashboard generated using a surgical plan review program in accordance with embodiments of the present technology. [Figure 10B] 10A-10C illustrate various aspects of a surgical plan review dashboard generated using a surgical plan review program in accordance with embodiments of the present technology. [Figure 11] 10A-10C illustrate various aspects of a surgical plan review dashboard generated using a surgical plan review program in accordance with embodiments of the present technology. [Figure 12] 10A-10C illustrate various aspects of a surgical plan review dashboard generated using a surgical plan review program in accordance with embodiments of the present technology. DETAILED DESCRIPTION OF THE INVENTION

[0006] The present technology is directed to systems and methods for planning and implementing medical procedures and / or devices. For example, in many of the embodiments disclosed herein, a method of providing medical care includes generating a patient-specific surgical plan that can provide a recommended treatment protocol for a particular patient. The surgical plan may include, among other information, the type of surgical intervention, the surgical site, and predicted post-operative data associated with the corresponding surgical intervention and / or surgical site. The systems and methods described herein further enable a surgeon or other healthcare provider to review and optionally provide feedback on the proposed patient-specific surgical plan. For example, the present technology provides a surgical plan review program that can be implemented on a smartphone, tablet, or other computing device and that a surgeon or other user can use to track, review, and analyze the proposed surgical plan. The surgical plan review program can also enable the surgeon to provide feedback on and / or approve the proposed surgical plan. Without intending to be bound by theory, it is expected that the surgical plan review program will provide a single, easy-to-use interface that allows a surgeon or other user to easily track multiple separate patient cases and easily review, comment on, and approve the proposed surgical plan. In this manner, the surgical plan review program described throughout this Detailed Description is expected to improve case management, the thoroughness of surgeon review of proposed surgical plans, the efficiency of surgeon review of proposed surgical plans, patient outcomes, and the like.

[0007] Embodiments of the present disclosure will now be described in more detail 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, the claimed embodiments may be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. The examples set forth herein are non-limiting examples and are merely examples among other possible examples.

[0008] The words "comprising," "having," "containing," and "including," and other forms of these terms, are intended to be equivalent in meaning and to be open-ended in that the items following any one of these terms are not intended to be an exhaustive description of such items or to be limited only to the items listed.

[0009] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise.

[0010] While the disclosure herein describes systems and methods for treatment planning primarily in the context of orthopedic surgery, the technology is equally applicable to medical procedures and devices in other fields (e.g., other types of surgical practices). Additionally, while many embodiments herein describe systems and methods for implantable devices, the technology is equally applicable to other types of medical devices (e.g., non-implantable devices).

[0011] Headings are for convenience only and should not be used to interpret the scope of the technology.

[0012] A. Selected Embodiments of a System for Patient-Specific Surgical Planning and Patient-Specific Implant Design 1 is a network connectivity diagram illustrating a system 100 for providing patient-specific medical care in accordance with an embodiment of the present technology. As described in detail herein, the system 100 is configured to generate a treatment plan for a patient. In some embodiments, the system 100 is configured to generate a treatment plan for a patient with an orthopedic or spinal disease or disorder, such as trauma (e.g., fracture), cancer, deformity, degeneration, pain (e.g., back pain, leg pain), abnormal spinal curvature (e.g., scoliosis, lordosis, kyphosis), abnormal spinal displacement (e.g., spondylolisthesis, lateral displacement, axial displacement), osteoarthritis, lumbar degenerative disc disease, cervical degenerative disc disease, lumbar or cervical stenosis, or a combination thereof. The treatment plan may include surgical information, technical recommendations (e.g., device and / or equipment recommendations), and / or medical device design. For example, a treatment plan may include at least a surgical procedure (e.g., a surgical procedure or intervention) and / or at least one medical device (e.g., an implantable medical device (also referred to herein as an "implant" or "implantable device") or an implant delivery instrument). Accordingly, in some embodiments, a treatment plan may also be referred to as a "surgical plan," a "patient-specific surgical plan," a "patient-specific treatment plan," etc.

[0013] In some embodiments, system 100 generates a customized treatment plan for a particular patient or group of patients, also referred to herein as a “patient-specific” or “personalized” treatment or surgical plan. A patient-specific surgical plan may include at least one patient-specific surgical procedure and / or at least one patient-specific medical device designed and / or optimized for the patient's particular characteristics (e.g., condition, anatomy, pathology, status, medical history). For example, a patient-specific medical device is not an off-the-shelf device but can be designed and manufactured specifically for a particular patient. However, it should be understood that a patient-specific surgical plan may also include aspects that are not customized for a particular patient. For example, a patient-specific or personalized surgical procedure may include one or more instructions, portions, steps, etc. that are not patient-specific. Similarly, a patient-specific or personalized medical device may include one or more components that are not patient-specific and / or may be used with equipment or instruments that are not patient-specific. A personalized implant design may be used for the manufacture or selection of patient-specific technology, including medical devices, instruments, and / or surgical kits. For example, a personalized surgical kit may include one or more patient-specific devices, patient-specific equipment, non-patient-specific technology (e.g., standard equipment, devices, etc.), instructions for use, patient-specific treatment planning information, or a combination thereof.

[0014] System 100 includes a client computing device 102, which may be a user device such as a smartphone, mobile device, laptop, desktop, personal computer, tablet, phablet, or other such device known in the art. As described in detail herein, client computing device 102 may include one or more processors and memory that stores instructions executable by the one or more processors to perform methods described herein. Client computing device 102 may be associated with a healthcare provider (e.g., a surgeon, a healthcare administrator, a hospital system, etc.) treating a patient. While FIG. 1 illustrates a single client computing device 102, in another embodiment, client computing device 102 may instead be implemented as a client computing system including multiple computing devices, such that operations described herein with respect to client computing device 102 may instead be performed by the client computing system and / or multiple client computing devices.

[0015] The client computing device 102 is configured to receive a patient dataset 108 associated with a treated patient. The patient dataset 108 may include data representing the patient's condition, anatomy, pathology, medical history, preferences, and / or any other information or parameters related to the patient. For example, the patient dataset 108 may include medical history, surgical intervention data, treatment outcome data, progress data (e.g., physician notes), patient feedback (e.g., quality of life questionnaires, feedback obtained using surveys), clinical data, provider information (e.g., physician, hospital, surgical team), patient information (e.g., demographics, gender, age, height, weight, type of condition, occupation, activity level, tissue information, health score, comorbidities, health-related quality of life (HRQL)), vital signs, diagnostic results, medication information, allergies, image data (e.g., camera images, magnetic resonance imaging (MRI) images, ultrasound images, computed tomography (CAT) scan images, positron emission tomography (PET) images, x-ray images), diagnostic equipment information (e.g., manufacturer, model number, specifications, user-selected settings / configuration, etc.), and the like. In some embodiments, the patient dataset 108 includes data representing one or more of the patient identification number (ID), age, gender, body mass index (BMI), lumbar lordosis, Cobb angle, pelvic intrinsic angle, disc height, segmental flexibility, bone quality, rotational displacement, and / or spinal treatment level.

[0016] The client computing device 102 is also configured to enable a user (e.g., a surgeon) to review one or more proposed surgical plans for a treated patient. Specifically, the client computing device 102 may include a surgical plan review software module 123 (“review module 123”). As described in more detail below and throughout this Detailed Description, the review module 123 may include computer-executable instructions for generating, displaying, and / or implementing a surgical plan review program or platform 125 (“review program 125”) that facilitates a surgeon's or user's review of one or more patient-specific surgical plans via the client computing device 102.

[0017] The review module 123 may be stored in the form of computer-readable or computer-executable instructions in a memory (not shown) of the client computing device 102. In other embodiments, the review module 123 may be stored remotely from the client computing device 102 (e.g., in the cloud or on a remote server) and may be implemented on the client computing device 102 via a remote (e.g., wireless) connection. In still other embodiments, portions of the review module 123 may be stored locally on the client computing device 102, and other portions of the review module 123 may be stored remotely.

[0018] The client computing device 102 is operatively connected to the server 106 via a communications network 104, thus enabling data transfer between the client computing device 102 and the server 106. The communications network 104 may be a wired and / or wireless network. If wireless, the communications network 104 may be implemented using communications technologies such as visible light communications (VLC), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE), wireless local area network (WLAN), infrared (IR) communications, public switched telephone network (PSTN), radio waves, and / or other communications technologies known in the art.

[0019] The server 106, which may also be referred to as a "therapy support network" or a "prescriptive analytics network," may include one or more computing devices and / or systems. As further described herein, the server 106 may include one or more processors and memory that stores instructions executable by the one or more processors to perform some or all of the methods described herein. In some embodiments, the server 106 is implemented as a distributed "cloud" computing system or organization across any suitable combination of hardware and / or virtual computing resources.

[0020] The client computing device 102 and the server 106 may individually or collectively perform some or all of the various methods described herein for providing patient-specific medical care. For example, some or all of the steps of the methods described herein may be performed by the client computing device 102 alone, the server 106 alone, or a combination of the client computing device 102 and the server 106. Thus, while certain operations are described herein with respect to the server 106, it should be understood that these operations may also be performed by the client computing device 102, and vice versa, unless the context requires otherwise.

[0021] The server 106 includes at least one database 110 configured to store reference data useful for the treatment planning methods described herein. The reference data may include historical and / or clinical data from the same patient or other patients, data collected from previous surgeries and / or other treatments of the patient by the same or other healthcare providers, data related to medical device designs, data collected from research or survey groups, data from clinical databases, data from academic institutions, data from implant manufacturers or other medical device manufacturers, data from image analysis, data from simulations, clinical trials, demographic data, treatment data, prognosis data, mortality, etc.

[0022] In some embodiments, database 110 includes multiple reference patient datasets, each associated with a corresponding reference patient. For example, the reference patients can be patients who have previously been treated or patients currently undergoing treatment. Each reference patient dataset can include data representing the corresponding reference patient's condition, anatomy, pathology, medical history, disease progression, preferences, and / or any other information or parameters related to the reference patient, such as any of the data described herein with respect to patient dataset 108. In some embodiments, the reference patient dataset includes pre-operative data, intra-operative data, and / or post-operative data. For example, the reference patient dataset can include data representing one or more of patient ID, age, gender, BMI, lumbar lordosis, Cobb angle, pelvic intrinsic angle, disc height, segmental flexibility, bone quality, rotational displacement, and / or spinal treatment level. As another example, the reference patient dataset can include treatment data related to at least one surgical procedure performed on the reference patient, such as a description of the surgical procedure or intervention (e.g., surgical technique, bone resection, surgical procedure, reduction procedure, placement of an implant or other device). In some embodiments, the treatment data includes medical device design data of at least one medical device used to treat the reference patient, such as physical properties (e.g., size, shape, volume, material, mass, weight), mechanical properties (e.g., stiffness, strength, modulus, hardness), and / or biological properties (e.g., osteointegration, cell adhesion, antibacterial properties, antiviral properties). In yet another example, the reference patient dataset can include prognostic data describing the outcome of the treatment of the reference patient, such as reduction anatomical indices, presence of fusion, HRQL, pain level, activity level, return to work, complications, recovery time, efficacy, mortality, and / or follow-up surgery.

[0023] In some embodiments, the server 106 receives at least a portion of the reference patient datasets from multiple healthcare provider computing systems (e.g., systems 112a-112c, collectively 112). The server 106 can be connected to the healthcare provider computing systems 112 via one or more communication networks (not shown). Each healthcare provider computing system 112 can be associated with a corresponding healthcare provider (e.g., a doctor, surgeon, clinic, hospital, healthcare network, etc.). Each healthcare provider computing system 112 can include at least one reference patient dataset (e.g., reference patient datasets 114a-114c, collectively 114) associated with a reference patient treated by the corresponding healthcare provider. The reference patient datasets 114 can include, for example, electronic medical records, electronic health records, biomedical datasets, etc. The reference patient datasets 114 can be received by the server 106 from the healthcare provider computing systems 112 and can be reformatted into different formats for storage in the database 110. Optionally, the reference patient data set 114 can be processed (eg, cleaned) to ensure that the patient parameters represented are likely to be useful in the treatment planning methods described herein.

[0024] As described in more detail herein, the server 106 can be configured with one or more algorithms to generate patient-specific surgical planning data (e.g., treatment procedures, target anatomical reductions, medical devices, etc.) based on the reference data. In some embodiments, the patient-specific data is generated based on a correlation between the patient dataset 108 and the reference data. Optionally, the server 106 can predict prognosis, including recovery time, efficacy based on clinical endpoints, likelihood of success, predicted mortality, predicted associated follow-up surgeries, etc. In some embodiments, the server 106 can continuously or periodically analyze patient data (including patient data obtained during the patient stay) to determine near-real-time or real-time risk scores, mortality predictions, etc.

[0025] In some embodiments, the server 106 includes one or more modules for performing one or more steps of the patient-specific treatment planning methods described herein. For example, in the illustrated embodiment, the server 106 includes a data analysis module 116, a treatment planning module 118, a disease progression module 120, and an intervention timing module 121. In alternative embodiments, one or more of these modules may be combined with one another or omitted. Thus, although certain operations are described herein with respect to one or more particular modules, this is not intended to be limiting, and in alternative embodiments, such operations may be performed by one or more different modules.

[0026] The data analysis module 116 is configured with one or more algorithms for identifying a subset of reference data from the database 110 that is likely to be useful in developing a patient-specific treatment plan. For example, the data analysis module 116 can compare patient-specific data (e.g., the patient dataset 108 received from the client computing device 102) with reference data (e.g., a reference patient dataset) from the database 110 to identify similar data (e.g., one or more similar patient datasets within the reference patient dataset). The comparison can be based on one or more parameters, such as age, gender, BMI, lumbar lordosis, pelvic intrinsic angle, and / or treatment level. The parameters can be used to calculate a similarity score for each reference patient. The similarity score can represent a statistical correlation between the patient dataset 108 and the reference patient dataset. Thus, similar patients can be identified based on whether the similarity score is above, below, or at a predetermined threshold. For example, as described in more detail below, the comparison can be performed by assigning values ​​to each parameter and determining the aggregate difference between the test patient and each reference patient. Reference patients whose aggregate difference is below the threshold can be considered similar patients.

[0027] The data analysis module 116 may further be configured with one or more algorithms to select a subset of the reference patient dataset based on, for example, its similarity to the patient dataset 108 and / or the treatment outcomes of the corresponding reference patients. For example, the data analysis module 116 may identify one or more similar patient datasets within the reference patient dataset and then select a subset of the similar patient datasets based on whether the similar patient datasets contain data indicative of a favorable or desired treatment outcome. The prognostic data may include data representing one or more prognostic parameters, such as reduction anatomical indices, presence of fusion, HRQL, activity level, complications, recovery time, efficacy, mortality, or follow-up surgery. As described in more detail below, in some embodiments, the data analysis module 116 calculates a prognostic score by assigning a value to each prognostic parameter. A patient may be considered to have a favorable prognosis if the prognostic score is above, below, or at a predetermined threshold.

[0028] In some embodiments, the data analysis module 116 selects a subset of the reference patient dataset based at least in part on user input (e.g., from a clinician, surgeon, physician, or health care provider). For example, the user input can be used to identify similar patient datasets. In some embodiments, similarity weightings and / or prognostic parameters can be selected by the health care provider or physician to adjust the similarity score and / or prognosis score based on the clinician input. In further embodiments, the health care provider or physician can select the set of similarity parameters and / or prognosis parameters (or define new similarity parameters and / or prognosis parameters) used to generate the similarity score and / or prognosis score, respectively.

[0029] In some embodiments, the data analysis module 116 includes one or more algorithms used to select a set or subset of reference patient datasets based on criteria other than patient parameters. For example, one or more algorithms can be used to select a subset based on healthcare provider parameters (e.g., based on healthcare provider rankings / scores, such as hospital / physician expertise, number of procedures performed, hospital rankings, etc.) and / or healthcare resource parameters (e.g., diagnostic devices, equipment, surgical devices, such as surgical robots), or other non-patient-related information that can be used to predict the prognosis and risk profile of the current healthcare provider's procedures. For example, to reduce or limit irregularities due to differences between diagnostic devices, reference patient datasets with images recorded from similar diagnostic devices can be aggregated. Furthermore, data from similar healthcare providers (e.g., healthcare providers with traditionally similar prognoses, physician expertise, surgical teams, etc.) can be used to develop a patient-specific treatment plan for a particular healthcare provider. In some embodiments, reference healthcare provider datasets, hospital datasets, physician datasets, surgical team datasets, post-treatment datasets, and other datasets can be used. For example, a patient-specific surgical plan for performing a battlefield surgery can be based on reference patient data from a similar battlefield surgery and / or a dataset associated with the battlefield surgery. In another example, a patient-specific surgical plan can be generated based on available robotic surgical systems. The reference patient dataset can be selected based on patients operated on using comparable robotic surgical systems under similar conditions (e.g., surgical team size and capabilities, hospital resources, etc.).

[0030] In some embodiments, the analysis module 116 is or includes a comparator configured to generate one or more visual comparisons of anatomical models, such as a comparison between a preoperative anatomical model and a predicted postoperative anatomical model. As described in more detail below, a virtual model of the preoperative and / or predicted postoperative patient anatomy can be generated by analyzing patient images. Image analysis can include, for example, a segmentation process, a boundary detection process, tissue analysis, predicted tissue manipulation, predicted tissue response, or a combination thereof. The system can generate relationships between imaged anatomical elements to generate a positioning map of the patient's anatomy. To compare multiple anatomical models, the analysis module 116 can identify key features of the patient's corresponding anatomical models. The analysis module 116 can then overlay, superimpose, or match the models based on the alignment of those key features. This allows a user to view differences between the models (e.g., differences between the preoperative anatomical model and the predicted postoperative anatomical model), as described in connection with FIG. 6C . In some embodiments, the analysis module 116 can digitally overlay multiple three-dimensional virtual models to generate a visual comparison. The analysis module 116 can identify and / or label differences between the models (e.g., differences that meet a threshold score).

[0031] The treatment planning module 118 is configured with one or more algorithms that generate at least one surgical plan (e.g., a preoperative plan, an intraoperative plan, a postoperative plan, etc.) based on the output from the data analysis module 116. In some embodiments, the treatment planning module 118 is configured to create and / or implement at least one predictive model for generating a patient-specific treatment plan, also referred to as a "prescriptive model." The predictive model may be created using clinical knowledge, statistics, machine learning, AI, neural networks, etc. In some embodiments, the output from the data analysis module 116 is analyzed (e.g., using statistics, machine learning, neural networks, AI) to identify interrelationships between data sets, patient parameters, healthcare provider parameters, healthcare resource parameters, treatment procedures, medical device designs, and / or treatment outcomes. These interrelationships can be used to create at least one predictive model that predicts the likelihood that the surgical plan will result in a favorable outcome for that particular patient. The predictive model may be validated, for example, by inputting data into the model and comparing the model's output to an expected output.

[0032] In some embodiments, the treatment planning module 118 is configured to generate a surgical plan based on previous treatment data from a reference patient. For example, the treatment planning module 118 can receive a selected subset of the reference patient dataset and / or similar patient dataset from the analysis module 116 and can determine or identify treatment data from the selected subset. The treatment data can include, for example, treatment procedure data (e.g., surgical procedure or intervention data) and / or medical device design data (e.g., implant design data) associated with a preferred or desired treatment outcome for the corresponding patient. The treatment planning module 118 can analyze the treatment procedure data and / or medical device design data to determine an optimal treatment protocol for the treated patient. For example, values ​​can be assigned to the treatment procedures and / or medical device designs and aggregated to produce a treatment score. A patient-specific surgical plan can be determined by selecting surgical plan(s) based on scores (e.g., higher or highest scores, lower or lowest scores, scores above, below, or at a specified threshold). This personalized patient-specific surgical plan can be based, at least in part, on patient-specific or patient-specific selection techniques.

[0033] Alternatively or in combination with the above, the treatment planning module 118 can generate a surgical plan based on correlations between data sets. For example, the treatment planning module 118 can correlate therapeutic procedure data and / or medical device design data from similar patients with favorable prognoses (e.g., identified by the data analysis module 116). The correlation analysis can include converting the correlation coefficient values ​​into values ​​or scores. This value / score can be aggregated, filtered, or otherwise analyzed to determine one or more statistical significances. These correlations can be used to determine surgical procedures and / or medical device designs that are optimal or likely to result in favorable prognoses for the treated patient.

[0034] Alternatively or in combination with the above, the treatment planning module 118 may generate the surgical plan using one or more AI techniques. AI techniques can be used to develop computing systems that can simulate aspects of human intelligence, such as learning, reasoning, planning, problem-solving, decision-making, etc. AI techniques may include, but are not limited to, case-based reasoning, rule-based systems, artificial neural networks, decision trees, support vector machines, regression analysis, Bayesian networks (e.g., naive Bayes classifiers), genetic algorithms, cellular automata, fuzzy logic systems, multi-agent systems, swarm intelligence, data mining, machine learning (e.g., supervised learning, unsupervised learning, reinforcement learning), and hybrid systems.

[0035] In some embodiments, the treatment planning module 118 generates the surgical plan using one or more trained machine learning models. Various types of machine learning models, algorithms, and techniques are suitable for use with the present technology. In some embodiments, the machine learning model is first trained on a training dataset, which is a set of examples used to fit the model's parameters (e.g., the connection weights between "neurons" in an artificial neural network). For example, the training dataset may include any of the reference data stored in the database 110, such as multiple reference patient datasets or a selected subset thereof (e.g., multiple similar patient datasets).

[0036] In some embodiments, a machine learning model (e.g., a neural network or a naive Bayes classifier) ​​can be trained on a training dataset using supervised learning methods (e.g., gradient descent or stochastic gradient descent). The training dataset can include pairs of generated "input vectors" and associated corresponding "answer vectors" (commonly referred to as targets). The current model is run using the training dataset to generate results, which are then compared to the targets for each input vector in the training dataset. Based on the results of the comparison and the particular learning algorithm used, the parameters of the model are adjusted. Model fitting can include both variable selection and parameter estimation. The fitted model can be used to predict responses of observations in a second dataset, called a validation dataset. The validation dataset can provide an unbiased assessment of model fit on the training dataset when tuning model parameters. The validation dataset can be used for regularization by early stopping, for example, by stopping training if the error increases in the validation dataset because the increase in error is a sign of overfitting on the training dataset. In some embodiments, the error on the validation dataset error may vary during training, so adaptive rules may be used to determine when overfitting truly sets in. Finally, a test dataset can be used to provide an unbiased assessment of the final model fit on the training data.

[0037] To generate a surgical plan, the patient dataset 108 can be input into the trained machine learning model. Additional data, such as a selected subset of the reference patient dataset and / or similar patient dataset, and / or treatment data from the selected subset, can also be input into the trained machine learning model. The trained machine learning model can then calculate whether various candidate treatment procedures and / or medical device designs are likely to result in a favorable outcome for the patient. Based on these calculations, the trained machine learning model can select at least one surgical plan for the patient. In some embodiments, the trained machine learning model can determine candidate procedures (or candidate surgical plans), analyze the candidate procedures, select candidate surgical plans or portions thereof, score the plans, and / or generate a surgical plan for the patient. Each surgical plan can be scored (e.g., based on favorable outcome, likelihood of outcome, etc.) and ranked according to the score. The trained machine learning model can determine a set of surgical plans that meet selection criteria for plan review by a user. The selection criteria can be based, for example, on regulatory requirements, reimbursement criteria, medical / provider expertise, available surgical equipment, manufacturing capabilities, exclusion criteria, combinations thereof, etc. A user can input one or more selection criteria to control the type and / or features of the surgical plans for comparison. In embodiments where multiple trained machine learning models are used, the models can be run sequentially or in parallel to compare results and can be periodically updated using training datasets. The treatment planning module 118 can use one or more of the machine learning models based on the model's predictive accuracy score.

[0038] The patient-specific surgical plan generated by the treatment planning module 118 may include at least one patient-specific surgical procedure (e.g., a surgical procedure or intervention) and / or at least one patient-specific medical device (e.g., an implant or implant delivery instrument). The patient-specific surgical plan may include an entire surgical procedure or a portion thereof. Additionally, one or more patient-specific medical devices may be selected or designed specifically for the corresponding surgical procedure, thus enabling various components of patient-specific technology to be used in combination to treat the patient.

[0039] In some embodiments, the patient-specific surgical procedure includes an orthopedic surgical procedure, such as spine surgery, hip surgery, knee surgery, jaw surgery, hand surgery, shoulder surgery, elbow surgery, joint reconstruction (arthroplasty), cranial reconstruction, foot surgery, or ankle surgery. Spinal surgery may include spinal fusion procedures, such as posterior lumbar interbody fusion (PLIF), anterior lumbar interbody fusion (ALIF), lateral or transforaminal lumbar interbody fusion (TLIF), lumbar lateral interbody fusion (LLIF), direct lumbar lateral interbody fusion (DLIF), or lateral approach lumbar interbody fusion (XLIF). In some embodiments, the patient-specific treatment procedure includes instructions and / or directions for performing one or more aspects of the patient-specific surgical procedure. For example, the patient-specific surgical procedure may include one or more of a surgical technique, a reduction procedure, a bone resection, or an implant placement.

[0040] In some embodiments, the patient-specific medical device design includes the design of an orthopedic implant and / or the design of an instrument for delivering the orthopedic implant. Examples of such implants include, but are not limited to, screws (e.g., bone screws, spinal screws, pedicle screws, intervertebral screws), interbody implant devices (e.g., interbody implants), cages, plates, rods, discs, fusion devices, spacers, rods, expandable devices, stents, brackets, ties, scaffolds, fixation devices, anchors, nuts, bolts, rivets, connectors, tethers, fasteners, joint replacements, hip implants, etc. Examples of instruments include, but are not limited to, screw guides, cannulas, ports, catheters, insertion tools, etc.

[0041] A patient-specific medical device design may include data representing one or more of the physical properties (e.g., size, shape, volume, material, mass, weight), mechanical properties (e.g., stiffness, strength, modulus, hardness), and / or biological properties (e.g., osteointegration, cell adhesion, antibacterial properties, antiviral properties) of a corresponding medical device. For example, a design for an orthopedic implant may include the shape, size, material, and / or effective stiffness of the implant (e.g., lattice density, number of struts, strut locations, etc.). In some embodiments, the patient-specific medical device design generated is a design for the entire device. Alternatively, the design generated may be a design for one or more components of the device rather than the entire device.

[0042] In some embodiments, the design is of one or more patient-specific device components that can be used with standard, off-the-shelf components. For example, in spinal surgery, a pedicle screw kit may include both standard and patient-specific customized components. In some embodiments, the generated design is of a patient-specific medical device that can be used with standard, off-the-shelf delivery instruments. For example, an implant (e.g., screw, screw holder, rod) can be designed and manufactured for the patient, while the instrument that delivers the implant can be a standard instrument. This approach allows the implanted components to be designed and manufactured based on the patient's anatomy and / or surgeon's preferences to enhance treatment. The patient-specific devices described herein are expected to improve delivery into the patient's body, placement at the treatment site, and / or interaction with the patient's anatomy.

[0043] In embodiments in which the patient-specific surgical plan includes a specific surgical procedure to implant a medical device, the treatment planning module 118 may also store various types of implant surgery information, such as implant parameters (e.g., type, size), implant availability, aspects of pre-operative planning (e.g., initial implant configuration, detection and measurement of the patient's anatomy, etc.), and FDA requirements for the implant (e.g., specific implant parameters and / or characteristics for compliance with FDA regulations). In some embodiments, the treatment planning module 118 may convert the implant surgery information into a format usable for machine learning-based models and algorithms. For example, the implant surgery information may be tagged with a specific identifier for a mathematical formula or converted into a numerical representation suitable for feeding into a trained machine learning model. The treatment planning module 118 may also store information about the patient's anatomy, such as two-dimensional or three-dimensional images or models of the anatomy, and / or information about the biology, geometry, and / or mechanical properties of the anatomy. The anatomy information may be used to inform implant design and / or placement.

[0044] The disease progression module 120 can be used to analyze, predict, and / or model disease progression for a particular patient. As described in more detail below, the disease progression module 120 can estimate the rate of disease progression for a patient under a variety of different circumstances, including (a) when no surgical intervention is performed, and (b) when one or more surgical plans (e.g., surgical procedures identified by the treatment plan module 118) are performed. Thus, the disease progression module 120 can include algorithms, machine learning models, or other software analysis tools for predicting disease progression in a particular patient.

[0045] In some embodiments, the disease progression module 120 includes a machine learning model or other software module that can be trained based on multiple reference patient data sets, including the patient data described above as well as disease progression metrics for each of the reference patients. The progression metrics can include measurements of disease metrics over a period of time. Suitable metrics can include spinopelvic parameters (e.g., lumbar lordosis, pelvic tilt, sagittal vertical axis (SVA), Cobb angle, coronal offset, etc.), disability scores, functional capacity scores, flexibility scores, VAS pain scores, etc. The progress of the metrics for each reference patient can be correlated with other patient information for that particular reference patient (e.g., age, gender, height, weight, activity level, diet, etc.). These disease metrics can include values ​​over a period of time. For example, the reference patient data can include values ​​of disease metrics daily, weekly, monthly, bimonthly, yearly, or other frequencies. By measuring the metrics over a period of time, changes in the values ​​of the metrics can be tracked as an estimate of disease progression and correlated with other patient data.

[0046] In some embodiments, the disease progression module 120 can thus estimate the rate of disease progression for a particular patient. Progression can be estimated by providing an estimated change in one or more disease metrics over a period of time (e.g., an increase in disease metrics of X% per year). The rate can be constant (e.g., a 5% increase in pelvic tilt per year) or variable (e.g., a 5% increase in pelvic tilt in the first year, a 10% increase in pelvic tilt in the second year, etc.). In some embodiments, the estimated rate of progression can be transmitted to the surgeon or other healthcare provider as part of surgical planning, as described in more detail below.

[0047] As a non-limiting example, a particular patient, who is a 55-year-old male, may have an SVA value of 6 mm. The disease progression module 120 can analyze the patient reference dataset to identify disease progression for individual reference patients who have one or more similarities to this particular patient (e.g., individual patients among the reference patients who have an SVA value of approximately 6 mm and who are approximately the same age, weight, height, and / or gender as the patient). Based on this analysis, the disease progression module 120 can predict the rate of disease progression in the absence of surgical intervention (e.g., the patient's VAS pain score is likely to increase by 5%, 10%, or 15% per year in the absence of surgical intervention, the SVA value is likely to continue to increase by 5% per year in the absence of surgical intervention, etc.).

[0048] The surgical treatment plans and / or associated patient-specific implants described herein can also be based at least in part on estimated disease progression rates, thereby enabling modeling of different outcomes over a desired time period. Thus, the model / simulation can consider any number of additional diseases or conditions to predict the patient's overall health, mobility, etc. These additional diseases or conditions can be used in combination with other patient health factors (e.g., height, weight, age, activity level, etc.) to generate a patient health score that reflects the patient's overall health. The patient health score can be displayed for the surgeon's review and / or incorporated into the disease progression estimate. Thus, the technology can generate one or more virtual simulations of predicted disease progression to show how the patient's anatomy is predicted to change over time. Physician input can be used to generate or modify the virtual simulations. The technology can generate one or more post-treatment virtual simulations based on physician input received for review by healthcare providers, patients, etc.

[0049] In some embodiments, the technology can also predict, model, and / or simulate disease progression based on one or more possible surgical plans. For example, the disease progression module 120 can simulate what a patient's anatomy and / or spinal metrics will look like one, two, five, or ten years post-surgery for several different surgical plans. The simulation can also incorporate non-surgical factors, such as the patient's age, height, weight, gender, activity level, and other health conditions, as described above. The system and / or surgeon can use this disease progression to help select which surgical plan will provide the best long-term effectiveness, as described below. These simulations can also be used to determine patient-specific reductions that compensate for the predicted disease progression.

[0050] Thus, in some embodiments, multiple (e.g., two, three, four, five, six, or more) disease progression models are simulated to provide disease progression data for several different surgical plans. For example, the disease progression module may generate a model that predicts postoperative disease progression for each of three different surgical plans. The surgeon or other healthcare provider can review the disease progression models and, based on that review, select which of the three surgical plans is likely to provide the best long-term outcome for the patient.

[0051] Based on the modeled disease progression, the systems and methods described herein can also (i) identify a recommended time point for surgical intervention and / or (ii) identify a recommended type of surgical procedure for the patient. In some embodiments, the present technology therefore includes an intervention timing module 121 that includes an algorithm, machine learning model, or other software analysis tool for determining the optimal time point for surgical intervention in a particular patient. This can be done, for example, by analyzing patient reference data including (i) preoperative disease progression metrics of the individual reference patient, (ii) disease state metrics at the time of surgical intervention of the individual reference patient, (iii) postoperative disease progression metrics of the individual reference patient, and / or (iv) a scored surgical outcome of the individual reference patient. The intervention timing module 121 can compare the disease state metrics of a particular patient to a reference patient dataset to determine the disease progression point for similar patients at which surgical intervention resulted in the most favorable outcome.

[0052] As a non-limiting example, the reference patient dataset may include data associated with the reference patient's sagittal vertical axis. This data may include (i) the individual patient's sagittal vertical axis value over a period of time before the surgical intervention (e.g., how quickly and to what extent the sagittal vertical axis value changed), (ii) the individual patient's sagittal vertical axis at the time of the surgical intervention, (iii) the change in sagittal vertical axis after the surgical intervention, and (iv) the degree to which the surgical intervention was successful (e.g., based on pain, quality of life, or other factors). Based on the above data, the intervention timing module 121 can identify when surgical intervention is most likely to result in the most favorable outcome based on the particular patient's sagittal vertical axis value. Of course, the above evaluation metrics are provided for illustrative purposes only, and the intervention timing module 121 can incorporate other evaluation metrics (e.g., lumbar lordosis, pelvic tilt, sagittal vertical axis, Cobb angle, coronal offset, disability score, functional ability score, flexibility score, VAS pain score) in place of or in combination with the sagittal vertical axis value to predict the time point at which surgical intervention has the highest probability of resulting in a favorable outcome for that particular patient.

[0053] The intervention timing module 121 may also incorporate one or more mathematical rules based on thresholds for the values ​​of various disease condition indicators. For example, the intervention timing module 121 may indicate that surgical intervention is necessary if one or more disease indicators exceed a predetermined threshold or meet some other criteria. Exemplary thresholds indicating that surgical intervention may be necessary include an SVA value greater than 7 mm, a mismatch between lumbar lordosis and the pelvic intrinsic angle greater than 10 degrees, a Cobb angle greater than 10 degrees, and / or a combination of a Cobb angle and LL / PI mismatch greater than 20 degrees. Of course, other thresholds and indicators may be used, and the above are provided by way of example only. In some embodiments, the above rules may be tailored to a particular patient population (e.g., for men over 50 years old, an SVA value greater than 7 mm indicates the need for surgical intervention). If a particular patient does not exceed the threshold indicating that surgical intervention is recommended, the intervention timing module 121 may provide an estimate of when the patient's indicators will exceed one or more thresholds, thereby providing the patient with an estimate of when surgical intervention may be recommended.

[0054] In some embodiments, the treatment planning module 118 identifies one or more types of surgical procedures for a patient based at least in part on the patient's disease progression as determined using the disease progression module 120 and / or the intervention timing module 121. The treatment planning module 118 may also incorporate one or more mathematical rules for identifying surgical procedures. As a non-limiting example, if the LL / PI mismatch is between 10 and 20 degrees, the treatment planning module 118 may recommend an anterior fusion; if the LL / PI mismatch is greater than 20 degrees, the treatment planning module may recommend both anterior and posterior fusion. As another non-limiting example, if the SVA value is between 7 mm and 15 mm, the treatment planning module may recommend a posterior fusion; if the SVA is greater than 15 mm, the treatment planning module may recommend both posterior and anterior fusion. Of course, other rules may be used, and the above are provided by way of example only.

[0055] Without being bound by theory, incorporating disease progression modeling into the patient-specific surgical planning described herein can further improve the efficacy of procedures and / or provide surgeons with more data for evaluating various surgical plans. For example, in many cases, it may be disadvantageous to operate after a patient's condition has progressed to an irreversible or unstable state. However, it may also be disadvantageous to operate too early, such as before the patient's disease causes symptoms and / or when the patient's disease is unlikely to progress further. Thus, the disease progression module 120 and / or intervention timing module 121 can facilitate identifying the time window in which surgical intervention in a particular patient is most likely to result in a favorable outcome for that patient.

[0056] The surgical plan generated by the treatment planning module 118 can be transmitted to the client computing device 102 via the communications network 104 for output to a user (e.g., a clinician, surgeon, healthcare provider, patient). In some embodiments, the client computing device 102 includes or is operably coupled to a display 122 for outputting the treatment plan. The display 122 can include a graphical user interface (GUI) for visually illustrating various aspects of the surgical plan. For example, the display 122 can show various aspects of the surgical procedure to be performed on the patient, such as the surgical approach, treatment levels, reduction techniques, tissue resection and / or implant placement. To facilitate visualization, the surgical plan can include one or more virtual models of the surgical procedure viewable via the display 122. For example, the surgical plan can include a first virtual model of the pre-operative patient anatomy, a second virtual model of the predicted post-operative patient anatomy if the surgical plan is performed, and / or a third virtual model comparing (e.g., overlaying) the predicted post-operative patient anatomy with the pre-operative patient anatomy. Display 122 may also display additional aspects of the surgical plan, such as predicted postoperative patient outcome measures, predicted disease progression outcomes, etc., associated with the identified surgical procedure. As another example, display 122 may show the design of a medical device to be implanted in the patient according to the transmitted surgical plan, such as a two-dimensional or three-dimensional model of the device design. Display 122 may also show patient information, such as two-dimensional or three-dimensional images or models of the patient's anatomy where the surgical procedure will be performed and / or where the device will be implanted. Examples of displays, such as display 122, illustrating the surgical plan are shown below in FIGS. 5A-12. Client computing device 102 may further include one or more user input devices (not shown) that allow a user to modify, select, accept, and / or reject the displayed treatment plan.

[0057] In some embodiments, the surgical plan review program 125 is used to display one or more aspects of the surgical plan. For example, the review program 125, which may be implemented as a mobile phone application, a computer application, or the like, may display (e.g., via the display 122) one or more aspects of the surgical plan (e.g., surgical procedures, a virtual model of the patient anatomy, implants, etc.). The review program 125 may provide an interactive interface that further allows the surgeon to select among different patients, select from different surgical plans for the same patient, compare surgical plans for the same patient, review the status of the surgical plan, provide feedback on the proposed surgical plan, accept the surgical plan, reject the surgical plan, etc. The review program 125 may also allow the surgeon or other user to select from different views of the virtual model of the patient's anatomy and / or different views of the patient-specific implants used in the surgical plan. Further details regarding the features of the review program 125 are described below with reference to FIGS. 5A-12 .

[0058] In some embodiments, the medical device designs generated by the treatment planning module 118 can be transmitted from the client computing device 102 and / or server 106 to a manufacturing system 124 for manufacturing the corresponding medical device. The manufacturing system 124 can be located on-site or off-site. On-site manufacturing can reduce the number of patient encounters and / or time to surgical availability, while off-site manufacturing can be useful for fabricating complex devices. Off-site manufacturing facilities can have dedicated manufacturing equipment. In some embodiments, more complex device components can be manufactured off-site, while simpler components can be manufactured on-site.

[0059] Various types of manufacturing systems are suitable for use with embodiments herein. For example, manufacturing system 124 can be configured for additive manufacturing, such as three-dimensional (3D) printing, stereolithography (SLA), digital light processing (DLP), fused deposition modeling (FDM), selective laser sintering (SLS), selective laser melting (SLM), selective heat sintering (SHM), electron beam melting (EBM), sheet additive manufacturing (LOM), powder bed printing (PP), thermoplastic printing, direct material deposition (DMD), inkjet photoresist printing, or similar techniques, or a combination thereof. Alternatively or in combination, manufacturing system 124 can be configured for subtractive (traditional) manufacturing, such as CNC machining, electrical discharge machining (EDM), grinding, laser cutting, waterjet machining, manual machining (e.g., milling, lathe / turning), or similar techniques, or a combination thereof. The manufacturing system 124 can manufacture one or more patient-specific medical devices based on manufacturing instructions or data (e.g., CAD data, 3D data, digital blueprints, stereolithography data, or other data suitable for various manufacturing techniques described herein). Different components of the system 100 can generate at least a portion of the manufacturing data used by the manufacturing system 124. The manufacturing data can include, but is not limited to, manufacturing instructions (e.g., programs executable by additive manufacturing equipment, subtractive manufacturing equipment, etc.), 3D data, CAD data (e.g., CAD files), CAM data (e.g., CAM files), path data (e.g., printhead paths, tool paths, etc.), material data, tolerance data, surface finish data (e.g., surface roughness data), regulatory data (e.g., FDA requirements, reimbursement data, etc.), etc. The manufacturing system 124 can analyze the manufacturability of the implant design based on the received manufacturing data. The implant design can be finalized by making changes to the geometry, surfaces, etc., and then generating manufacturing instructions. In some embodiments, the server 106 generates at least a portion of the manufacturing data, which is transmitted to the manufacturing system 124 .

[0060] The manufacturing system 124 can generate CAM data, print data (e.g., powder bed print data, thermoplastic print data, photoresin data, etc.), etc., and can include additive manufacturing equipment, subtractive manufacturing equipment, heat processing equipment, etc. Additive manufacturing equipment can be a 3D printer, stereolithography device, digital light processing device, fused deposition modeling device, selective laser sintering device, selective laser melting device, electron beam melting device, sheet additive manufacturing device, powder bed printer, thermoplastic printer, direct material deposition device, inkjet photoresin printer, or similar technology. Subtractive manufacturing equipment can be a CNC machine, an EDM machine, a grinding machine, a laser cutter, a water jet machine, a manual machine (e.g., a milling machine, a lathe), or similar technology. Both additive and subtractive techniques can be used to fabricate implants having complex geometries, surface finishes, material properties, etc. The generated fabrication instructions can be configured to cause the manufacturing system 124 to manufacture a patient-specific orthopedic implant that matches or is therapeutically identical to the patient-specific design. In some embodiments, to simplify manufacturing, the patient-specific medical device may include features, materials, and designs shared among multiple designs. For example, deployable patient-specific medical devices for different patients may have similar internal deployment mechanisms but different deployment configurations. In some embodiments, the components of the patient-specific medical device may be selected from a set of available pre-fabricated components, and the selected pre-fabricated components may be modified based on manufacturing instructions or data.

[0061] The surgical plans described herein can be performed by a surgeon, a surgical robot, or a combination thereof, thus allowing for treatment flexibility. In some embodiments, a surgical procedure can be performed entirely by a surgeon, entirely by a surgical robot, or a combination thereof. For example, one step of a surgical procedure may be performed manually by a surgeon, and another step of the procedure may be performed by a surgical robot. In some embodiments, the treatment planning module 118 generates control instructions configured to cause a surgical robot (e.g., a robotic surgery system, a navigation system, etc.) to partially or completely perform the surgical procedure. The control instructions can be transmitted to the robotic device by the client computing device 102 and / or the server 106.

[0062] Following treatment of the patient according to the surgical plan, the progress of the treatment can be monitored over one or more time periods to update the data analysis module 116, the treatment planning module 118, the disease progression module 120, and / or the intervention timing module 121. The post-treatment data can be added to the reference data stored in the database 110. The post-treatment data can be used to train machine learning models to develop patient-specific treatment plans, patient-specific medical devices, or a combination thereof.

[0063] It should be appreciated that the components of system 100 can be configured in many different ways. For example, in another embodiment, database 110, data analysis module 116, treatment planning module 118, disease progression module 120, and / or intervention timing module 121 can be components of client computing device 102 rather than server 106. As another example, database 110, data analysis module 116, treatment planning module 118, disease progression module 120, and / or intervention timing module 121 can be located across multiple different servers, computing systems, or other types of cloud computing resources rather than on a single server 106 or client computing device 102.

[0064] Further, in some embodiments, system 100 is operational with many other computing system environments or configurations. Examples of computing systems, environments, and / or configurations that may be suitable for use with the present technology include, but are not limited to, personal computers, server computers, handheld or laptop devices, mobile phones, wearable electronics, tablet devices, multiprocessor systems, microprocessor-based systems, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

[0065] 2 illustrates a computing device 200 suitable for use in connection with the system 100 of FIG. 1 , according to one embodiment. The computing device 200 may be incorporated into various components of the system 100 of FIG. 1 , such as the client computing device 102 or the server 106. The computing device 200 includes one or more processors 210 (e.g., a CPU, a GPU, an HPU, etc.). The processor 210 may be a single processing unit or multiple processing units within a device, or may be distributed across multiple devices. The processor 210 may be coupled to other hardware devices using a bus, such as a PCI bus or a SCSI bus. The processor 210 may be configurable to execute one or more computer-readable program instructions, such as program instructions for performing any of the methods described herein.

[0066] Computing device 200 may include one or more input devices 220 that provide input to processor 210, for example, to notify processor 210 of actions from a user of device 200. Actions may be communicated by a hardware controller that interprets signals received from the input devices and communicates the information to processor 210 using a communication protocol. Input devices 220 may include, for example, a mouse, keyboard, touchscreen, infrared sensor, touchpad, wearable input device, camera-based or image-based input device, microphone, or other user input device.

[0067] Computing device 200 may include a display 230 used to display various types of output, such as text, models, virtual procedures, surgical plans, implants, graphics, and / or images (e.g., images having voxels representing radiodensity units or Hounsfield units representing tissue density at a site). In some embodiments, display 230 provides graphical or textual visual feedback to a user. Processor 210 may communicate with display 230 through a hardware controller for the device. In some embodiments, display 230 includes input device 220 as part of display 230, such as when input device 220 includes a touchscreen or comprises an eye gaze monitoring system. In other embodiments, display 230 is separate from input device 220. Examples of display devices include LCD display screens, LED display screens, projection, holographic, or augmented reality displays (e.g., head-up displays or head-mounted devices), etc.

[0068] Optionally, other I / O devices 240, such as a network card, a video card, an audio card, a USB, FireWire or other external device, a camera, a printer, speakers, a CD-ROM drive, a DVD drive, a disk drive, or a Blu-Ray drive, may also be coupled to processor 210. Other I / O devices 240 may also include input ports for information from directly connected medical equipment, such as imaging equipment, including MRI machines, X-ray machines, CT machines, etc. Other I / O devices 240 may further include input ports for receiving data from these types of equipment, from other sources, such as over a network, or from previously captured data stored, for example, in a database.

[0069] In some embodiments, computing device 200 also includes a communications device (not shown) capable of wirelessly or wired communication with network nodes. The communications device can communicate with another device or server over a network using, for example, the TCP / IP protocol. Computing device 200 can use the communications device to distribute operations across multiple network devices, including imaging equipment, manufacturing equipment, etc.

[0070] Computing device 200 may include memory 250, which may be located within a single device or distributed across multiple devices. Memory 250 may include one or more of a variety of hardware devices for volatile and non-volatile storage, including both read-only and writeable memory. For example, memory may include random access memory (RAM), various caches, CPU registers, read-only memory (ROM), and writeable, non-volatile memory such as flash memory, hard drives, floppy disks, CDs, DVDs, magnetic storage devices, tape drives, and device buffers. Memory is not a propagated signal separate from the underlying hardware; therefore, memory is non-transitory. In some embodiments, memory 250 is a non-transitory, computer-readable storage medium that stores, for example, programs, software, data, and the like. In some embodiments, memory 250 may include program memory 260, which stores programs and software, such as an operating system 262, one or more therapeutic support modules 264, and other application programs 266. Treatment assistance module 264 may include one or more modules configured to perform various methods described herein (e.g., data analysis module 116 and / or treatment planning module 118 described with respect to FIG. 1 ). Memory 250 may also include data memory 270 that may include, for example, reference data, configuration data, settings, user options or preferences, etc., that may be provided to program memory 260 or any other element of computing device 200.

[0071] B. Selected methods for modeling and developing patient-specific surgical plans The present technology includes systems and methods for generating one or more patient-specific surgical plans and transmitting the one or more patient-specific surgical plans to a surgeon or other healthcare provider for review, feedback, revision, and / or approval. As described below, the patient-specific surgical plans can include, among other things, the patient's surgical procedure and predicted post-operative outcome if the surgical procedure is performed.

[0072] 3 is a flow diagram illustrating a method 300 for providing patient-specific medical care, in accordance with one embodiment of the present technology. Portions or all of the method 300 can be performed by various computing systems or software modules, including, for example, the computing systems described above with respect to FIGS. 1 and 2.

[0073] The method 300 may begin at block 302 by receiving a patient dataset for a particular patient in need of treatment. The patient dataset may include data representing the patient's condition, anatomy, pathology, symptoms, medical history, preferences, and / or any other information or parameters related to the patient. For example, the patient dataset may include surgical intervention data, treatment prognosis data, progress data (e.g., surgeon's notes), patient feedback (e.g., quality of life questionnaires, feedback obtained using surveys), clinical data, patient information (e.g., demographics, gender, age, height, weight, type of condition, occupation, activity level, tissue information, health score, comorbidities, health-related quality of life (HRQL)), vital signs, diagnosis results, medication information, allergies, diagnostic equipment information (e.g., manufacturer, model number, specifications, user-selected settings / configuration, etc.), etc. The patient dataset may also include image data such as camera images, magnetic resonance imaging (MRI) images, ultrasound images, computed tomography (CAT) scan images, positron emission tomography (PET) images, x-ray images, etc. In some embodiments, the patient dataset includes data representing one or more of patient identification (ID), age, gender, body mass index (BMI), lumbar lordosis, Cobb angle, pelvic intrinsic angle, intervertebral disc height, segmental flexibility, bone quality, rotational displacement, and / or spinal treatment level. The patient dataset may be received at a server, computing device, or other computing system. For example, in some embodiments, the patient dataset may be received by server 106 shown in FIG. 1. In some embodiments, the computing system that receives the patient dataset in block 302 also stores one or more software modules (e.g., data analysis module 116, treatment planning module 118, disease progression module 120, and / or intervention timing module 121 shown in FIG. 1, or additional software modules for performing various operations of method 300).

[0074] In some embodiments, the received patient data set may include disease metrics such as lumbar lordosis, Cobb angle, coronal parameters (e.g., coronal balance, global coronal balance, coronal pelvic tilt, etc.), sagittal parameters (e.g., pelvic intrinsic angle, sacral slope, thoracic kyphosis, etc.), and / or pelvic parameters. The disease metrics may include micro-measurements (e.g., metrics associated with a specific or individual segment of the patient's spine) and / or macro-measurements (e.g., metrics associated with multiple segments of the patient's spine). In some embodiments, the disease metrics are not included in the patient data set, and the method 300 includes determining (e.g., automatically determining) one or more of the disease metrics based on the patient image data or an associated virtual model, as described below. In some embodiments, the received patient data may include functional mobility test scores (e.g., step test, 6-meter walk test, chair stand test, timed up-and-go test, etc.). The received patient data set may include additional subjective test scores reflecting aspects of the patient's condition, such as pain tests (e.g., visual analog scale (VAS) pain scores, back pain assessment scores, etc.), disability tests (e.g., Oswestry Disability Index scores, Quebec Back Pain Disability Test scores, etc.), and quality of life tests (e.g., Quality of Life Scale scores).

[0075] The method 300 may continue at block 304 by generating a surgical plan based at least in part on the patient dataset received at block 302. As described in more detail below, the surgical plan may include a patient target site or region of interest for surgical intervention and one or more surgical procedures or interventions to be performed at the region of interest. The surgical plan may also include predicted post-operative data associated with the performance of the surgical procedure at the target site. For example, the surgical plan may include a predicted or target post-operative anatomical configuration depicted as a two-dimensional or three-dimensional virtual model. In some embodiments, the surgical plan also includes additional predictive post-operative analysis results, such as predicted disease progression, predicted patient satisfaction, predicted patient mobility, predicted patient pain, and predicted patient quality of life.

[0076] In some embodiments, generating a surgical plan includes identifying specific target locations relevant to the surgical procedure. For example, in the context of spine surgery, generating a surgical plan may include identifying one or more spinal levels for surgical intervention. In some embodiments, the spinal levels are the high cervical spine (e.g., C1-C5), the high thoracic spine (e.g., T1-T12), the high lumbar spine (e.g., L1-L5), and / or the sacrum. In some embodiments, the identified target locations include a specific range of spinal levels relevant to the surgery (e.g., L1-L3, L3-L5, L4-T12, C1-C3, etc.). The identified target locations may include two, three, four, five, or more cervical vertebrae. Of course, the above target locations are provided by way of example only, and the technology is not limited to these anatomical locations. Indeed, in some embodiments, as described throughout this Detailed Description, the target site may include anatomical structures other than the spine, such as the hip, knee, ankle, shoulder, elbow, wrist, hand, jaw, skull, or other anatomical site.

[0077] The target regions can be identified by reviewing the patient's image data. In some embodiments, a computing system (e.g., computing system 106 of FIG. 1 ) and / or one or more software modules (e.g., treatment planning module 118 of FIG. 1 ) can review and analyze the patient image data and automatically identify the target regions. In such embodiments, a trained machine learning program or other software-based program can analyze the patient image data, extract measurements from the patient image data, compare the extracted measurements to reference data (e.g., predetermined thresholds or ranges associated with "healthy" patients normalized for age, sex, gender, etc.), and identify anatomical regions that are candidates for surgical reduction. Alternatively or additionally, the target regions can be identified and / or confirmed by other suitable means, such as a technician or healthcare provider reviewing the image data and identifying anatomical anomalies.

[0078] As mentioned above, in some embodiments, generating a surgical plan also includes identifying a surgical procedure for the patient. In embodiments in which the surgical plan includes identifying a target site, the surgical procedure can be associated with the target site. In the context of spinal surgery, exemplary surgical procedures include spinal fusion, artificial disc replacement, vertebroplasty, vertebroplasty, spinal laminectomy / decompression, discectomy, facetectomy, foraminotomy, or other spinal surgical procedures. Examples of spinal fusion procedures include posterior lumbar interbody fusion (PLIF), anterior lumbar interbody fusion (ALIF), lateral or transforaminal lumbar interbody fusion (TLIF), lumbar lateral interbody fusion (LLIF), direct lumbar lateral interbody fusion (DLIF), or lateral approach lumbar interbody fusion (XLIF). The above is provided by way of example only, and the present technology may include identifying any type of spinal or other surgical procedure in block 304a.

[0079] The surgical procedures associated with the surgical plan can be identified using any of the methods and systems described herein. For example, in some embodiments, the computing system 106 of FIG. 1 and / or an associated software module (e.g., treatment planning module 118) can identify one or more surgical procedures based on, for example, user input, received or extracted patient data, and / or identified target sites. For example, if the computing system 106 determines that the patient is suffering from disc degeneration at L3-L5, the computing system 106 can recommend a PLIF procedure to fuse L2-T12. Alternatively, the computing system 106 can recommend artificial disc replacement at L3-L4 and L4-L5 to reduce the degeneration while preserving motion. Surgical procedures can also be identified using other methods and systems.

[0080] In some embodiments, the operations at block 304 may include reviewing and / or analyzing multiple types of surgical procedures and / or surgical steps to identify surgical procedures for inclusion in the surgical plan. Types of surgical procedures and / or surgical steps may be selected for inclusion in the surgical plan (or excluded from inclusion in the surgical plan) based, for example, on user input, insurance coverage of the procedure or step, healthcare provider parameters (e.g., based on hospital / physician expertise, number of similar procedures performed, healthcare provider rankings / scores, etc., for the procedure), healthcare resource parameters (e.g., diagnostic equipment, facilities, surgical devices such as surgical robots), and / or other non-patient-related information (e.g., information that can be used to score, predict the prognosis and risk profile of the current healthcare provider's procedure and / or rank the procedure).

[0081] In some embodiments, generating the surgical plan includes identifying or planning a reduced anatomical configuration for the patient (the reduced anatomical configuration may also be referred to herein as a “planned configuration,” “optimized shape,” “post-operative anatomical configuration,” or “target outcome”). The reduced anatomical configuration may reflect the patient's desired and / or predicted anatomy when the surgical plan is performed. In some embodiments, generating the surgical plan includes generating one or more virtual models (e.g., two-dimensional model, three-dimensional model, etc.) representing the reduced anatomical configuration. The virtual model may include some or all of the patient's anatomy within the target site (e.g., any combination of tissue types including, but not limited to, bony structure, cartilage, soft tissue, vascular tissue, neural tissue, etc.). In some embodiments, the virtual model may be a multi-dimensional (e.g., two-dimensional or three-dimensional) virtual model and may include, for example, CAD data, material data, surface modeling, manufacturing data, etc. CAD data may include, for example, solid modeling data (e.g., part files, assembly files, libraries, part / object identifiers, etc.), model geometry, object representations, parameter data, object representations, topology data, surface data, assembly data, metadata, etc. Examples of virtual models are U.S. patent application Ser. Nos. 16 / 048,167, 16 / 242,877, 16 / 207,116, 16 / 352,699, 16 / 383,215, 16 / 569,494, 16 / 699,447, 16 / 735,222, 16 / 987,113, 16 / 990,810, 17 / 0 Nos. 17 / 85,564, 17 / 100,396, 17 / 342,329, 17 / 518,524, 17 / 531,417, 17 / 835,777, 17 / 851,487, 17 / 867,621, and 17 / 824,242, each of which is incorporated herein by reference in its entirety.

[0082] In some embodiments, one or more clinical checks can be performed on or with the generated virtual model representing the predicted postoperative patient anatomy. The clinical checks can include measuring features of the virtual model (e.g., disc space height, lordosis, etc.) to determine whether acceptable metrics are achieved. The system can store acceptable metrics based on historical patient data or other generally accepted medical standards. If one or more measured metrics are outside of an acceptable range, the user can be notified so that the user can determine whether the surgical plan should be updated or discarded. Clinical checks can be performed any number of times throughout method 300 to ensure that individual steps of the surgical plan are not outside of acceptable standards (e.g., clinical checks can be performed until the virtual model representing the predicted postoperative anatomy matches one or more metrics). In some embodiments, the reduced anatomical configuration is identified / determined before the surgical procedure and / or target site. That is, a computing system or a user can model a preferred anatomical outcome and, based on the desired anatomical outcome, identify a surgical procedure and target site that will result in the desired anatomical outcome after implementation.

[0083] In some embodiments, generating the surgical plan includes generating one or more patient evaluation metrics associated with the reduced anatomical configuration. In the context of spine surgery, the patient evaluation metrics may include, for example, coronal parameters, sagittal parameters, pelvic parameters, Cobb angle, shoulder slope, iliopectineal angle, coronal balance, lordosis angle, intervertebral space height, or other similar spinal parameters. As above, the patient evaluation metrics can be determined prior to identifying a surgical procedure and / or target site for surgical intervention. That is, a computing system or user can use the patient evaluation metrics to identify a surgical procedure and target site that will result in the patient evaluation metrics after its performance.

[0084] The surgical plan may include additional features. In some embodiments, for example, the surgical plan may include predicted disease progression, predicted patient satisfaction, predicted patient mobility, predicted patient pain, predicted patient quality of life, etc. For example, the surgical plan may include an estimate of disease progression if the patient undergoes a specified surgical procedure at a specified target site. That is, the surgical plan may include a virtual model (e.g., a two-dimensional or three-dimensional virtual model) of the patient's anatomy at various intervals after surgery. For example, the surgical plan may include a predicted model of the patient's anatomy at one or more of the following intervals: 6 months post-surgery, 1 year post-surgery, 2 years post-surgery, 3 years post-surgery, 4 years post-surgery, 5 years post-surgery, 6 years post-surgery, 7 years post-surgery, 8 years post-surgery, 9 years post-surgery, and / or 10 years post-surgery. The disease progression model may also include predicted patient outcome metrics (e.g., any of the patient outcome metrics described herein, including coronal parameters, sagittal parameters, pelvic parameters, Cobb angle, shoulder slope, iliopectineal angle, coronal balance, lordosis angle, intervertebral space height, or other similar spinal parameters) at the various postoperative intervals identified above, in addition to or instead of a virtual model of predicted patient anatomy.

[0085] Once generated, the surgical plan may be digitally displayed as a surgical report on one or more display screens for easy review, editing, annotation, etc. In some embodiments, the surgical plan may be stored on the client computing device 102 of FIG. 1 as computer-executable instructions executable via a surgical plan review module 123. More specifically, the surgical plan may be reviewed using a surgical plan review program 125 such as that shown in FIG. 1 and described in more detail below with reference to FIGS. 5A-12.

[0086] In some embodiments, the act of generating a surgical plan in block 304 includes generating a plurality of candidate surgical plans (or a subset of surgical plans, such as surgical procedures) and then selecting a surgical plan from the plurality of candidate surgical plans. For example, in some embodiments, a computing system may automatically identify a plurality (e.g., two, three, four, five, six, seven, eight, nine, ten, or more) of surgical plans (or a subset of surgical plans, such as surgical procedures) based on a patient dataset and / or one or more user-input criteria. The identified candidate surgical plans may be ranked and / or scored based on various factors, including predicted patient outcome, user reviews, etc. The identified candidate surgical plan with the highest rank (e.g., based on predicted patient outcome) may be selected as the surgical plan. In some embodiments, a particular ranked surgical plan may not be selected as the surgical plan based on user reviews and / or non-compliance with various user criteria. For example, if a particular surgical plan is identified as requiring a surgical procedure that is unfamiliar to the physician, the particular surgical plan may not be selected, and the method may instead include selecting a suboptimal surgical plan as the surgical plan. Thus, in some embodiments, physician-specific scoring is used to score candidate procedures / surgical plans before selecting a surgical plan. For example, procedures having scores that meet a threshold score (e.g., a threshold postoperative performance index score, a physician-entered threshold score, a threshold prognosis score, etc.) may be identified for user review. Thus, the system may compare the advantages and disadvantages of candidate procedures against each other before selecting a surgical plan.

[0087] After the surgical plan is selected at block 304, method 300 may continue by transmitting the surgical plan to the surgeon at block 306. In some embodiments, the same computing system used at blocks 302 and 304 may transmit the surgical plan to a computing device (e.g., client computing device 102 of FIG. 1 ) for review by the surgeon. This may include transmitting the surgical plan directly to the computing device or uploading the first and second surgical plans to a cloud or other storage system for later download.

[0088] The surgeon may review the surgical plan and approve or disapprove the surgical plan at block 308. For example, to determine whether the surgeon deems the surgical plan acceptable, the surgeon may review the surgical plan using the surgical plan review program 125 (FIG. 1). This may include, for example, reviewing the surgical plan target site, surgical procedure, target / predicted postoperative anatomical configuration, and predicted postoperative patient outcome metrics. Further aspects of using the surgical plan review program 125 to review the surgical plan are described below in Section C with reference to FIGS. 4-12.

[0089] In some embodiments, the surgeon may not approve the surgical plan at block 308. In such embodiments, the surgeon may optionally provide feedback and / or suggested modifications to the surgical plan (e.g., by adjusting the virtual model or changing one or more aspects of the plan, commenting on or further requesting changes to the surgical plan, etc.). Accordingly, method 300 may optionally include receiving surgeon feedback and / or suggested modifications (e.g., via a computing system) at block 310. This may include, for example, modifying the target site of the surgical intervention, the surgical procedure, and / or the target post-operative anatomical configuration. If surgeon feedback and / or suggested modifications are received at block 310, method 300 may continue at block 312 by refining (e.g., automatically by a computing system) the surgical plan based at least in part on the surgeon feedback and / or suggested modifications received at block 310. In some embodiments, the surgeon does not provide feedback and / or suggested modifications if they reject the surgical plan. In such embodiments, block 310 may be omitted, and method 300 may continue by selecting new and / or additional reference patient data sets to refine the surgical plan (e.g., automatically refined by a computing system) and / or by generating a new candidate surgical plan at block 312. The refined and / or new surgical plan may then be sent to the surgeon for review.

[0090] The operations at blocks 306, 308, 310, and 312 may be repeated as many times as necessary until the surgeon selects and approves a particular surgical plan. In some embodiments, method 300 may include performing one or more checks (e.g., clinical checks, manufacturability checks, etc.) on one or more suggested revisions provided by the surgeon before refining the surgical plan based on the suggested revisions. Similar checks may also be performed on the approved surgical plan.

[0091] After receiving surgeon approval of the surgical plan at block 308, method 300 may continue at block 314 by designing one or more patient-specific implants (using the same computing system that performed blocks 302-308) based on the selected surgical plan. For example, the patient-specific implants may be designed based on the target site and surgical procedure included in the selected surgical plan. The patient-specific implant may also be specially designed such that, when implanted at the target site for that particular patient using the identified surgical procedure, it causes the patient's anatomy to occupy a targeted post-operative anatomical configuration (e.g., transforms the patient's anatomy from the patient's native anatomical configuration to a reduced anatomical configuration). Once implanted, the patient-specific implant may be designed to cause the patient's anatomy to occupy the reduced anatomical configuration for the expected life of the implant (e.g., 5+ years, 10+ years, 20+ years, 50+ years, etc.). In some embodiments, the patient-specific implant is designed based solely on a virtual model of the reduced anatomical configuration and / or without reference to pre-operative patient images.

[0092] The patient-specific implant may be any of the implants described herein or in any of the patent references incorporated herein by reference. For example, the patient-specific implant may include one or more screws (e.g., bone screws, spinal screws, pedicle screws, intervertebral screws), interbody implant devices (e.g., intervertebral implants), cages, plates, rods, discs, fusion devices, spacers, rods, expandable devices, stents, brackets, ties, scaffolds, fixation devices, anchors, nuts, bolts, rivets, connectors, tethers, fasteners, joint replacements (e.g., artificial intervertebral discs), hip implants, etc. The patient-specific implant design may include data describing one or more of the implant's physical properties (e.g., size, shape, volume, material, mass, weight), mechanical properties (e.g., stiffness, strength, modulus, hardness), and / or biological properties (e.g., osteointegration, cell adhesion, antibacterial properties, antiviral properties). For example, the design of an orthopedic implant may include implant shape, size, material, and / or effective stiffness (e.g., lattice density, number of struts, strut location, etc.). Examples of patient-specific implants that can be designed in block 314 are U.S. Patent Application Nos. 16 / 048,167, 16 / 242,877, 16 / 207,116, 16 / 352,699, 16 / 383,215, 16 / 569,494, 16 / 699,447, 16 / 735,222, 16 / 987,113, 16 / 990,8 Nos. 17 / 085,564, 17 / 100,396, 17 / 342,329, 17 / 518,524, 17 / 531,417, 17 / 835,777, 17 / 851,487, 17 / 867,621, and 17 / 824,242, each of which is incorporated herein by reference in its entirety.

[0093] In some embodiments, designing the implant at block 316 may optionally include generating manufacturing instructions for manufacturing the implant. For example, a computing system may generate computer-executable manufacturing instructions that, when executed by a manufacturing system, cause the manufacturing system to manufacture the implant.

[0094] In some embodiments, the patient-specific implants are designed in block 316 only after the surgeon selects the surgical plan. Thus, in some embodiments, the implant design is not sent along with the surgical plan to the surgeon and manufactured in block 308 before receiving surgeon approval of the surgical plan. Without being bound by theory, waiting to design the patient-specific implants until after surgeon approval of the surgical plan may increase the efficiency of method 300 and / or reduce the resources required to perform method 300. In other embodiments, one or more patient-specific implants may be designed and included in the surgical plan sent to the surgeon in block 306. Thus, in some embodiments, the operations in block 314 may be included in block 304.

[0095] The method 300 may continue at block 316 by manufacturing the patient-specific implant. The implant may be manufactured using additive manufacturing, such as 3D printing, stereolithography, digital light processing, fused deposition modeling, selective laser sintering, selective laser melting, electron beam melting, sheet additive manufacturing, powder bed printing, thermoplastic printing, direct material deposition, or inkjet photoresin printing, or similar techniques, or a combination thereof. Alternatively or additionally, the implant may be manufactured using subtractive manufacturing, such as CNC machining, electrical discharge machining (EDM), grinding, laser cutting, waterjet machining, manual machining (e.g., milling, lathe / turning), or similar techniques, or a combination thereof. The implant may be manufactured by any suitable manufacturing system (e.g., manufacturing system 124 shown in FIG. 1 ). In some embodiments, the implant is manufactured by the manufacturing system executing computer-readable manufacturing instructions generated by a computing system at block 316.

[0096] After the implant is manufactured at block 316, the method 300 may continue at block 318 by performing the selected surgical plan and implanting the patient-specific implant into the patient. Aspects of the surgical plan, such as some or all of the surgical procedure, may be performed manually, by a robotic surgical platform (e.g., a surgical robot), or a combination thereof. In embodiments in which the surgical procedure is performed at least in part by a robotic surgical platform, the surgical plan may include computer-readable control instructions configured to cause the surgical robot to perform at least in part the patient-specific surgical procedure.

[0097] Method 300 may be implemented or performed in a variety of ways. In some embodiments, the operations in blocks 302-314 may be performed by a computing system associated with a first entity, block 316 may be performed by a manufacturing system associated with a second entity, and block 318 may be performed by a surgical provider, surgeon, and / or robotic surgery platform associated with a third entity. Any of the above blocks may be implemented as computer-readable instructions stored in memory and executable by one or more processors of the associated computing systems.

[0098] C. Selected Embodiments of the Surgical Plan Review Program and Associated Methods Figure 4 is a flowchart of a method 400 of providing patient-specific medical care in accordance with an embodiment of the present technology. More specifically, method 400 is directed to reviewing, revising, and / or approving a candidate patient-specific surgical plan using a surgical plan review program, such as the surgical plan described with reference to method 300 of Figure 3 and surgical plan review program 125 of Figure 1, respectively. Thus, method 400 may be a computer method performed via a client computing device, such as a computer, tablet, phone, etc. (e.g., computing device 102 of Figure 1).

[0099] Method 400 may begin by receiving a surgical plan at a client computing device at block 402. In some embodiments, the surgical plan is received at the client computing device by downloading the surgical plan from a server or cloud-based storage platform. In other embodiments, the surgical plan is received at the client computing device directly from another computing system (e.g., system 106) via Wi-Fi, cellular, Bluetooth, NFC, or other communications network.

[0100] After the surgical plan is received at block 402, the method 400 may continue by displaying the surgical plan on the client computing device at block 402. In some embodiments, displaying the surgical plan includes launching the surgical plan review program 125 and / or opening the surgical plan in the surgical plan review program 125. As described throughout this Detailed Description, the surgical plan review program 125 provides a convenient, easy-to-use interface that allows a surgeon or other user to review, comment on, approve, and / or reject the surgical plan.

[0101] In some embodiments, displaying the surgical plan in block 404 includes displaying via a display screen the surgical plan's target sites for surgical intervention, the surgical procedures of the surgical plan, the target anatomical configuration of the surgical plan (e.g., a virtual model of the target anatomical configuration), metrics associated with the target anatomical configuration, a comparison of metrics associated with the current patient anatomy and the target anatomical configuration, a disease progression prediction, a patient outcome prediction, etc. In some embodiments, the display is interactive such that a user (e.g., a surgeon) can toggle between different aspects of the surgical plan, zoom in or out (e.g., on the model of the target anatomical configuration), annotate the plan, provide feedback on the plan, etc. Examples of interactivity of the surgical plan when viewed using the surgical plan review program 125 and features provided by the surgical plan review program 125 are described below with reference to FIGS. 5A-12.

[0102] Method 400 may continue at block 406 by receiving surgeon comments on the surgical plan. The surgeon's comments may include requested changes to the surgical plan, such as, but not limited to, changes to the target site of surgical intervention, the surgical procedure, the target post-operative anatomical configuration, patient metrics associated with the target post-operative anatomical configuration, implant design, implant type, number of implants, etc. The surgeon's comments may also include notes by the surgeon regarding the surgical plan instead of or in addition to requested changes to the surgical plan. The surgeon's comments may also include questions regarding the surgical plan that can be sent back to the computing system for review by a technician. In some embodiments, the surgeon may not have any comments on the surgical plan, in which case the operation at block 406 may be omitted from method 400.

[0103] Method 400 may continue by receiving approval of the surgical plan by the surgeon at block 408 or receiving rejection of the surgical plan by the surgeon at block 410. Method 400 may continue at block 412 by transmitting (a) an indication of the approval or rejection at blocks 408 and 410 and (b) any comments received from the surgeon at block 406. In some embodiments, this includes transmitting an indication to the computing system that originally transmitted the surgical plan to the client computing device and / or uploaded the surgical plan to a server.

[0104] As described above, the surgeon's review of the surgical plan may be facilitated using a surgical plan review program 125, which may be a mobile phone application, a computer application, etc., stored as instructions on a client computing device as a surgical plan review module 123. Figures 5A-12 provide further details regarding the surgical plan review program 125.

[0105] The surgical plan review program 125 may include different dashboards for reviewing surgical plans. For example, FIGS. 5A-5E are screenshots illustrating various aspects of an exemplary case view dashboard 500 of the surgical plan review program 125, in accordance with an embodiment of the present technology. Referring first to FIG. 5A, which is a first screenshot of the case view dashboard 500, the case view dashboard 500 may display multiple patient cases 502a-502d (collectively referred to as "patient cases 502"). Each of the patient cases 502 may correspond to a different patient receiving treatment from a surgeon. As shown, the patient cases 502 displayed on the dashboard may include identifying information that allows the surgeon to distinguish between the cases. Suitable identifying information may include, but is not limited to, a case number, surgery date, surgeon name, patient name, patient medical record number, surgical target, type of surgery, planning stage, etc. In embodiments in which the surgical plan review program 125 is shared by a clinic or system, the patient cases 502 may correspond to patients receiving treatment from the clinic or system (e.g., not limited to patients receiving treatment from a particular surgeon). The case display dashboard 500 may display the patient cases 502 in chronological order by surgery date, as shown in FIG. 5A . In other embodiments, the patient cases 502 may be displayed alphabetically, in chronological order based on the time the case was received, by case status, or other suitable criteria. A user (e.g., a surgeon) can select a particular case from the dashboard 500 for more detailed review. Although only four patient cases 502 are shown, the dashboard 500 can include all current cases for a particular surgeon or healthcare provider, and thus the dashboard 500 may include more or fewer cases than those shown in FIG. 5A . In some embodiments, the dashboard 500 may also include past (e.g., non-current) patient cases 502.

[0106] FIG. 5B is a second screenshot of an exemplary case view dashboard 500 of the surgical plan review program 125 with a specific patient case 502a selected from the multiple patient cases 502. As shown, once a specific patient case 502a is selected, the case view dashboard 500 displays additional details about the specific patient case 502a. For example, in the illustrated embodiment, additional details may be displayed, such as status (e.g., planning, production, post-op, etc.), surgery date, surgeon name, patient name, patient medical record number, MDM, location, institution, type of treatment, and level of treatment. Information other than that shown and described above may also be displayed. In some embodiments, the information may not fit on one screen at a time, and the display may be scrollable so that the user can quickly and easily view more information by simply scrolling up or down on the page.

[0107] The case view dashboard can be modified or organized according to user preferences. For example, FIGS. 5C and 5D are third and fourth screenshots of the exemplary case view dashboard 500 of FIGS. 5A and 5B , illustrating additional features of the case view dashboard 500, in accordance with embodiments of the present technology. Specifically, FIG. 5C illustrates a first view in which completed cases are hidden and has a third selector 507 (e.g., a button) for displaying completed cases, and FIG. 5D illustrates a second view in which completed cases are displayed and has a fourth selector 508 for hiding completed cases. Thus, a user can switch between the views shown in FIGS. 5C and 5D by selecting the corresponding third selector 507 or fourth selector 508. While shown selecting between completed and open cases, in some embodiments, the dashboard 500 can provide options for sorting cases based on additional categories, such as by case stage (e.g., planning, preparation, pre-operative, post-operative, completed, etc.). Dashboard 500 may also be organized and sorted in other ways depending on the user's preferences.

[0108] FIG. 5E is a fifth screenshot of the case view dashboard 500 of the surgical plan review program 125, illustrating a further embodiment of the case view dashboard 500 in accordance with embodiments of the present technology. The embodiment of FIG. 5E is generally similar to the embodiment of FIG. 5B, except that when a particular patient case 502a is selected, the thread for the patient case 502 is not displayed. Instead, the particular patient case 502a is opened in a new screen, window, or tab. The user can nevertheless easily switch between patient cases 502 by simply selecting the return option 503 to return to the home view, such as that shown in FIG. 5A. Like the embodiment shown in FIG. 5B, the embodiment of FIG. 5C also displays details associated with the surgical plan, including the date of surgery, surgeon name, patient name, patient medical record number, MDM, location, institution, type of treatment, and level of treatment. Information other than that shown and described above can also be displayed. The view of the dashboard 500 in FIG. 5C also provides a first selector 504 for viewing files associated with the patient case and a second selector 505 for viewing the surgical plan for the patient case 502a. In some embodiments, the surgical plan review program 125 opens a surgical plan review dashboard to facilitate user review of the surgical plan in response to the user selecting the second selector 505, as described below.

[0109] As mentioned above, the surgical plan review program 125 also includes a surgical plan review dashboard 600 that allows a surgeon or other user to review aspects of the proposed surgical plan. FIGS. 6A-12 illustrate various aspects of a surgical plan review dashboard 600 of the surgical plan review program 125 in accordance with embodiments of the present technology. As described in more detail below, the surgical plan review dashboard 600 allows a surgeon or other user to review various aspects of the proposed patient-specific surgical plan, including pre-operative metrics, predicted post-operative metrics, a virtual model of the patient's anatomy, and the like. In some embodiments, a user can launch the surgical plan review dashboard 600 by selecting the second selector 505 (FIG. 5E) to view the surgical plan from the case view dashboard 500 (see FIGS. 5A-5E).

[0110] FIG. 6A is a first screenshot of the surgical plan review dashboard 600 illustrating various selected features of the surgical plan review dashboard 600. As shown in FIG. 6A, the surgical plan review dashboard 600 includes a menu 630 having various selectors (e.g., virtual buttons, tabs, etc.) that a user can toggle on and off to selectively control the information displayed by the surgical plan review dashboard 600. For example, in FIG. 6A, a first selector 632 is toggled on / selected. As a result of the first selector 623 being “on,” the surgical plan review dashboard 600 displays a first table or chart 610 showing pre-operative patient evaluation metrics and a second table or chart 612 showing predicted post-operative patient evaluation metrics. As described above, the pre-operative patient evaluation metrics in the first table 610 are the patient's current evaluation metrics, and the post-operative patient evaluation metrics in the second table 612 are the patient's predicted post-operative patient evaluation metrics if the surgical plan is approved and performed by the surgeon. In the embodiment shown in FIG. 6A , a second selector 634 labeled “Pre-op” has also been toggled on / selected from the menu 630. As a result of the second selector 634 being “on,” the surgical plan review dashboard 600 displays a pre-operative virtual model 620 (sometimes referred to as the “first virtual model 620”) depicting the pre-operative patient anatomy. The pre-operative virtual model 620 can be a two-dimensional or three-dimensional model of the patient's current (e.g., pre-operative or natural) anatomy in the region of interest. The surgical plan review dashboard 600 further includes a plan feedback selector 605 that is selectable to open a window for commenting on and / or approving the displayed surgical plan, as will be described in more detail with reference to FIGS. 11 and 12 .

[0111] FIG. 6B is a second screenshot of the surgical plan review dashboard 600, illustrating additional dashboard features. More specifically, in FIG. 6B, in addition to the first selector 632, a third selector 635 labeled “Plan” has been toggled on / selected from the menu 630. As a result of the third selector 635 being “on,” the surgical plan review dashboard 600 displays the surgical plan in the form of a predicted postoperative virtual model 622 (sometimes referred to as the “second virtual model 622”). Like the preoperative virtual model 620 of FIG. 6A, the postoperative virtual model 622 can be a two-dimensional or three-dimensional virtual model of the patient anatomy in the region of interest. However, unlike the preoperative virtual model 620, the postoperative virtual model 622 illustrates the predicted postoperative patient anatomy in the region of interest if the surgical plan is approved and performed by the surgeon. In some embodiments, post-operative virtual model 622 can further include one or more virtual implants corresponding to implants that would be implanted in the region of interest if the surgical plan were approved and performed by the surgeon. For example, in the illustrated embodiment, a first virtual implant 626a and a second virtual implant 626b (collectively referred to as "virtual implants 626") are shown in the L4-L5 interbody and L5-S1 interbody vertebral bodies, respectively. This is for illustrative purposes only; more or fewer virtual implants may be displayed in post-operative virtual model 622, depending on the surgical plan. Similarly, types of virtual implants other than interbody implants may be shown, depending on the type of surgical intervention called for in the surgical plan.

[0112] FIG. 6C is a third screenshot of the surgical plan review dashboard 600, illustrating further features of the surgical plan review dashboard. More specifically, in FIG. 6C, both the second selector 634 (“Pre-op”) and the third selector 635 (“Plan”) have been toggled on / selected from the menu 630. As a result of both the second selector 634 and the third selector 635 being “on,” the surgical plan review dashboard 600 displays a comparison virtual model 624 (sometimes referred to as the “third virtual model 624”) showing the post-operative virtual model 622 ( FIG. 6B ) overlaid on the pre-operative virtual model 620 ( FIG. 6A ). In some embodiments, the comparison virtual model 624 is generated or constructed using a comparator of the design platform, such as the comparator described above with reference to the analysis module 116 of FIG. 1 . Thus, the comparison virtual model 624 can use one or more clue features to ensure that the pre-operative virtual model 620 and the post-operative virtual model 622 are properly aligned. In some embodiments, the pre-operative virtual model 620 and the post-operative virtual model 622 are shown in different colors or shading to more easily compare the current patient anatomy to the predicted post-operative patient anatomy using the comparison virtual model 624.

[0113] In some embodiments, the surgical plan review dashboard can display or otherwise indicate additional metrics associated with the comparison virtual model 624. For example, in addition to generating a virtual comparison of the pre-operative virtual model 620 and the post-operative virtual model 622, the comparator can generate a comparison of spinal loading of the spine in the pre-operative virtual model 620 and the predicted post-operative virtual model 622. The loading can be adjusted by the user to simulate different loading conditions. In the case of dynamic loading, the comparator can perform dynamic simulations and provide comparisons between various models to assess how the surgical procedure will affect patient motion.

[0114] In some embodiments, the surgical plan review dashboard 600 is a dynamic interface displayed by the physician device for simultaneously viewing multiple surgical plans and / or virtual models, such as the preoperative virtual model 620, the postoperative virtual model 622, the comparison virtual model 624, and / or additional comparison models. The user can select the number of models and virtual representations for simultaneous display. In some embodiments, each model can be displayed in a separate window, allowing the user to visually manipulate the model, for example, by panning, zooming, cropping, or otherwise manipulating the model. In some embodiments, the windows are synchronized so that rotation of a model in one window results in a corresponding rotation of another model. This allows the user to view different models from the same perspective to visually compare differences between the models. In some embodiments, the surgical plan review dashboard 600 allows real-time viewing of model updates based on simultaneously entered positional inputs. For example, as described below, if a user modifies target postoperative metrics, one or more of the displayed models can be updated in real time to immediately view those inputs.

[0115] 6A-6C illustrate examples of a preoperative virtual model 620, a postoperative virtual model 622, and a comparative virtual model 624 in the lumbar spine region of a patient and simulated surgical interventions at the L4-L5 and L5-S1 levels. However, the preoperative virtual model 620, the postoperative virtual model 622, and the comparative virtual model 624 may illustrate other regions of interest for potential surgical interventions, depending on the specific surgical interventions included in the surgical plan. Additionally, the virtual models may include additional anatomical structures beyond those shown, such as additional bony structures, cartilage, soft tissue, vascular tissue, and neural tissue.

[0116] The surgical plan review dashboard 600 may also enable a user to selectively hide and display various portions / levels of the pre-operative virtual model 620 ( FIG. 6A ) and the post-operative virtual model 622 ( FIG. 6B ). For example, FIGS. 7A and 7B are third and fourth screenshots of the surgical plan review dashboard 600, illustrating the functionality of the program 125 for enabling a user to selectively hide and display various portions / levels of the virtual model. As shown in FIGS. 7A and 7B , the menu 630 includes a fourth selector 736 that, when selected, opens a selector window 702 for selectively hiding and displaying various regions of the virtual model. Specifically, the selector window 702 divides the anatomical structures included in the pre-operative virtual model 620 and / or the post-operative virtual model 622 into various distinct anatomical subregions 703 (shown in the illustrated embodiment as six vertical spinal levels and two virtual implants, although other subdivisions are possible). The selector window 702 further includes a plurality of first selectors 704 that can be selectively toggled by a user to show or hide each individual anatomical subregion 703 of the pre-operative virtual model 620, and a plurality of second selectors 705 that can be selectively toggled by a user to show or hide each individual anatomical subregion 703 of the post-operative virtual model 622. The plurality of second selectors 705 further include an option to selectively show or hide virtual implants 626 (shown as "L4 / L5" and "L5 / S1"). Thus, a user can use the selector window 702 to selectively show or hide any combination of individual anatomical subregions 703, including both pre-operative and post-operative models of the individual anatomical subregions 703.

[0117] Referring to FIG. 7A , each of the plurality of second selectors 705 is switched “on” and each of the first selectors 704 is switched “off.” As a result, each individual anatomical subregion 703 is displayed for the post-operative virtual model 622, and none of the individual anatomical subregions 703 are displayed for the pre-operative virtual model 620. Referring to FIG. 7B , compared to the configuration shown in FIG. 7A , the second selector 705 associated with the L1 and L2 subregions 703 is switched “off,” and thus the L1 and L2 anatomical regions are not displayed for the post-operative virtual model 622. As will be appreciated by those skilled in the art from the disclosure herein, FIGS. 7A and 7B are shown by way of example to illustrate that a user can selectively control which subregions 703 of a virtual model are displayed by the surgical plan review dashboard 600. Any combination of subregions 703 can be displayed using the selector window 702.

[0118] The surgical plan review dashboard 600 may also allow a user to selectively change the opacity or transparency of the displayed virtual model. For example, as shown in FIG. 8 , a fifth screenshot of the surgical plan review dashboard 600, the menu 630 includes a fifth selector 837 that, when selected, opens the virtual model opacity window 810. The vertebral body opacity window 810 includes a sliding selector 811 that allows a user to adjust the opacity or transparency of the displayed virtual model. In the illustrated embodiment, the displayed virtual model is the postoperative virtual model 622, but in other embodiments, the displayed virtual model may be the preoperative virtual model 620 and / or the composite virtual model 624. In some embodiments, sliding the sliding selector 811 changes the opacity of the anatomical structure shown in the displayed virtual model without changing the opacity of the virtual implant 626 shown in the displayed virtual model. In this manner, the virtual model opacity window 810 facilitates better visualization of the virtual implant 626 by allowing the user to “see through” the patient anatomy.

[0119] The surgical plan review dashboard 600 may also allow the user to view the virtual model from various orientations, as shown in FIGS. 9A-9C, which are sixth, seventh, and eighth screenshots of the surgical plan review dashboard 600. For example, referring first to FIG. 9A, the menu 630 includes a sixth selector 938 that, when selected, allows the user to choose from various views for the displayed virtual model. In the illustrated embodiment, the views include coronal, sagittal (PL) (e.g., left sagittal), parasagittal (PR) (e.g., left sagittal), and axial / transverse. FIG. 9A shows a coronal view of the post-operative virtual model 622, FIG. 9B shows a left sagittal view of the post-operative virtual model 622, and FIG. 9C shows an axial view of the post-operative virtual model 622. Depending on the embodiment, the program 125 may provide more or fewer views. 9A-9C illustrate providing various views of the post-operative virtual model 622, the program 125 can also provide the same views of the pre-operative virtual model 620 and the composite virtual model 624. The program 125 can also provide views of only a portion of the virtual model based on selections made using the selector window 702 (FIGS. 7A and 7B).

[0120] The surgical plan review dashboard may also allow a user to selectively display or hide virtual implants 626, as shown in FIGS. 10A and 10B, which are the ninth and tenth screenshots of the surgical plan review dashboard 600. Specifically, a user may choose to display or hide the first virtual implant 626 a and the second virtual implant 626 b using a virtual implant display selector 1020. In some embodiments, this functionality is omitted because the same functionality is provided by the selector window 702 described with reference to FIGS. 7A and 7B. FIG. 10A shows a coronal view in which both virtual implants 626 are visible, while FIG. 10B shows an axial view in which only the first virtual implant 626 a is visible.

[0121] In some embodiments, as shown in FIG. 10B , a second menu 1030 is displayed to the user while the user is viewing the virtual implant 626 in the post-operative virtual model 622. The user can use the second menu 1030 to select various viewing options associated with the virtual implant 626. For example, the user can use the second menu 1030 to select a camera view 1031 that displays the virtual implant 626 from various angles (e.g., left / right sagittal, coronal, and / or axial). The second menu 1030 can also include a measurement selector 1032 that can be selected / toggled on to display various measurements overlaid on the virtual model 622. For example, the measurements can include the distance of the implant 626 to the vertebral disc edge along the anterior, posterior, patient right, and / or left side (as shown in FIG. 10B ) and / or other measurements. The second menu 1030 may also include a selector 1033 for changing the display of the upper vertebrae (e.g., by changing the opacity of the upper vertebrae, by hiding the upper vertebrae, etc.) to allow the user to better see how the implant will fit into the intervertebral space. The second menu 1030 may also include an implant measurements selector 1034 that, when toggled on, causes an implant measurements table 1040 to be displayed. As shown, the implant measurements table 1040 may display various metrics of the virtual implant 626, including, but not limited to, the minimum and maximum height and angle of each side of the virtual implant 626.

[0122] A user can also use the surgical plan review dashboard 600 to approve the surgical plan and / or make suggested changes. For example, FIG. 11 is an eleventh screenshot of the surgical plan review dashboard 600, illustrating the functionality of the program 125 that can receive comments on the surgical plan from a user reviewing the surgical plan using the program 125. More specifically, FIG. 11 shows the surgical plan review dashboard 600 after the plan feedback selector 605 has been selected by the user. In response to the plan feedback selector 605 being selected, the surgical plan review dashboard 600 displays an “Approve Plan” option 1112 and a “Suggest Changes” option 1114. In the embodiment shown in FIG. 11 , the user has selected the suggest changes option 1114. As a result, as shown in FIG. 11 , the surgical plan review dashboard 600 opens a comments window 1115 and a user input mechanism 1116 (e.g., a touchscreen keyboard, a physical keyboard, a microphone, a stylus, etc.). A user (e.g., a surgeon) can enter feedback, suggestions, or comments regarding the surgical plan in a comments window 1115 using user input mechanism 1116. Program 125 can then save those inputs (e.g., comments, target postoperative metrics, disapproval metrics, etc.) with the surgical plan and, optionally, transmit those inputs to a remote computing system for surgical plan refinement, as described above with respect to block 310 of method 300 (FIG. 3). The embodiment shown in FIG. 12, a twelfth screenshot of surgical plan review dashboard 600, illustrates dashboard 600 after selection of plan approval option 1112. In response to a user selecting plan approval option 1112, dashboard 600 opens an approval window 1215 that allows the user (e.g., a surgeon) to authenticate and confirm their approval of the surgical plan. Program 125 can then transmit an indication to the remote computing system that the user has approved the surgical plan, as described with reference to block 308 of method 300 (FIG. 3) and block 412 of method 400 (FIG. 4).

[0123] As described above throughout this Detailed Description, a design platform (e.g., system 100 of FIG. 1 ) can design an implant based on the approved surgical plan (e.g., if the implant has not been designed and sent with the proposed surgical plan). For example, the design platform can design a virtual implant that fits into a virtual anatomical model of the patient corresponding to the approved surgical plan such that the implant will result in a predicted post-operative anatomy. The design platform can generate CAD data, manufacturing data, manufacturing instructions, or other data for a manufacturing system (e.g., manufacturing system 124 of FIG. 1 ).

[0124] As mentioned above, FIGS. 5A-12 illustrate various aspects of the surgical plan review program 125 that enable a surgeon or other user to review, approve, and / or provide feedback on a proposed surgical plan. As previously mentioned, the surgical plan review program 125 may include a case view dashboard 500 ( FIGS. 5A-5E ) and a surgical plan review dashboard 600 ( FIGS. 6A-12 ). As will be appreciated by those skilled in the art, the case view dashboard 500 and / or the surgical plan review dashboard 600 may include more or less functionality than that described above with respect to FIGS. 5A-12 . Additionally, the case view dashboard 500 and / or the surgical plan review dashboard 600 may have a different appearance than that shown in FIGS. 5A-12 , including a different configuration, color pattern, organization, etc.

[0125] Without intending to be bound by theory, the surgical plan review program 125, including the case view dashboard 500 and the surgical plan review dashboard 600, is expected to provide several advantages. For example, the case view dashboard 500 is expected to enable a clinic, surgeon, or other user to easily track and sort any number of unique patient cases. The surgical plan review dashboard 600 is also expected to enable a surgeon or other user to thoroughly review a proposed surgical plan for a patient, including predicted postoperative patient anatomy and evaluation metrics. The surgical plan review dashboard also allows a surgeon or other user to easily communicate any suggested feedback or changes to the surgical plan, which can be transmitted back to a remote computing system as described above with reference to FIGS. 3 and 4. Thus, in various embodiments, the surgical plan review program 125 described herein is expected to improve the efficiency of case management, surgeon review of proposed surgical plans, and surgeon review of proposed surgical plans and / or patient outcomes.

[0126] As will be appreciated by those skilled in the art, any of the aforementioned software modules may be combined into a single software module to perform the operations described herein. Similarly, the software modules may be distributed across any combination of the computing systems and devices described herein and are not limited to the explicit configurations described herein. Thus, unless otherwise specified, any of the operations described herein may be performed by any of the computing devices or systems described herein. [Example]

[0127] The following examples describe some aspects of the present technology.

[0128] Example 1 1. A method of designing one or more patient-specific spinal implants, comprising: constructing a first spine model representing the patient's preoperative spinal anatomy, the first spine model including vertebral elements; generating a second spine model representing a predicted post-operative spinal anatomy of the patient based on the one or more surgical reductions; generating at least one visual comparison of the first spine model and the second spine model using a comparator of the design platform; linking the first spine model and the second spine model to a dynamic interface displayed by the physician device for simultaneously viewing the first spine model, the second spine model, and at least one visual comparison; Dynamic browsing interface displaying the first spine model, the second spine model, and at least one visual comparison; dynamically displaying one or more spinal assessment metrics corresponding to the anatomical element selected for viewing by the user; configured to receive physician input entered into the dynamic interface; linking the first spine model and the second spine model; generating, via the design platform, a modified second spine model representing the patient's predicted postoperative anatomy for the target postoperative metrics in response to the received physician input including one or more target postoperative metrics, wherein the modified second spine model and at least one visual comparison between the first spine model and the modified second spine model generated by the comparator are viewable using the dynamic interface; and designing one or more spinal implants using a design platform based on one of the second spinal model or the modified second spinal model approved by a user, such that the one or more spinal implants, when implanted in a patient, result in a predicted post-operative anatomy of the approved second spinal model or the modified second spinal model.

[0129] Example 2 2. The method of example 1, further comprising using a comparator to digitally overlay the first model and the second model to generate at least one visual comparison.

[0130] Example 3 The method of example 1 or example 2, wherein linking the first spine model and the second spine model to a dynamic interface enables real-time viewing of model updates based on concurrently entered physician input.

[0131] Example 4 4. The method of any one of embodiments 1-3, wherein the linking is via a wide area network between the design platform and the user device.

[0132] Example 5 The method of any of Examples 1-4, further comprising performing one or more clinical checks on the approved second spine model or the approved modified second spine model.

[0133] Example 6 6. The method of any of Examples 1-5, wherein the linking enables panning, rotation, and / or zooming of one or more of the first spine model or the second spine model for visual comparison via a dynamic interface.

[0134] Example 7 7. The method of any of Examples 1-6, further comprising synchronizing pan, rotation, and / or zoom of the first spine model and the second spine model for display by the dynamic interface.

[0135] Example 8 8. The method of any of examples 1-7, wherein at least one visual comparison displayed in the dynamic interface indicates a difference in spinal loading between the patient's spine in the first spine model and the patient's spine in the second spine model.

[0136] Example 9 1. A system for patient-specific surgical planning, comprising: a first virtual model of the patient's preoperative anatomy; a second virtual model of the patient's predicted postoperative anatomy; and a third virtual model of the predicted postoperative anatomy overlaid on the preoperative anatomy; and a processor; a memory storing non-transient instructions for a surgical plan review program; The system, wherein the surgical plan review program allows a user to view the first virtual model, the second virtual model, and the third virtual model.

[0137] Example 10 The system of Example 9, wherein the surgical plan review program includes one or more virtual model selectors that allow a user to selectively switch between viewing the first virtual model, the second virtual model, and the third virtual model.

[0138] Example 11 The system of example 9 or example 10, wherein the surgical plan review program includes one or more orientation selectors that allow the user to selectively change the viewing orientation of the first virtual model, the second virtual model, and the third virtual model.

[0139] Example 12 12. The system of any of Examples 9-11, wherein the third virtual model shows the predicted postoperative anatomy in a different color and / or pattern than the preoperative anatomy.

[0140] Example 13 A system described in any of Examples 9 to 12, wherein the surgical plan review program further displays anatomical evaluation metrics associated with any of the displayed virtual models, including the first virtual model, the second virtual model, and the third virtual model.

[0141] Example 14 A system described in any of Examples 9 to 13, wherein each virtual model includes multiple individual subregions and the surgical plan review program provides one or more selectors for selectively displaying and hiding each of the individual subregions.

[0142] Example 15 The system of Example 14, wherein the distinct subregions are individual spinal levels.

[0143] Example 16 A non-transitory computer-readable medium storing instructions associated with a surgical plan review program for reviewing a patient-specific surgical plan using a computing device, the instructions including first instructions and second instructions; The first instructions, when executed, cause the computing device to display via the display screen a first digital dashboard, the first digital dashboard including a plurality of patient cases associated with a plurality of patients, each of the plurality of patient cases including a proposed surgical plan for treating a corresponding patient; The second instructions, when executed, cause the computing device to display a second digital dashboard via the display screen, the second digital dashboard comprising: a menu for selecting to view one or more features associated with a proposed surgical plan for a particular patient of the plurality of patients, the one or more features including a virtual model of the patient's predicted post-operative anatomy if the surgical plan is performed and predicted post-operative anatomical evaluation metrics associated with the predicted post-operative anatomy; and a plan feedback selector, wherein in response to a user selecting the plan feedback selector, a second digital dashboard enables the user to provide feedback on and / or approve the proposed surgical plan.

[0144] Example 17 17. The non-transitory computer-readable medium of Example 16, wherein the second instructions are executed in response to a user selecting a proposed surgical plan for a particular patient in the first dashboard for viewing.

[0145] Example 18 The non-transitory computer-readable medium of Example 16 or Example 17, wherein the second digital dashboard is interactive and configured to receive user input via the input mechanism, and in response to the user input, the second digital dashboard modifies one or more features associated with the surgical plan displayed for user review.

[0146] Example 19 19. The non-transitory computer-readable medium of any of Examples 16-18, wherein the one or more features associated with the surgical plan further include one or more virtual implants.

[0147] Example 20 20. The non-transitory computer-readable medium of any of Examples 16-19, wherein the virtual model comprises one or more virtual implants.

[0148] Example 21 21. The non-transitory computer-readable medium of any of Examples 16-20, wherein the virtual model is a first virtual model and the one or more features associated with the surgical plan further include a second virtual model of the pre-operative patient anatomy.

[0149] Example 22 22. The non-transitory computer-readable medium of Example 21, wherein the one or more features associated with the surgical plan further include a third virtual model, the third virtual model including both the first virtual model and the second virtual model to compare a predicted postoperative patient anatomy with a preoperative patient anatomy.

[0150] Example 23 The non-transitory computer-readable medium of Examples 16 to 22, wherein the predictive postoperative anatomical evaluation index includes coronal parameters, sagittal parameters, pelvic parameters, Cobb angle, shoulder slope, iliopsoas angle, coronal balance, lordosis angle, and / or intervertebral space height.

[0151] Example 24 A non-transitory computer-readable medium described in any of Examples 16 to 23, wherein the virtual model includes a plurality of individual subregions and the menu includes a selector for selectively displaying and hiding each individual subregion of the plurality of individual subregions.

[0152] Example 25 25. The non-transitory computer-readable medium of Example 24, wherein the individual subregions of the plurality of individual subregions correspond to individual spinal heights.

[0153] Example 26 A non-transitory computer-readable medium described in any of Examples 16 to 25, wherein the menu includes a selector for changing the transparency of the virtual model.

[0154] Example 27 27. A non-transitory computer-readable medium according to any one of Examples 16 to 26, wherein the menu includes a selector for changing the viewing orientation of the virtual model.

[0155] Example 28 28. The non-transitory computer-readable medium of Example 27, wherein the selector provides a plurality of viewing orientations, the plurality of viewing orientations comprising a coronal view, a sagittal view, and an axial view.

[0156] Example 29 A non-transitory computer-readable method as described in any of Examples 16 to 28, wherein the surgical review program is configured to associate any feedback received from the user via the plan feedback selector with the proposed surgical plan.

[0157] Example 30 1. A computer-implemented method for reviewing a patient-specific surgical plan for a patient using a surgical plan review program, comprising: displaying a first digital dashboard, the first digital dashboard including a plurality of patient cases associated with a plurality of patients, each of the plurality of patient cases including a proposed surgical plan for treating a corresponding patient; receiving, from a user, a selection of a particular patient case from the plurality of patient cases; displaying a second digital dashboard in response to receiving the selection of the particular patient case, the second digital dashboard comprising: a menu for selecting to view one or more features associated with a proposed surgical plan for a particular patient, the one or more features including a virtual model of the patient's predicted post-operative anatomy if the surgical plan is performed and predicted post-operative anatomical evaluation metrics associated with the predicted post-operative anatomy; a plan feedback selector for receiving feedback on and / or approving the proposed surgical plan; displaying a second digital dashboard; and receiving a selection of a plan feedback selector; In response to receiving a selection of the plan feedback selector, displaying a field for receiving comments from the user and / or displaying a selector for approving the proposed surgical plan; and receiving from the user feedback on the proposed surgical plan via a field and / or approval of the proposed surgical plan via a selector.

[0158] Example 31 Before receiving your Plan Feedback Selector selection, receiving, via a menu, a selection of one or more features associated with the surgical plan; 31. The computer-implemented method of example 30, comprising, in response to receiving the selection, updating the second digital dashboard to a display for viewing the selected feature.

[0159] Example 32 The computer-implemented method of example 30 or example 31, wherein the one or more features associated with the surgical plan further include one or more virtual implants.

[0160] Example 33 33. The computer-implemented method of any of Examples 30-32, wherein the virtual model is a first virtual model and the one or more features associated with the surgical plan further include a second virtual model of the preoperative patient anatomy.

[0161] Example 34 The computer-implemented method of Example 33, wherein the one or more features associated with the surgical plan further include a third virtual model, the third virtual model including both the first virtual model and the second virtual model to compare the predicted postoperative patient anatomy with the preoperative patient anatomy.

[0162] Example 35 35. The computer-implemented method of any of Examples 30-34, wherein the predicted postoperative anatomical evaluation index includes coronal parameters, sagittal parameters, pelvic parameters, Cobb angle, shoulder slope, iliopectineal angle, coronal balance, lordosis angle, and / or intervertebral space height.

[0163] Example 36 A computer-implemented method described in any of Examples 30 to 35, wherein the virtual model includes a plurality of individual subregions and the menu includes a selector for selectively displaying and hiding each individual subregion of the plurality of individual subregions.

[0164] The above detailed description sets forth various embodiments of devices and / or processes using block diagrams, flowcharts, and / or examples. To the extent that such block diagrams, flowcharts, and / or examples include one or more functions and / or operations, those skilled in the art will recognize that each function and / or operation within such block diagrams, flowcharts, or examples can be implemented individually and / or in combination by a wide range of hardware, software, firmware, or virtually any combination thereof. In some embodiments, portions of the subject matter described herein can be implemented by an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), or other integrated form. However, those skilled in the art will recognize that some aspects of the embodiments disclosed herein may equivalently be implemented in whole or in part in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as substantially any combination thereof, and that designing such circuitry and / or writing code for such software and / or firmware would be well within the skill of one of ordinary skill in the art in light of this disclosure. Furthermore, those skilled in the art will recognize that the subject matter described herein can be distributed as a program product in a variety of forms, and that exemplary embodiments of the subject matter described herein apply regardless of the particular type of signal-bearing medium actually used to effect the distribution.Examples of signal-bearing media include, but are not limited to, recordable media such as floppy disks, hard disk drives, CDs, DVDs, digital tape, computer memory, and transmission media such as digital and / or analog communications media (e.g., fiber optic cables, wave guides, wired communications links, wireless communications links, etc.).

[0165] Those skilled in the art will recognize that it is common in the art to describe devices and / or processes as described herein and then use engineering practices to incorporate such described devices and / or processes into a data processing system. That is, at least a portion of the devices and / or processes described herein can be incorporated into a data processing system with a reasonable amount of experimentation. Those skilled in the art will recognize that a typical data processing system generally includes one or more of the following: a system unit housing; a video display device; memory, such as volatile and non-volatile memory; a processor, such as a microprocessor and a digital signal processor; computer entities, such as an operating system, drivers, a graphical user interface, and application programs; one or more interaction devices, such as a touchpad or screen; and / or a control system, including feedback loops and control motors (e.g., feedback for sensing position and / or velocity, control motors for moving and / or adjusting components and / or quantities). A typical data processing system can be implemented using any suitable commercially available components, such as those commonly found in data computing / communications and / or network computing / communications systems.

[0166] The subject matter described herein may depict different components housed within or connected to different other components. It should be understood that such depicted architectures are merely examples, and that in fact many other architectures that achieve the same functionality are possible. In a conceptual sense, any configuration of components to achieve the same functionality is effectively “associated” such that the desired functionality is achieved. Thus, any two components herein that combine to achieve a particular functionality can be considered to be “associated” with each other such that the desired functionality is achieved, regardless of the architecture or intermediate components. Similarly, any two components so associated can also be considered to be “operably connected” or “operably coupled” to each other to achieve the desired functionality, and any two components that can be so associated can also be considered to be “operably coupleable” to each other to achieve the desired functionality. Specific examples of operably coupleable include, but are not limited to, physically coupled and / or physically interactable components and / or wirelessly interactable and / or wirelessly interacting components and / or logically interacting and / or logically interacting components.

[0167] The embodiments, features, systems, devices, materials, methods, and techniques described herein may, in some embodiments, be similar to any one or more of the embodiments, features, systems, devices, materials, methods, and techniques described in the following documents: U.S. Patent Application No. 16 / 048,167, filed July 27, 2018, entitled "SYSTEMS AND METHODS FOR ASSISTING AND AUGMENTING SURGICAL PROCEDURES"; U.S. Patent Application No. 16 / 242,877, filed January 8, 2019, entitled "SYSTEMS AND METHODS OF ASSISTING A SURGEON WITH SCREW PLACEMENT DURING SPINAL SURGERY"; U.S. Patent Application No. 16 / 207,116, filed December 1, 2018, entitled "SYSTEMS AND METHODS FOR MULTI-PLANAR ORTHOPEDIC ALIGNMENT"; U.S. Patent Application No. 16 / 352,699, filed March 13, 2019, entitled "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANT FIXATION"; U.S. Patent Application No. 16 / 383,215, entitled "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANT FIXATION," filed April 12, 2019; U.S. Patent Application No. 16 / 569,494, entitled "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANTS," filed September 12, 2019; U.S. Patent Application No. 16 / 699,447, entitled "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANTS," filed November 29, 2019; U.S. Patent Application No. 16 / 735,222, filed January 6, 2020, entitled "PATIENT-SPECIFIC MEDICAL PROCEDURES AND DEVICES, AND ASSOCIATED SYSTEMS AND METHODS"; U.S. Patent Application No. 16 / 987,113, filed August 6, 2020, entitled "PATIENT-SPECIFIC ARTIFICIAL DISCS, IMPLANTS AND ASSOCIATED SYSTEMS AND METHODS"; U.S. Patent Application No. 16 / 990,810, filed August 11, 2020, entitled "LINKING PATIENT-SPECIFIC MEDICAL DEVICES WITH PATIENT-SPECIFIC DATA, AND ASSOCIATED SYSTEMS, DEVICES AND METHODS"; U.S. Patent Application No. 17 / 085,564, filed October 30, 2020, entitled "SYSTEMS AND METHODS FOR DESIGNING ORTHOPEDIC IMPLANTS BASED ON TISSUE CHARACTERISTICS"; U.S. Patent Application No. 17 / 100,396, entitled "PATIENT-SPECIFIC VERTEBRAL IMPLANTS WITH POSITIONING FEATURES," filed November 20, 2020; U.S. Patent Application No. 17 / 342,439, filed June 8, 2021, entitled "PATIENT-SPECIFIC MEDICAL PROCEDURES AND DEVICES, AND ASSOCIATED SYSTEMS AND METHODS"; U.S. Patent Application No. 17 / 463,054, filed August 31, 2021, entitled "BLOCKCHAIN ​​MANAGED MEDICAL IMPLANTS" U.S. Patent Application No. 17 / 518,524, filed November 3, 2021, entitled "PATIENT-SPECIFIC ARTHROPLASTY DEVICES AND ASSOCIATED SYSTEMS AND METHODS"; U.S. Patent Application No. 17 / 531,417, filed November 19, 2021, entitled "PATIENT-SPECIFIC JIG FOR PERSONALIZED SURGERY"; U.S. Patent Application No. 17 / 678,874, filed February 23, 2022, entitled "NON-FUNGIBLE TOKEN SYSTEMS AND METHODS FOR STORING AND ACCESSING HEALTHCARE DATA"; U.S. Patent Application No. 17 / 835,777, filed June 8, 2022, entitled "PATIENT-SPECIFIC EXPANDABLE INTERVERTEBRAL IMPLANTS"; U.S. Patent Application No. 17 / 842,242, filed June 16, 2022, entitled "PATIENT-SPECIFIC ANTERIOR PLATE IMPLANTS"; U.S. Patent Application No. 17 / 851,487, filed June 28, 2022, entitled "PATIENT-SPECIFIC ADJUSTMENT OF SPINAL IMPLANTS, AND ASSOCIATED SYSTEMS AND METHODS"; U.S. Patent Application No. 17 / 856,625, filed July 1, 2022, entitled "SPINAL IMPLANTS FOR MESH NETWORKS"; U.S. Patent Application No. 17 / 867,621, filed July 18, 2022, entitled "PATIENT-SPECIFIC SACROILIAC IMPLANT, AND ASSOCIATED SYSTEMS AND METHODS"; U.S. Patent Application No. 17 / 868,729, filed July 19, 2022, entitled "SYSTEMS FOR PREDICTING INTRAOPERATIVE PATIENT MOBILITY AND IDENTIFYING MOBILITY-RELATED SURGICAL STEPS"; U.S. Patent Application No. 17 / 978,673, filed November 1, 2022, entitled "SPINAL IMPLANTS AND SURGICAL PROCEDURES WITH REDUCED SUBSIDENCE, AND ASSOCIATED SYSTEMS AND METHODS"; U.S. Patent Application No. 17 / 978,746, filed November 1, 2022, entitled "PATIENT-SPECIFIC SPINAL INSTRUMENTS FOR IMPLANTING IMPLANTS AND DECOMPRESSION PROCEDURES"; U.S. Patent Application No. 18 / 102,444, filed January 27, 2023, entitled "TECHNIQUES TO MAP THREE-DIMENSIONAL HUMAN ANATOMY DATA TO TWO-DIMENSIONAL HUMAN ANATOMY DATA"; U.S. Patent Application No. 18 / 113,573, filed February 24, 2023, entitled "PATIENT-SPECIFIC IMPLANT DESIGN AND MANUFACTURING SYSTEM WITH A DIGITAL FILING CABINET"; U.S. Patent Application No. 18 / 120,979, filed March 13, 2023, entitled "MULTI-STAGE PATIENT-SPECIFIC SURGICAL PLANS AND SYSTEMS AND METHODS FOR CREATING AND IMPLEMENTING THE SAME"; U.S. Patent Application No. 18 / 455,881, filed August 25, 2023, entitled "SYSTEMS AND METHODS FOR GENERATING MULTIPLE PATIENT-SPECIFIC SURGICAL PLANS AND MANUFACTURING PATIENT-SPECIFIC IMPLANTS"; U.S. Patent Application No. 17 / 951,085, filed September 22, 2022, entitled "SYSTEMS FOR MANUFACTURING AND PRE-OPERATIVE INSPECTING OF PATIENT-SPECIFIC IMPLANTS"; U.S. Patent Application No. 63 / 420,279, filed October 28, 2022, entitled "SYSTEMS AND METHODS FOR SELECTING, REVIEWING, MODIFYING AND / OR APPROVING SURGICAL PLANS"; U.S. Patent Application No. 63 / 387,009, filed December 12, 2022, entitled "PATIENT-SPECIFIC IMPLANT DESIGN AND MANUFACTURING SYSTEM WITH A REGULATORY AND REIMBURSEMENT MANAGER"; U.S. Patent Application No. 63 / 436,860, filed January 3, 2023, entitled "PATIENT-SPECIFIC SPINAL FUSION DEVICES AND ASSOCIATED SYSTEMS AND METHODS"; U.S. Patent Application No. 63 / 437,966, filed January 9, 2023, entitled "SYSTEM FOR EDGE CASE PATHOLOGY IDENTIFICATION AND IMPLANT MANUFACTURING" U.S. Patent No. 63 / 437,975, entitled "SYSTEM FOR MODELING PATIENT SPINAL CHANGES," filed January 9, 2023; U.S. Patent No. 63 / 522,815, filed June 23, 2023, entitled "SYSTEMS AND METHODS FOR DIAGNOSING SPINAL CONDITIONS AND DETERMINING TREATMENT OF THE SAME"; U.S. Patent Application No. 63 / 530,427, filed August 2, 2023, entitled "MEDICAL DEVICE INSERTER INSTRUMENTS WITH RETRACTABLE COUPLING ELEMENTS AND METHODS OF USING THE SAME," and U.S. Patent Application No. 63 / 542,264, filed October 3, 2023, entitled "PATIENT-SPECIFIC SURGICAL POSITIONING GUIDES AND METHODS OF MAKING AND USING THE SAME."

[0168] All of the above patents and patent applications are incorporated by reference in their entirety. Furthermore, the embodiments, features, systems, devices, materials, methods, and techniques described herein may, in a particular embodiment, be applicable to or usable in connection with any one or more of those embodiments, features, systems, devices, or other items.

[0169] Ranges disclosed herein encompass all overlaps, subranges, and combinations thereof. Expressions such as "up to," "at least," "greater than," "less than," and "to" include the recited numbers. As used herein, numbers preceded by terms such as "approximately," "about," and "substantially" are inclusive of the recited number (e.g., about 10% = 10%) and also refer to an amount close to the recited amount that performs the desired function or achieves the desired result. For example, the terms "approximately," "about," and "substantially" can refer to an amount that is within less than 10%, less than 5%, less than 1%, less than 0.1%, and less than 0.01% of the recited amount.

[0170] From the foregoing, it will be appreciated that various embodiments of the present disclosure have been described herein for purposes of illustration, and that various modifications can be made without departing from the scope and spirit of the present disclosure. Accordingly, the various embodiments disclosed herein are not intended to be limiting.

Claims

1. 1. A method of designing one or more patient-specific spinal implants, comprising: constructing a first spine model representing the patient's preoperative spinal anatomy, the first spine model including vertebral elements; generating a second spine model representing a predicted post-operative spinal anatomy of the patient based on one or more surgical reductions; generating at least one visual comparison of the first spine model and the second spine model using a comparator of a design platform; linking the first spine model and the second spine model to a dynamic interface displayed by a physician device for simultaneously viewing the first spine model, the second spine model, and the at least one visual comparison; The dynamic browsing interface comprises: displaying the first spine model, the second spine model, and the at least one visual comparison; dynamically displaying one or more spinal assessment metrics corresponding to the anatomical element selected for viewing by the user; configured to receive physician input entered into the dynamic interface; linking the first spine model and the second spine model; generating, via the design platform, a modified second spine model representing the patient's predicted post-operative anatomical structure for the target post-operative metrics in response to the received physician input including one or more target post-operative metrics, wherein the modified second spine model and at least one visual comparison of the modified second spine model with the first spine model generated by the comparator are viewable using the dynamic interface; and designing the one or more spinal implants using the design platform based on one of the second spinal model or the modified second spinal model approved by the user, such that the one or more spinal implants, when implanted in the patient, result in the predicted post-operative anatomy of the approved second spinal model or the modified second spinal model.

2. The method of claim 1 , further comprising using the comparator to digitally overlay the first model and the second model to generate the at least one visual comparison.

3. 10. The method of claim 1, wherein linking the first spine model and the second spine model to the dynamic interface enables real-time viewing of model updates based on concurrently entered physician input.

4. The method of claim 1 , wherein the linking is via a wide area network between the design platform and a user device.

5. The method of claim 1 , further comprising performing one or more clinical checks on the approved second spine model or the approved modified second spine model.

6. 10. The method of claim 1, wherein the linking enables panning, rotation, and / or zooming of one or more of the first spine model or the second spine model for visual comparison via the dynamic interface.

7. The method of claim 1 , further comprising synchronizing panning, rotation, and / or zooming of the first spine model and the second spine model for display by the dynamic interface.

8. 2. The method of claim 1, wherein the at least one visual comparison displayed in the dynamic interface indicates a difference in spinal loading between the patient's spine in the first spine model and the patient's spine in the second spine model.

9. 1. A system for patient-specific surgical planning, comprising: a first virtual model of the patient's preoperative anatomy; a second virtual model of the patient's predicted post-operative anatomy; and a third virtual model of the predicted post-operative anatomy overlaid on the pre-operative anatomy; and a processor; a memory storing non-transient instructions for a surgical plan review program; The system, wherein the surgical plan review program allows a user to view the first virtual model, the second virtual model, and the third virtual model.

10. 10. The system of claim 9, wherein the surgical plan review program includes one or more virtual model selectors that allow a user to selectively switch between viewing the first virtual model, the second virtual model, and the third virtual model.

11. 10. The system of claim 9, wherein the surgical plan review program includes one or more orientation selectors that allow a user to selectively change a viewing orientation of the first virtual model, the second virtual model, and the third virtual model.

12. The system of claim 9 , wherein the third virtual model shows the predicted post-operative anatomy in a different color and / or pattern than the pre-operative anatomy.

13. 10. The system of claim 9, wherein the surgical plan review program further displays anatomical evaluation metrics associated with any of the first, second, and third virtual models being displayed.

14. 10. The system of claim 9, wherein each virtual model includes a plurality of individual sub-regions, and wherein the surgical plan review program provides one or more selectors for selectively displaying and hiding each of the individual sub-regions.

15. The system of claim 14 , wherein the discrete subregions are discrete spinal levels.

16. A non-transitory computer-readable medium storing instructions associated with a surgical plan review program for reviewing a patient-specific surgical plan using a computing device, the instructions including first instructions and second instructions; When the first instructions are executed, the computing device displays a first digital dashboard via a display screen, the first digital dashboard including a plurality of patient cases associated with a plurality of patients, each of the plurality of patient cases including a proposed surgical plan for treating the corresponding patient; When the second instructions are executed, the computing device displays a second digital dashboard via the display screen, the second digital dashboard comprising: a menu for selecting to view one or more features associated with the proposed surgical plan for a particular patient of the plurality of patients, the one or more features including a virtual model of a predicted post-operative anatomy of the patient if the surgical plan is performed and predicted post-operative anatomical evaluation metrics associated with the predicted post-operative anatomy; a plan feedback selector, wherein in response to a user selecting the plan feedback selector, the second digital dashboard enables the user to provide feedback on and / or approve the proposed surgical plan.

17. 17. The non-transitory computer-readable medium of claim 16, wherein the second instructions are executed in response to a user selecting the proposed surgical plan for the particular patient in the first dashboard for viewing.

18. 17. The non-transitory computer-readable medium of claim 16, wherein the second digital dashboard is interactive and configured to receive user input via an input mechanism, and in response to the user input, the second digital dashboard modifies the one or more features associated with the surgical plan displayed for user review.

19. 17. The non-transitory computer-readable medium of claim 16, wherein the one or more features associated with the surgical plan further include one or more virtual implants.

20. 17. The non-transitory computer-readable medium of claim 16, wherein the virtual model includes one or more virtual implants.

21. 17. The non-transitory computer-readable medium of claim 16, wherein the virtual model is a first virtual model and the one or more features associated with the surgical plan further include a second virtual model of a pre-operative patient anatomy.

22. 22. The non-transitory computer-readable medium of claim 21, wherein the one or more features associated with the surgical plan further include a third virtual model, the third virtual model including both the first virtual model and the second virtual model to compare a predicted post-operative patient anatomy to a pre-operative patient anatomy.

23. 17. The non-transitory computer-readable medium of claim 16, wherein the predictive postoperative anatomical evaluation metrics include coronal parameters, sagittal parameters, pelvic parameters, Cobb angle, shoulder slope, iliopectineal angle, coronal balance, lordosis angle, and / or intervertebral space height.

24. 17. The non-transitory computer-readable medium of claim 16, wherein the virtual model includes a plurality of individual sub-regions, and the menu includes a selector for selectively showing and hiding each individual sub-region of the plurality of individual sub-regions.

25. 25. The non-transitory computer readable medium of claim 24, wherein individual subregions of the plurality of individual subregions correspond to individual spinal heights.

26. The non-transitory computer-readable medium of claim 16 , wherein the menu includes a selector for changing the transparency of the virtual model.

27. The non-transitory computer-readable medium of claim 16 , wherein the menu includes a selector for changing a viewing orientation of the virtual model.

28. 28. The non-transitory computer-readable medium of claim 27, wherein the selector provides a plurality of viewing orientations, the plurality of viewing orientations including a coronal view, a sagittal view, and an axial view.

29. 17. The non-transitory, computer readable method of claim 16, wherein the surgical review program is configured to associate any feedback received from the user via the plan feedback selector with the proposed surgical plan.

30. 1. A computer-implemented method for reviewing a patient-specific surgical plan for a patient using a surgical plan review program, comprising: displaying a first digital dashboard, the first digital dashboard including a plurality of patient cases associated with a plurality of patients, each of the plurality of patient cases including a proposed surgical plan for treating a corresponding patient; receiving a selection of a particular patient case from the plurality of patient cases from a user; displaying a second digital dashboard in response to receiving the selection of a particular patient case, the second digital dashboard comprising: a menu for selecting to view one or more features associated with the proposed surgical plan for the particular patient, the one or more features including a virtual model of the patient's predicted post-operative anatomy if the surgical plan is performed and predicted post-operative anatomical metrics associated with the predicted post-operative anatomy; a plan feedback selector for receiving feedback on and / or approving the proposed surgical plan. displaying the second digital dashboard; and receiving a selection of the plan feedback selector; in response to receiving a selection of the plan feedback selector, displaying a field for receiving comments from the user and / or displaying a selector for approving the proposed surgical plan; receiving feedback on the proposed surgical plan from the user via the field and / or approval of the proposed surgical plan via the selector.

31. prior to receiving a selection of said plan feedback selector; receiving, via the menu, a selection of the one or more features associated with the surgical plan; 31. The computer-implemented method of claim 30, further comprising: in response to receiving the selection, updating the second digital dashboard to a display viewing the selected feature.

32. 31. The computer-implemented method of claim 30, wherein the one or more features associated with the surgical plan further include one or more virtual implants.

33. 31. The computer-implemented method of claim 30, wherein the virtual model is a first virtual model and the one or more features associated with the surgical plan further include a second virtual model of pre-operative patient anatomy.

34. 34. The computer-implemented method of claim 33, wherein the one or more features associated with the surgical plan further include a third virtual model, the third virtual model including both the first virtual model and the second virtual model to compare a predicted post-operative patient anatomy to a pre-operative patient anatomy.

35. 31. The computer-implemented method of claim 30, wherein the predictive postoperative anatomical evaluation metrics include coronal parameters, sagittal parameters, pelvic parameters, Cobb angle, shoulder slope, iliopectineal angle, coronal balance, lordosis angle, and / or intervertebral space height.

36. 31. The computer-implemented method of claim 30, wherein the virtual model includes a plurality of individual sub-regions, and the menu includes a selector for selectively showing and hiding each individual sub-region of the plurality of individual sub-regions.

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