Patient-specific implant design and manufacturing system including a surgical implant positioning manager

Abstract

Systems and methods are disclosed for designing and implementing patient-specific surgical procedures and / or medical devices. In some embodiments, the methods include receiving intra-operative data during a surgical procedure and installing a patient-specific implant in the patient. The system can compare the intra-operative data with a pre-operative plan to determine whether the implant is positioned and placed according to the pre-operative plan.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation of U.S. Patent Application No. 18 / 415,577, entitled "PATIENT-SPECIFIC IMPLANT DESIGN AND MANUFACTURING SYSTEM WITH A SURGICAL IMPLANT POSITIONING MANAGER," filed January 17, 2024, which claims priority to and benefit of U.S. Provisional Patent Application No. 63 / 443,676, filed February 6, 2023, the contents of which are incorporated herein by reference in their entireties.

[0002] The present disclosure relates generally to the design, manufacture, and performance of medical care, and more particularly to systems and methods for monitoring patient-specific surgical procedures and / or intra-operative positioning of medical devices. [Background technology]

[0003] Numerous types of data related to patient treatment and surgical interventions are available. To determine a patient's treatment protocol, physicians often rely on a subset of patient data available via the patient's medical records and past outcome data. However, the amount of patient and past data may be limited, and the available data may not be correlated or relevant to the specific patient being treated. Conventional techniques in the field of orthopedics may lack the ability to utilize large data sets to generate and optimize patient-specific treatments (e.g., surgical interventions and / or implant designs) to achieve beneficial treatment outcomes. However, no method currently exists for actively monitoring and assessing whether a patient-specific treatment is progressing as planned during the treatment. Summary of the Invention

[0004] The accompanying drawings illustrate various embodiments of systems, methods, and various other aspects of the present disclosure. Any person skilled in the art will understand that the boundaries of elements shown in the figures (e.g., boxes, groups of boxes, or other shapes) represent one example of the boundaries. In some examples, one element may be designed as multiple elements, or multiple elements may be designed as one element. An element shown as an internal component of one element in some examples may be implemented as an external component in another example, and vice versa. Furthermore, elements may not be drawn to scale. A non-limiting and non-exhaustive description is set forth with reference to the following drawings. The components in the figures are not necessarily to scale, with emphasis instead being placed on 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, according to an embodiment. [Figure 2] 2 illustrates a computing device suitable for use in connection with the system of FIG. 1, according to an embodiment. [Figure 3] FIG. 1 is a flow diagram illustrating a method for providing patient-specific medical care, according to an embodiment. [Figure 4A] 4A-4D illustrate exemplary data sets that may be used and / or generated in connection with the methods described herein, according to embodiments. [Figure 4B] 4A-4C illustrate exemplary data sets that may be used and / or generated in connection with the methods described herein, according to embodiments. [Figure 4C] 4A-4C show exemplary datasets that may be used and / or generated in connection with the methods described herein, according to embodiments. FIG. 4C shows similarity scores and result scores for the reference patient dataset of FIG. 4B. [Figure 5]FIG. 10 is a flow diagram illustrating another method for providing patient-specific medical care, according to an embodiment. [Figure 6A] FIG. 1 is a flow diagram illustrating a method for providing confirmation of intra-operative positioning of a surgical implant, according to an embodiment. [Figure 6B] FIG. 1 is a flow diagram illustrating a method for providing confirmation of intra-operative positioning of a surgical implant, according to an embodiment. [Figure 7A] FIG. 1 illustrates an exemplary patient dataset that may be used and / or generated in connection with the methods described herein, according to an embodiment. [Figure 7B] FIG. 1 illustrates an exemplary patient dataset that may be used and / or generated in connection with the methods described herein, according to an embodiment. [Figure 7C] FIG. 1 illustrates an exemplary patient dataset that may be used and / or generated in connection with the methods described herein, according to an embodiment. [Figure 7D] FIG. 1 illustrates an exemplary patient dataset that may be used and / or generated in connection with the methods described herein, according to an embodiment. [Figure 8A] FIG. 1 illustrates an exemplary virtual model of a patient's spine that may be used and / or generated in connection with the methods described herein, according to an embodiment. [Figure 8B] FIG. 1 illustrates an exemplary virtual model of a patient's spine that may be used and / or generated in connection with the methods described herein, according to an embodiment. [Figure 9A-1.9A-2] 9A-1 and 9A-2 show exemplary virtual models of a patient's spine in pre-operative and modified anatomical configurations. More specifically, FIGS. 9A-1 and 9A-2 show the patient's pre-operative anatomical configuration. [Figure 9B-1.9B-2] 9B-1 and 9B-2 show exemplary virtual models of a patient's spine in a pre-operative anatomical configuration and a modified anatomical configuration. [Figure 10A]1A-1C illustrate exemplary interactive surgical planning for a patient-specific surgical procedure, according to an embodiment. [Figure 10B] 1A-1C illustrate pre-operative, planning, intra-operative, and post-operative images to enable assessment of achievement of surgical goals, according to an embodiment. [Figure 10C] 1A-1C illustrate pre-operative, planning, intra-operative, and post-operative images to enable assessment of achievement of surgical goals, according to an embodiment. [Figure 10D] 10A-10C illustrate images overlaid to align pre-operative, planning, intra-operative, and post-operative images to enable assessment of achievement of surgical goals, according to an embodiment. [Figure 10E] 1A-1C illustrate intraoperative images and a surgical model displayed on a user interface, according to an embodiment. [Figure 10F] 10A-10C illustrate images of an implant and an inserter device displayed on a user interface, according to an embodiment. [Figure 11] FIG. 10 illustrates an exemplary surgical planning report detailing a surgical plan that may be used and / or generated in connection with the methods described herein for surgeon review, according to an embodiment. [Figures 12A-12B] FIG. 1 illustrates an exemplary patient-specific implant that may be used and / or generated in connection with the methods described herein, according to an embodiment. [Figure 13] FIG. 1 illustrates a patient's spinal segments after multiple patient-specific implants have been implanted. DETAILED DESCRIPTION OF THE INVENTION

[0006] The present technology is directed to systems and methods for monitoring a surgical procedure based on a pre-operatively generated surgical plan. The technology can provide confirmation of intra-operative positioning of surgical instruments, surgical implants, and / or anatomical elements (e.g., intra-operative pathology, anatomical configuration, etc.) based on the pre-operative plan, user input, or other data sources. The technology can display a patient-specific interactive surgical plan generated by a surgical planning platform via an on-device display. The patient-specific interactive surgical plan can include user input elements for modifying and / or approving the interactive surgical plan, entering surgical data (e.g., physician's notes, observations, etc.), etc. In some cases, the interactive surgical plan includes a displayable planned intra-operative pathology of the patient. The system can overlay intra-operative images on pre-operatively planned images to confirm the positioning of patient-specific implants as placed and positioned according to the surgical plan. The system can confirm implant placement during the surgical procedure using, for example, images (e.g., real-time images, radiological images, fluoroscopy, etc.), direct visualization, and / or other data. The intraoperative images can display, for example, the position of the implant relative to anatomical elements. The planned images can be generated based on one or more patient images, a virtual model of the patient's anatomy, images from the surgical plan, etc. The intraoperative images of the actual anatomy can be synchronized or keyed to the planned images to determine whether surgical instruments, implants, or other image features are in the planned position. In some embodiments, the system can provide a positioning score (e.g., a position score for the current position of an implant or group of implants) to provide a likelihood of achieving a target outcome. The user can reposition the implants multiple times until they achieve an appropriate score. The system provides real-time feedback (e.g., predicted post-operative results) based on real-time image data to ensure proper positioning. Each time the implant is moved, the system can generate a new simulation and output feedback.Prediction can be used to confirm that the implant position will produce the desired results.

[0007] A patient-specific implant may be designed to be placed in a single, specific location on the patient. In some cases, during a surgical procedure to install a patient-specific implant, it is difficult to assess whether the implant reached its intended / planned location. The system can capture intraoperative images using one or more cameras or imaging devices (e.g., MRI, X-ray, CT scan, direct visualization, optical visualization, machine visualization, etc.). The system can virtually overlay images (e.g., overlaying an intraoperative image on a planned or target image (or vice versa)) to determine whether the implant is positioned according to the preoperative surgical plan. The planned or target image may be generated from a virtual model representing the patient's anatomy. The virtual model can be, for example, a three-dimensional model with anatomical features at the target or planned intraoperative location. In some embodiments, the system can overlay images displaying the planned and actual intraoperative positions of instruments. A user can view a comparison to reposition instruments to facilitate implant insertion and / or delivery. If an instrument, implant, or other surgical device is mispositioned, the system can notify the user whether the mispositioning may affect the patient outcome. The system can run simulations to generate predicted consequences of mispositioning (e.g., joint biomechanics, anatomical configuration, pathology, pain outcomes, etc.). If the predicted consequences of mispositioning are acceptable, the user may leave the instrument, implant, or other device in these new positions. This allows the user to intraoperatively evaluate and modify the surgical procedure to achieve the desired outcome.

[0008] Because implants are designed to reside and fit in one specific location, confirming or assisting in optimal implant positioning is helpful for personalized implant solutions. If the implant is not positioned in the specific location, it can result in a suboptimal fit, undesirable outcomes, and / or impaired function for the patient. In some embodiments, the planned or target image can be superimposed on continuous imaging (e.g., fluoroscopic imaging) to provide continuous real-time guidance. The operator can reposition the fluoroscopic imaging equipment to facilitate alignment of the planned or target image and the fluoroscopic imaging. In some embodiments, the planned or target image can include visual indicators (e.g., annotations, boxes, implant templates, instrument templates, etc.) to facilitate alignment and / or positioning. The system can scale and manipulate the planned or target image to achieve the best fit with the fluoroscopic or other type of imaging.

[0009] The system and method can update the treatment plan. In some embodiments, the image data can include a depiction of the native anatomical configuration of the anatomical element. The method can then include identifying one or more auxiliary, alternative, additional, and / or unusual steps and / or procedures (collectively referred to as "additional steps" or "auxiliary steps") to adjust the intraoperative mobility of the anatomical element (e.g., vertebrae of the patient's spine in spine surgery, articular elements in a joint repair procedure, etc.) to achieve the modified anatomical configuration. The additional steps can include surgically altering the implant treatment site, manipulating soft tissue or anatomical elements, etc. In some embodiments, the additional steps can be displayed to the user for modification and / or approval. In some embodiments, the method can compare the pre-operative planned anatomical configuration with intraoperative image data collected during the surgical procedure. This allows the user to visually identify differences between the planned and actual positions. The additional steps can be designed to limit, minimize, or eliminate one or more of those differences. For example, additional steps can include steps generated intraoperatively based on intraoperative data to facilitate accurate positioning of an implant at a target site. In spine-related surgical procedures, additional steps can include tissue manipulation. For example, soft tissues surrounding the patient's spine (e.g., ligaments, muscles, nerves, discs, etc.), vertebrae (e.g., vertebrae outside the target vertebra), and other anatomical features can be manipulated to widen an access path to an implant treatment site, adjust the size of the implant treatment site, or otherwise position anatomical elements to facilitate the implant treatment process.Examples of additional steps may include cutting ligaments along the subject's spine, removing at least a portion of an annulus of an intervertebral disc, resecting cartilage along the spine, performing additional decompression procedures, osteotomies, and / or facetectomies, disrupting unintentional (or undesired) bone fusion, and / or addressing malformations and / or irregularities in the bone (e.g., addressing fibrous dysplasia). The method can then include generating a surgical plan and / or series of surgical steps, including at least one of the additional surgical steps, before and / or during surgery.

[0010] The system can compare the planned position with the actual / intraoperative position. The position can be of an instrument, an anatomical element, a tissue, an implant, or any other position disclosed herein. Additional or alternative surgical steps can be generated based on the comparison, for example, using a machine learning platform. In some embodiments, the comparison can be displayed to the user for visual review of the planned position against the actual position. The method can generate one or more alerts if the planned position deviates from the actual position, for example, by a threshold value. The threshold value can be based on predicted adverse outcomes, user input, etc. In some embodiments, the user can pre-operatively identify the envelope or boundary of the implant treatment site. The system can determine whether the implant is positioned within the envelope or boundary. The system can also predict the post-operative position of the implant after the patient recovers from surgery. For example, the system can predict the position of the implant under various loading conditions as the user performs a task. Based on these predictions, the system can determine whether the implant will remain within the boundary. If the system predicts that the implant will be positioned outside the boundary, the system can modify the surgical plan to position the implant at a different site to achieve the desired outcome post-operatively. In some embodiments, the threshold may be the percentage of implants located within the boundary (e.g., by volume), a predicted mobility score, a predicted quality of life outcome, or a combination thereof (e.g., a composite score threshold).

[0011] The system can provide information regarding unplanned positions of the implant. The system can identify that the current position of the implant differs from the planned position. The system can analyze the current position and provide analysis results to the user. The analysis results can include, but are not limited to, the patient's altered anatomical configuration (e.g., the configuration or curvature of the patient's spine and spinal surgery, joint function and joint repair procedures, bone configuration and bone repair procedures, etc.), predicted outcome scores, disease progression predictions, etc. In some embodiments, the system can compare and display the results of the planned procedure with the results of the implant in its current position, allowing the user to evaluate whether an implant positioned in an unplanned position is acceptable. For example, the surgical plan may specify a specific implant treatment site for the implant. During the surgical procedure, the user may experience difficulty or be unable to properly deliver the implant to the planned implant treatment site. The user may decide to implant the implant in an alternative position. The system can provide real-time analysis results based on intraoperative data to determine whether the alternative position is acceptable. In some embodiments, the system can generate new simulations and virtual models based on the intraoperative data. The results of the intraoperative analysis can be used to determine whether the current position of the implant is acceptable.

[0012] In some procedures, anatomical features may be altered in an unplanned manner, for example, to access one or more surgical sites, to provide an adequate surgical pathway for delivering an implant to a surgical site, to address an unplanned adverse event (e.g., unplanned damage to tissue, organs, etc.), etc. The system can determine whether to alter the surgical plan based on the alterations. In response to a decision to alter the procedure, the system can receive intraoperative data describing the altered anatomical features and then generate a new or altered surgical plan during surgery.

[0013] Embodiments of the present disclosure will now be described more fully with reference to the accompanying drawings, in which like numerals represent like elements throughout the several views and in which example embodiments are shown. However, the claimed embodiments may be embodied in various forms and should not be construed as limited to the embodiments shown herein. The examples shown herein are, among other possible examples, non-limiting examples and are merely examples.

[0014] The words "comprising," "having," "including," and "including," as well as other forms of these words, are intended to be equivalent in meaning and are intended to be open-ended in that the reference to one or more items following any one of these words is not intended to be an exhaustive list of such one or more items, nor is it intended to be limited to only the listed one or more items. 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.

[0015] While the disclosure herein primarily describes systems and methods for treatment planning in the orthopedic context, the technology may be applied to treatments and devices in other fields (e.g., other types of surgical practices) as well. Additionally, while many embodiments herein describe systems and methods related to implanted devices, the technology may be applied to other types of medical devices (e.g., non-implanted devices) as well.

[0016] FIG. 1 is a network connectivity diagram illustrating a computing system 100 for patient-specific medical care, according to an embodiment. As described in further detail herein, the system 100 is configured to generate a treatment plan based on patient data, patient-specific implants, radiological images, etc. The system 100 includes a client computing device 102, which can be a user device such as a smartphone, mobile device, laptop, desktop, personal computer, tablet, phablet, or other such device as known in the art. As described further herein, the client computing device 102 can include one or more processors and memory storing instructions executable by the one or more processors to perform the methods described herein. The client computing device 102 can be associated with a healthcare provider treating a patient. While FIG. 1 depicts a single client computing device 102, in alternative embodiments, the client computing device 102 can instead be implemented as a client computing system encompassing multiple computing devices, such that the operations described herein with respect to the client computing device 102 can instead be performed by a computing system and / or multiple computing devices.

[0017] The client computing device 102 is configured to receive a patient dataset 108 associated with a patient to be treated. The patient dataset 108 may include data representing the patient's condition, anatomy, medical condition, 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., doctor's 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 medical condition, occupation, activity level, organizational information, health assessment, 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, computerized aided 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 a patient identification number (ID), age, sex, body mass index (BMI), lumbar lordosis, Cobb angle, pelvic intrinsic angle, disc height, segmental flexibility, bone quality, rotational displacement, and / or spinal treatment level.

[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 network and / or a wireless network. If the communications network 104 is wireless, it may be implemented using communications technologies such as Visible Light Communication (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] Server 106, sometimes referred to as a "therapy support network" or a "normative analytics network," may include one or more computing devices and / or systems. As further described herein, server 106 may include one or more processors and memory storing instructions executable by the one or more processors to perform the methods described herein. In some embodiments, server 106 is implemented as a distributed "cloud" computing system or functionality 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 the various methods described herein to provide 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, although 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.

[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 design, data collected from testing or research groups, data from practice databases, data from academic institutions, data from implant manufacturers or other medical device manufacturers, data from imaging studies, data from simulations, clinical trials, demographic data, treatment data, outcome data, mortality, etc.

[0022] In some embodiments, the database 110 includes multiple reference patient datasets, each associated with a corresponding reference patient. For example, the reference patient can be a patient who has previously undergone treatment or is currently undergoing treatment. Each reference patient dataset can include data representing the corresponding reference patient's condition, anatomy, medical condition, 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 the 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 treatment procedure performed on the reference patient, such as a description of a surgical procedure or intervention (e.g., a surgical technique, bone resection, surgical operation, corrective operation, 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 outcome data describing the results of the reference patient's treatment, such as corrected anatomical landmarks, presence of fusion surgery, HRQL, activity level, return to work, complications, recovery time, efficacy, mortality, and / or reoperation.

[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 may be connected to the healthcare provider computing systems 112 via one or more communication networks (not shown). Each healthcare provider computing system 112 may be associated with a corresponding healthcare provider (e.g., doctor, surgeon, clinic, hospital, healthcare network, etc.). Each healthcare provider computing system 112 may 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 may include, for example, electronic medical records, electronic health records, biomedical datasets, biomechanical datasets, mobility datasets, pain datasets, intra-operative image data, payment information, insurance information, insurer information, etc. The reference patient datasets 114 may be received by the server 106 from the healthcare provider computing systems 112 and may be reformatted into different formats for storage in the database 110. Optionally, the reference patient dataset 114 may be processed (e.g., purified) to ensure that the patient parameters represented are likely to be useful for the treatment planning methods described herein.

[0024] The server 106 can receive at least some information from an intraoperative imaging system 141 (e.g., a device that captures radiological images, fluoroscopic images, C-arm device images, X-ray images, etc.). In some embodiments, radiological images are captured using an X-ray machine, a C-arm machine, a fluoroscopic imaging device, etc. For example, the server 106 can be connected to the system 141 via one or more communication networks (not shown). The system 141 can include one or more results data databases, an image database, a pre-operative database, an intra-operative database, a post-operative database, etc. The server 106 can request and retrieve data sets 117 from the system 141. The system 141 can include, but is not limited to, an X-ray machine, a fluoroscopic imaging device, a CT scanner, an MRI machine, or other imaging equipment that can be located near or within the operating room.

[0025] As described in further detail herein, the server 106 may be configured with one or more algorithms to generate patient-specific treatment planning data (e.g., therapeutic procedures, 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 may predict outcomes, including recovery time, efficacy based on clinical endpoints, likelihood of success, predicted mortality, predicted associated reoperations, etc. In some embodiments, the server 106 may continuously or periodically analyze patient data (including patient data obtained during the patient's stay) to determine near-real-time or real-time risk scores, mortality predictions, etc.

[0026] In some embodiments, 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, server 106 includes a data analysis module 116 and a surgical planning and confirmation platform 109 (“SPC (surgical planning and confirmation) platform 109”). SPC platform 109 includes a treatment planning module 118, a surgical implant positioning manager 119, and a database 151. 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 a particular module or modules, this is not intended to be limiting, and in alternative embodiments, such operations may be performed by a different module or modules. For example, SPC platform 109 may be incorporated into data analysis module 116. In other embodiments, modules of system 100 may be combined with modules of other systems. For example, the SPC platform 109 can be part of or integrated into the medical system 133 and can manage adjustments to intra-operative implant positioning relative to the surgical plan. The adjustments can be outcome-driven adjustments to mitigate or eliminate incorrect intra-operative implant positioning that is likely to impact one or more outcomes by more than an acceptable threshold amount.

[0027] The data analysis module 116 is configured using one or more algorithms to identify 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). This comparison can be based on one or more parameters, such as age, gender, BMI, lumbar lordosis, pelvic intrinsic angle, and / or treatment level. These 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 higher, lower, or at a specified threshold. For example, as described in more detail below, this comparison can be performed by assigning a value to each parameter and determining the sum of the differences between the patient of interest and each reference patient. Reference patients with a sum of differences below a threshold may be considered similar patients.

[0028] The data analysis module 116 may be further configured using one or more algorithms to select a subset of the reference patient dataset based on, for example, similarity to the patient dataset 108 and / or treatment outcomes of 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 beneficial or desirable treatment outcome. The outcome data may include data representing one or more outcome parameters, such as corrected anatomical landmarks, presence of fusion, HRQL, activity level, complications, recovery time, effectiveness, mortality, or reoperation. As described in more detail below, in some embodiments, the data analysis module 116 calculates an outcome score by assigning a value to each outcome parameter. If the outcome score is higher, lower, or at or within a specified threshold, the patient may be considered to have a beneficial outcome.

[0029] 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 healthcare provider). For example, the user input may be used to identify similar patient datasets. In some embodiments, weightings of the similarity and / or outcome parameters may be selected by the healthcare provider or physician to adjust the similarity and / or outcome score based on the clinician's input. In further embodiments, the healthcare provider or physician may select the set of similarity and / or outcome parameters (or define new similarity and / or outcome parameters) used to generate the similarity and / or outcome score, respectively.

[0030] 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 may be used to select a subset based on provider parameters (e.g., based on provider rankings / scores, such as hospital / physician expertise, number of procedures performed, hospital rankings, etc.) and / or medical resource parameters (e.g., diagnostic equipment, facilities, surgical equipment, such as surgical robots), or other non-patient-related information that may be used to predict outcomes and risk profiles for the current provider's procedures. For example, reference patient datasets including images captured from similar diagnostic equipment may be aggregated to reduce or limit irregularities due to variations between diagnostic equipment. Furthermore, patient-specific treatment plans may be developed for a particular provider using data from similar providers (e.g., providers who traditionally have similar outcomes, physician expertise, surgical teams, etc.). In some embodiments, reference provider datasets, hospital datasets, physician datasets, surgical team datasets, post-treatment datasets, and other datasets may be utilized. As an example, a patient-specific treatment 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 treatment plan can be generated based on available robotic surgery systems. The reference patient dataset can be selected based on patients operated on using comparable robotic surgery systems according to similar conditions (e.g., surgical team size and capabilities, hospital resources, etc.).

[0031] The SPC platform 109 may include a treatment planning module 118, a surgical implant positioning manager 119, and a database 151. The treatment planning module 118 is configured using one or more algorithms to generate at least one treatment plan (e.g., a pre-operative plan, an intra-operative plan, a surgical plan, a post-operative plan, etc.) based on output from the data analysis module 116. In some embodiments, the treatment planning module 118 is configured to develop and / or implement at least one predictive model to generate the plan. The predictive model may be developed 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 correlations between datasets, patient parameters, healthcare provider parameters, healthcare resource parameters, treatment procedures, medical device designs, and / or treatment outcomes. These correlations may be used to develop at least one predictive model that predicts the likelihood that a treatment plan will cause a beneficial outcome for a particular patient. A predictive model may be validated, for example, by inputting data into the model and comparing the output of the model to an expected output. A machine learning model may be trained to analyze pre-operative planning and intra-operative data to determine whether the position (e.g., location, orientation, etc.) of an anatomical element, instrument, or implant in a patient during a surgical procedure matches the position in the pre-operative plan.

[0032] In orthopedic procedures, the machine learning model can be trained to determine whether anatomical elements, such as bones and / or joints, are in a target position. The instrument can be a surgical instrument for accessing a surgical site, implanting an implant, fixation (e.g., fixing an implant to bone tissue), etc. In joint repair procedures, the anatomical elements can include bone, cartilage, connective tissue, and other anatomical elements that affect the position and / or function of the joint. The instrument can be a joint repair instrument. In spinal surgery, the position of the anatomical elements can include soft tissue that may contribute to nerve compression. The system can identify tissue that can be removed, for example, to relieve nerve compression, facilitate implant healing, and / or perform other steps for decompression. The machine learning model can be trained based on the procedure being performed.

[0033] The system 100 can predict intraoperative patient mobility and identify surgical steps related to mobility. The system 100 can implement the techniques and methods disclosed in U.S. Patent Application No. 17 / 868,729, which is incorporated by reference in its entirety. For example, the SPC platform 109 can identify soft tissue surgical steps to adjust the intraoperative mobility of anatomical features to facilitate implant placement at a target location. One or more predictive models can identify specific soft tissues (e.g., cartilage, ligaments, etc.) that can be cut, removed, or manipulated to achieve desired intraoperative mobility of bones, organs, or other anatomical elements, for example. The altered intraoperative capabilities can facilitate implant delivery and positioning. In some embodiments, intraoperative mobility can be predicted prior to the start of surgery, the sequence of surgical steps, etc. In some embodiments, the system 100 can intraoperatively generate surgical steps based on intraoperative data, allowing real-time intraoperative steps to be generated based on the patient's current condition. In some surgeries, the surgical plan may include soft tissue surgical steps to facilitate anatomical element movement, implant placement, etc. Additionally, the methods and systems disclosed herein may be combined with or used in conjunction with the techniques or methods disclosed in U.S. Patent Application No. 17 / 978,746, which is incorporated by reference in its entirety. For example, one or more decompression steps may be performed during a surgical procedure. Sites of nerve compression may be identified pre-operatively and / or intra-operatively. Target tissues contributing to the nerve compression may be identified. The system 100 may develop one or more surgical steps (e.g., removal and / or repositioning of target tissue) to access the target tissue and perform one or more decompression steps on the target tissue, thereby enabling a spinal decompression procedure to be performed to improve outcomes.

[0034] The treatment planning module 118 may be configured to include one or more soft tissue surgical steps. The soft tissue surgical steps may promote movement of anatomical features and facilitate implant treatment. The soft tissue surgical steps may include cutting, dissecting, resecting, and / or removing tissue. For example, ligaments (e.g., supraspinous ligaments, interspinous ligaments, spinal ligaments, etc.) may be cut to access and separate adjacent spinous processes, vertebral bodies, etc. In some exemplary plans, the soft tissue surgical steps include one or more of cutting soft tissue located along the patient's spine, removing at least a portion of the annulus, and / or resecting cartilage along the vertebrae. The treatment planning module 118 may virtually move anatomical elements to identify soft tissue that inhibits or obstructs desired movement, soft tissue that blocks access to the implant treatment site, etc. A simulation of the soft tissue surgical steps may be performed to select recommended soft tissue surgical steps and achieve positioning of the anatomical elements.

[0035] In some exemplary plans, the soft tissue surgery step includes one or more decompression procedures. The system can predict a decompression score for each decompression procedure. The neural decompression score can be based, for example, on a predicted percentage reduction in pain experienced by the patient. The system can generate multiple decompression plans, determine a decompression score (e.g., post-operative pain score, neural decompression score, etc.) for each decompression plan, receive a selection of one of the decompression plans, and generate a decompression surgery plan based on the selected decompression plan. The user can modify the selected decompression plan based on a corrected configuration of the patient's spine. The decompression plan can include at least one of a laminectomy, a laminotomy, a microdiscectomy, a foraminotomy, and / or an osteophyte surgery.

[0036] In some exemplary plans, the planned surgical steps include one or more decompression steps for spinal surgery. The system can predict a decompression score for each decompression step, series of steps, and / or decompression procedure. The neural decompression score can be based, for example, on a predicted rate of reduction in pain experienced by the patient. The system can generate multiple decompression plans, determine a decompression score (e.g., post-operative pain score, neural decompression score, etc.) for each decompression plan, receive a selection of one of the decompression plans, and generate a decompression surgical plan based on the selected decompression plan. The user can modify the selected decompression plan based on a corrected configuration of the patient's spine. The decompression plan can include at least one of a laminectomy, a laminotomy, a microdiscectomy, a foraminotomy, and / or an osteophyte surgery.

[0037] To facilitate surgical planning and simulation, implant movement, anatomical elements, and other target characteristics resulting from each step can be predicted. The simulation can predict joint mobility of the patient's spine or a specific joint. The user can select one or more of the implant location (e.g., preoperatively planned location, intraoperatively planned location, predicted postoperative location based on one or more loading conditions), identified surgical steps based on simulated joint mobility, target anatomical configurations for correction, etc. The treatment planning module 118 can predict intraoperative and / or postoperative joint mobility associated with the selected soft tissue surgical step. This allows the user to select a surgical plan that includes surgical steps to assist with anatomical element repositioning, implant treatment at the target site, etc.

[0038] In some embodiments, the treatment planning module 118 is configured to generate a treatment 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 data analysis module 116 and 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 beneficial 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 treating the patient. For example, the treatment procedures and / or medical device designs can be assigned values ​​and aggregated to generate a treatment score. A patient-specific treatment plan can be determined by selecting a treatment plan based on this score (e.g., a higher or highest score, a lower or lowest score, a score above, below, or at a specified threshold). The personalized patient-specific treatment plan can be based at least in part on a patient-specific technique or a patient-specific selected technique.

[0039] Alternatively, or in combination, the treatment planning module 118 can generate a treatment 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 beneficial outcomes (e.g., as identified by the data analysis module 116). The correlation analysis can include converting correlation coefficient values ​​into values ​​or scores. These values / scores can be aggregated, filtered, or otherwise analyzed to determine one or more statistical significances. These correlations can be used to determine therapeutic procedures and / or medical device designs that are optimal for treating the patient or that are likely to cause beneficial outcomes.

[0040] Alternatively, or in combination, the treatment planning module 118 can generate the treatment 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 can 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.

[0041] In some embodiments, the treatment planning module 118 generates the treatment 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 weights of the connections 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).

[0042] In some embodiments, a machine learning model (e.g., a neural network or naive Bayes classifier) ​​may be trained on a training dataset using supervised learning methods (e.g., gradient descent or stochastic gradient descent). The training dataset may include pairs of generated "input vectors" with associated corresponding "answer vectors" (commonly denoted 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 being used, the parameters of the model are adjusted. Model fitting may include both variable selection and parameter estimation. The fitted model may be used to predict responses to observations in a second dataset, called a validation dataset. The validation dataset may provide an unbiased assessment of the model's fit to the training dataset while adjusting the model parameters. The validation dataset may be used for regularization by early stopping, for example, by stopping training early when the error on the validation dataset increases, as this may be a sign of overfitting to the training dataset. In some embodiments, the error in the validation data set is allowed to vary during training so that ad hoc rules can be used to determine when overfitting has truly set in. Finally, a test data set can be used to provide an unbiased assessment of the fit of the final model to the training data set.

[0043] To generate a treatment plan, the patient dataset 108 may be input into a trained machine learning model. Additional data, such as a selected subset of a reference patient dataset and / or similar patient datasets, and / or treatment data from the selected subset, may 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 cause beneficial outcomes for the patient and satisfy one or more parameters (e.g., coverage parameters, reimbursement parameters, regulatory parameters, etc.). Based on these calculations, the trained machine learning model can select at least one treatment plan for the patient. In embodiments in which multiple trained machine learning models are used, the models can be run sequentially or simultaneously to compare results and may be periodically updated using the training dataset. The treatment planning module 118 can use one or more of the machine learning models based on the models' predicted accuracy scores.

[0044] The patient-specific treatment plan generated by the treatment planning module 118 may include at least one patient-specific therapeutic 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 treatment plan may include an entire surgical procedure or a portion thereof. Furthermore, one or more patient-specific medical devices may be selected or designed specifically for the corresponding surgical procedure, thus allowing various components of the patient-specific technology to be used in combination to treat the patient.

[0045] In some embodiments, the patient-specific therapeutic procedure comprises an orthopedic procedure, such as spine surgery, hip surgery, knee surgery, jaw surgery, hand surgery, shoulder surgery, elbow surgery, total joint reconstruction (arthroplasty), skull reconstruction, foot surgery, or ankle surgery. The spinal surgery can include spinal fusion procedures, such as posterior lumbar interbody fusion (PLIF), cervical fusion, anterior lumbar interbody fusion (ALIF), transverse or transforaminal lumbar interbody fusion (TLIF), lateral lumbar interbody fusion (LLIF), direct lateral lumbar interbody fusion (DLIF), or extreme lateral lumbar interbody fusion (XLIF). In some embodiments, the patient-specific treatment procedure includes a description of and / or instructions for performing one or more aspects of a patient-specific surgical procedure. For example, the patient-specific surgical procedure can include one or more of a surgical technique, a revision operation, a bone resection, or an implant placement.

[0046] 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, facet screws), interbody implant devices (e.g., interbody implants), cages, plates, rods, intervertebral discs, fusion devices, spacers, bars, 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.

[0047] 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 the corresponding medical device. For example, a design for an orthopedic implant may include the shape, size, material, and / or effective stiffness (e.g., lattice density, number of struts, location of struts, etc.) of the implant. In some embodiments, the generated patient-specific medical device design is a design for the entire device. Alternatively, the generated design can be a design for one or more components of the device rather than the entire device.

[0048] In some embodiments, the design is for one or more patient-specific device components that can be used with standard, commercially available components. For example, in spinal surgery, a pedicle screw kit can include both standard and patient-specific customized components. In some embodiments, the generated design is for a patient-specific medical device that can be used with standard, commercially available delivery instruments. For example, implants (e.g., screws, screw holders, rods) can be designed and manufactured for the patient, while the instruments for delivering the implants can be standard instruments. This approach allows implanted components to be designed and manufactured based on the patient's anatomy and / or surgeon's preferences to improve 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.

[0049] In embodiments in which the patient-specific treatment plan includes a 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, pre-operative planning aspects (e.g., initial implant configuration, detection, and measurements of the patient's anatomy), 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 can be tagged with a specific identifier for formulas 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, shape, and / or mechanical properties of the anatomy. The anatomy information may be used to inform the design and / or placement of the implant.

[0050] The treatment plan generated by the treatment planning module 118 may 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 may include a graphical user interface (GUI) for visually presenting various aspects of the treatment plan. For example, the display 122 may display various aspects of a surgical procedure to be performed on a patient, such as the surgical technique, treatment levels, corrective operations, tissue resection, and / or implant placement. To facilitate visualization, a virtual model of the surgical procedure may be displayed. As another example, the display 122 may display the design of a medical device to be implanted in the patient, such as a two-dimensional or three-dimensional model of the device design. The display 122 may also display patient information, such as two-dimensional or three-dimensional images or models of the patient's anatomy on which the surgical procedure will be performed and / or the patient's anatomy on which the device will be implanted. The client computing device 102 may further include one or more user input devices (not shown) that allow the user to modify, select, accept, and / or reject the displayed treatment plan.

[0051] The surgical implant positioning manager 119 can analyze and manage confirmation of intra-operative positioning data, intra-operative data (e.g., radiological images, ultrasound, MRI, etc.), and other information. The database 151 can search, retrieve, and store data from the system 141 or other systems. For example, the server 106 can be trained to generate a new treatment plan, and the database 151 can provide adjustments to the intra-operative implant positioning relative to the surgical plan. The database 151 can then retrieve intra-operative, pre-operative, and post-operative data sets from the system 141. The surgical implant positioning manager 119 can analyze and provide confirmation of the intra-operative positioning of the surgical implant based on the pre-operative plan. The surgical implant positioning manager 119 can compensate for loading conditions of anatomical elements relative to the pre-operative data sets. For example, the surgical implant positioning manager 119 can modify the pre-operative dataset (or a virtual model generated based on the pre-operative dataset) to compensate for differences in loading conditions between the pre-operative dataset (e.g., the patient was standing to obtain the pre-operative upright X-ray data) and the intra-operative dataset having other loading conditions (e.g., the patient was lying down).

[0052] In some embodiments, the medical device design generated by the server 106 may be transmitted from the client computing device 102 and / or the server 106 to a manufacturing system 124 to manufacture the corresponding medical device. The manufacturing system 124 may be located on-site or off-site. On-site manufacturing may reduce the number of sessions with the patient and / or the time available to perform the surgery, while off-site manufacturing may be useful for creating complex devices. Off-site manufacturing facilities may have specialized manufacturing equipment. In some embodiments, more complex device components can be manufactured off-site, while simpler device components may be manufactured on-site.

[0053] Various types of manufacturing systems are suitable for use in accordance with embodiments herein. Manufacturing can be achieved using human design, machine design, a combination of human design and machine design, or other design techniques. 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), laminated object 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 may 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. Manufacturing system 124 may 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 the various manufacturing techniques described herein). Various components of system 100 may generate at least a portion of the manufacturing data used by manufacturing system 124.The manufacturing data may 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., print head 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 may be finalized by modifying the shape, surface, etc., and then generating manufacturing instructions. In some embodiments, the server 106 generates at least a portion of the manufacturing data that is sent to the manufacturing system 124.

[0054] The manufacturing system 124 can generate CAM data, printing data (e.g., powder bed printing data, thermoplastic printing data, photoresist data, etc.), etc., and can include additive manufacturing equipment, subtractive manufacturing equipment, heat processing equipment, etc. The additive manufacturing equipment can be a three-dimensional printer, a stereolithography device, a digital light processing device, a fused deposition modeling device, a selective laser sintering device, a selective laser melting device, an electron beam melting device, a laminated object manufacturing device, a powder bed printer, a thermoplastic printer, a direct material deposition device, or an inkjet photoresist printer, or similar technology. The subtractive manufacturing equipment can be a CNC machine, an electrical discharge machine, a grinder, a laser cutter, a waterjet machine, a manual machine (e.g., a milling machine, a lathe, etc.), or similar technology. Both additive and subtractive technologies can be used to manufacture implants having complex shapes, surface finishes, material properties, etc. The generated manufacturing 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, patient-specific medical devices can include shared features, materials, and designs throughout the design to simplify manufacturing. For example, deployable patient-specific medical devices for different patients can have similar internal deployment mechanisms but different deployed configurations. In some embodiments, components of the patient-specific medical device are selected from a set of available off-the-shelf components, and the selected off-the-shelf components can be modified based on manufacturing instructions or data.

[0055] The manufacturing system 124, the implant analyzer 129, and / or the surgical implant positioning manager 119 can communicate with each other directly or via the communication network 104. The system 100 can perform one or more validation steps on the manufactured implant. The analyzer 129 can include one or more scanners, cameras, or imaging devices and can be integrated into the manufacturing system 124 or other components of the system 100 to scan the manufactured implant, for example, to identify manufacturing defects, verify that the implant meets one or more regulatory requirements, etc. By analyzing the implant's characteristics (e.g., material composition, surface topology, etc.) and manufacturing parameters (e.g., material composition, temperature, printing speed, manufacturing conditions, printer accuracy, etc.), the system 100 can determine whether the implant should be implanted in the patient. If the implant is not acceptable, the system 100 can determine manufacturing adjustments for the implant to be remanufactured. The analyzer 129 can be an on-site manufacturing scanner positioned to scan the implant during and / or after manufacturing. In some embodiments, the analyzer 129 is off-site at the manufacturing location. For example, analyzer 129 may be located at a healthcare provider (eg, hospital, clinic, operating room, etc.) to enable quality control checks immediately prior to implant treatment, verification of regulatory compliance, and the like.

[0056] The manufacturing system 124 can manufacture all or some of the kit components. The kit components can be selected based on requirements, including regulatory requirements, reimbursement requirements, or other requirements. The surgical kit can include one or more implants, instruments, instructions for use, and reusable and disposable components. The kit requirements can be retrieved from the database 151. The system 100 can synchronize the surgical plan with the requirements to generate a patient-specific surgical kit that meets the requirements.

[0057] The treatment plans described herein can be performed by a surgeon, a surgical robot, or a combination thereof, thus enabling treatment flexibility. In some embodiments, a surgical procedure may be performed entirely by a surgeon, entirely by a surgical robot, or a combination thereof. For example, one step of the 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 surgical system, a navigation system, etc.) to partially or completely perform the surgical procedure. The control instructions may be transmitted to the robotic device by the client computing device 102 and / or the server 106.

[0058] Following treatment of the patient according to the treatment plan, the progress of the treatment may be monitored over one or more time periods to update the data analysis module 116 and / or the treatment planning module 118. The post-treatment data may be added to the reference data stored in the database 110. The post-treatment data may be used to train machine learning models to develop patient-specific treatment plans, patient-specific medical devices, or a combination thereof.

[0059] It should be understood that the components of system 100 may be configured in various ways. For example, in an alternative embodiment, database 110, data analysis module 116, and / or treatment planning module 118 may be components of client computing device 102 rather than server 106. As another example, database 110, data analysis module 116, and / or treatment planning module 118 may 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.

[0060] The treatment planning module 118 can communicate with the surgical implant positioning manager 119 to obtain intra-operative data. The display 122 can display the intra-operative data 123 and the pre-operative data 127 virtually overlaid on one another to show the placement and position of the implant 161. A user can review the proposed pathology 129, the treatment plan 157, and the implant 161. The treatment plan 157 can be an interactive plan that includes user input elements 165 (e.g., one or more buttons, drop-down menus, toggles, etc.) for modification and / or approval. The intra-operative data 123 and the pre-operative data 127 can be dynamically updated based on user input. This allows the user to identify the intra-operative positioning of the surgical implant based on the pre-operative plan. The display 122 can graphically overlay the intra-operative image on the pre-operative plan / model / image. A user (e.g., a healthcare provider such as a surgeon) can manipulate (e.g., zoom, stretch, crop, and / or rotate) the intraoperative images to match a preoperative model (e.g., a virtual 3D model), image (e.g., an image of a virtual model), anatomical rendering, or other image displaying anatomical location information on the device. In some cases, a user can zoom, stretch, and / or rotate a virtual 3D model (or other preoperative image) to match it with an intraoperative image on the device or other display platform. In some embodiments, the treatment planning module 118 can analyze the preoperative data and then manipulate the preoperative data (e.g., preoperative images, virtual 3D model, etc.) to align or otherwise synchronize the preoperative and intraoperative data. For example, the treatment planning module 118 can generate images of a virtual 3D model of the patient's anatomy in a modified configuration so that the images match the intraoperative images. The treatment planning module 118 can use a machine learning engine to match anatomical features in the virtual 3D model with corresponding anatomical features in the image, for example, by manipulating the virtual 3D model, the image, or both.The 3D virtual model may include, for example, a representation of the patient's anatomy, implants, instruments, or other models disclosed herein.

[0061] The system 100 is configured to determine one or more measurements to confirm implant placement. For example, the system 100 calculates the difference (e.g., delta, deviation, etc.) between the intraoperative data and the preoperative plan. The display 122 can display the measurements to the user. In some implementations, the display 122 displays a live comparison between the intraoperative data and the preoperative plan during the surgical procedure. In some embodiments, a threshold difference can be determined by the system 100, entered by the user, etc. The system 100 can notify the user if the measurement exceeds the threshold difference. In some procedures, the threshold difference can be based on an envelope, boundary, or other target feature of the implant treatment determined by the system 100, the user, etc. For example, the user can draw two-dimensional or three-dimensional boundaries on an anatomical image for acceptable implant locations. The system 100 can then determine whether the implant, or a sufficient amount of implants, is positioned within the boundaries. The system 100 can calculate a completion score for the surgical procedure and display this score on the display 122. In one example, a device captures intraoperative images and displays the intraoperative images over the preoperative plan. The system 100 can scale and orient the intraoperative images to reflect the locations of anatomical landmarks and implants and closely match the preoperative plan. This matching can be performed using one or more segmentation programs, best fit algorithms, image manipulation programs, etc.

[0062] The system 100 can display, correlate, and / or measure the planned and current positions of implants to assist healthcare providers in properly implanting and positioning implants in patients. Additionally, the system 100 can compare post-operative imaging with pre-operative models, intra-operative images, and treatment plans in accordance with the techniques described herein. The system 100 can utilize the techniques described herein for multi-stage surgeries (e.g., anterior surgery performed first, posterior surgery performed next, lateral surgery performed next, etc.). The system 100 can confirm implant placement based on the surgical plan or monitor transitions between other aspects of patient care or subsequent surgery. The system 100 can predict post-operative outcomes, for example, based on monitoring local anatomical environmental conditions. Image analysis can be used to determine / predict post-operative mobility (e.g., anatomical configuration, mobility after surgical intervention, etc.) based at least in part on intra-operative data, disease progression scores, etc.

[0063] The system 100 is configured to design a physical, patient-specific implant 154 to achieve the approved, planned pathology 129. The surgical implant positioning manager 119 may also retrieve information about the patient's anatomy, such as pre-operative measurements, 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. Exemplary implant designs are described in connection with FIGS. 3-13.

[0064] Additionally, in some embodiments, system 100 may be operational with numerous 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 these systems or devices, etc.

[0065] 2 illustrates a computing device 200 suitable for use in connection with the system 100 of FIG. 1 , according to an 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 can be a single processing unit or multiple processing units within one device or 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 configured 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. These actions may be mediated by a hardware controller that interprets signals received from the input devices and communicates this 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 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 including voxels showing radiodensity units or Hounsfield units representing tissue density at a location). In some embodiments, display 230 provides graphical and textual visual feedback to the user. Processor 210 may communicate with display 230 via the device's hardware controller. In some embodiments, display 230 includes input device 220 as part of display 230, such as when input device 220 includes a touchscreen or is equipped with a gaze direction monitoring system. In alternative embodiments, display 230 is separate from input device 220. Examples of display devices include an LCD display screen, an LED display screen, a projected display, a holographic display, or an augmented reality display (e.g., a head-up display device or a head-mounted device).

[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 device, 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 devices, 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 machines from other sources, such as via a network, or from previously captured data stored, for example, in a database.

[0069] In some embodiments, computing device 200 also includes a communication device (not shown) that can communicate wirelessly or wire-based with network nodes. The communication device can communicate with another device or server over a network using, for example, the TCP / IP protocol. Computing device 200 can utilize the communication 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 within a single device or distributed across multiple devices. Memory 250 may include one or more of various hardware devices for volatile and non-volatile storage, including both read-only and writable memory. For example, memory may include random access memory (RAM), various caches, CPU registers, read-only memory (ROM), and writable, 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 propagating 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 assistance modules 264, and other application programs 266. The treatment assistance module 264 may include one or more modules configured to perform various methods described herein (e.g., the data analysis module 116 and / or the treatment planning module 118 described with respect to FIG. 1). The memory 250 may also include a data memory 270 that may include, for example, reference data, configuration data, settings, user options or preferences, etc., that may be provided in the program memory 260 or any other element of the computing device 200.

[0071] 3 is a flow diagram illustrating a method 300 for providing patient-specific medical care, according to an embodiment. The method 300 may include a data phase 310, a modeling phase 320, and an execution phase 330. The data phase 310 may include collecting data (e.g., medical condition data) of the patient to be treated and comparing the patient data with reference data (e.g., previous patient data, such as medical condition data, surgery data, and / or outcome data). For example, a patient dataset may be received (block 312). The patient dataset may be compared to multiple reference patient datasets (block 314), for example, to identify one or more similar patient datasets among the multiple reference patient datasets. Each of the multiple reference patient data sets can include data representing one or more of age, gender, BMI, lumbar lordosis, Cobb angle, pelvic intrinsic angle, disc height, coronal offset distance, segmental flexibility, LL-PI greater than a predetermined angle (e.g., 5 degrees, 10 degrees, etc.), LL-PI discrepancy (e.g., age-adjusted), sagittal vertical axis offset distance, coronal offset distance, coronal angle, bone quality, rotational displacement, or spinal treatment level.

[0072] For example, a subset of the multiple reference patient datasets may be selected based on similarity with the patient datasets and / or treatment outcomes of corresponding reference patients (block 316). For example, a similarity score may be generated for each reference patient dataset based on a comparison of the patient dataset and the reference patient dataset. The similarity score may represent a statistical correlation between the patient data and the reference patient dataset. One or more similar patient datasets may be identified based at least in part on the similarity score.

[0073] In some embodiments, each patient data set of the selected subset includes and / or is associated with data indicative of a beneficial treatment outcome (e.g., a beneficial treatment outcome based on a single target outcome, an aggregate outcome score, or outcome thresholding). This data may include, for example, data representing one or more of corrected anatomical landmarks, presence of fusion, health-related quality of life, activity level, or comorbidities. In some embodiments, the data is or includes an outcome score that may be calculated based on a single target outcome, an aggregate outcome, and / or an outcome threshold.

[0074] Optionally, the data analysis phase 310 can include identifying or determining, for at least one patient data set of the selected subset (e.g., for at least one similar patient data set), surgical procedure data and / or medical device design data associated with a beneficial treatment outcome. The surgical procedure data can include data representing one or more of a surgical technique, a corrective operation, a bone resection, or an implant placement. The at least one medical device design can include data representing one or more of a physical characteristic, a mechanical characteristic, or a biological characteristic of a corresponding medical device. In some embodiments, the at least one patient-specific medical device design includes a design for an implant or an implant delivery instrument.

[0075] In the modeling phase 320, surgical procedures and / or medical device designs are generated (block 322). The generating step may include developing at least one predictive model (e.g., using statistics, machine learning, neural networks, AI, etc.) based on a selected subset of the patient dataset and / or the reference patient dataset. The predictive model may be configured to generate a surgical procedure and / or medical device design.

[0076] In some embodiments, the predictive model includes one or more trained machine learning models that at least partially generate surgical procedures and / or medical device designs. For example, the trained machine learning model can determine multiple candidate surgical procedures and / or medical device designs for treating a patient. Each surgical procedure can be associated with a corresponding medical device design. In some embodiments, the surgical procedures and / or medical device designs are determined based on surgical procedure data and / or medical device design data associated with beneficial outcomes, as described above with respect to data analysis phase 310. For each surgical procedure and / or corresponding medical device design, the trained machine learning model can calculate a probability of achieving a target outcome (e.g., a beneficial or desired outcome) for the patient. The trained machine learning model can then select at least one surgical procedure and / or corresponding medical device design based at least in part on the calculated probability.

[0077] The execute phase 330 can include manufacturing the medical device design (block 332). In some embodiments, the medical device design is manufactured by a manufacturing system configured to perform one or more of additive manufacturing, three-dimensional printing, stereolithography, digital light processing, fused deposition modeling, selective laser sintering, selective laser melting, electron beam melting, laminated object manufacturing, powder bed printing, thermoplastic printing, direct material deposition, or inkjet photo-resin printing. The execute phase 330 can optionally include generating manufacturing instructions configured to cause the manufacturing system to manufacture a medical device having the medical device design.

[0078] The execution phase 330 can include performing the surgical procedure (block 334). The surgical procedure can include implanting a medical device having the medical device design into the patient. The surgical procedure can be performed manually, by a surgical robot, or a combination thereof. In embodiments in which the surgical procedure is performed by a surgical robot, the execution phase 330 can include generating control instructions configured to cause the surgical robot to at least partially perform the patient-specific surgical procedure.

[0079] Method 300 may be implemented and performed in various ways. In some embodiments, one or more steps of method 300 (e.g., data phase 310 and / or modeling phase 320) may be implemented as computer-readable instructions stored in memory and executable by one or more processors of any of the computing devices and systems described herein (e.g., system 100) or components thereof (e.g., client computing device 102 and / or server 106). Alternatively, one or more steps of method 300 (e.g., execution phase 330) may be performed by a healthcare provider (e.g., doctor, surgeon), a robotic device (e.g., surgical robot), a manufacturing system (e.g., manufacturing system 124), or a combination thereof. In some embodiments, one or more steps of method 300 (e.g., execution phase 330) are omitted.

[0080] 4A-4C illustrate exemplary data sets that may be used and / or generated in connection with the methods described herein (e.g., data analysis phase 310 described with respect to FIG. 3 ), according to embodiments. FIG. 4A illustrates a patient data set 400 for a patient to be treated. The patient data set 400 may include a patient ID and multiple pre-operative patient metrics (e.g., age, gender, BMI, lumbar lordosis (LL), pelvic incidence (PI), and spinal treatment level (level)). FIG. 4B illustrates multiple reference patient data sets 410. In the illustrated embodiment, the reference patient data set 410 includes a first subset 412 from an examination group (Exam Group X), a second subset 414 from a practice database (Practice Y), and a third subset 416 from an academic group (University Z). In alternative embodiments, the reference patient data set 410 may include data from other sources, as previously described herein. Each reference patient dataset can include a patient ID, multiple pre-operative patient metrics (e.g., age, sex, BMI, lumbar lordosis (LL), pelvic intrinsic angle (PI), and spinal treatment level (level)), treatment outcome data (outcome) (e.g., presence of fusion, HRQL, complications), and treatment procedure data (surgical intervention) (e.g., implant design, implant placement, surgical technique).

[0081] FIG. 4C illustrates a comparison of patient dataset 400 with reference patient dataset 410. As previously described, patient dataset 400 may be compared to reference patient dataset 410 to identify one or more similar patient datasets from the reference patient dataset. In some embodiments, patient indices from reference patient dataset 410 are converted to numerical values ​​and compared to patient indices from patient dataset 400 to calculate a similarity score 420 ("pre-operative similarity") for each reference patient dataset. Reference patient datasets with similarity scores below a threshold may be considered similar to patient dataset 400. For example, in the illustrated embodiment, reference patient dataset 410a has a similarity score of 9, reference patient dataset 410b has a similarity score of 2, reference patient dataset 410c has a similarity score of 5, and reference patient dataset 410d has a similarity score of 8. Because each of these scores is below the threshold of 10, reference patient datasets 410a-d are identified as being similar patient datasets.

[0082] The treatment outcome data of similar patient datasets 410a-d may be analyzed to determine the surgical procedure and / or implant design with the highest probability of success. For example, the treatment outcome data for each reference patient dataset may be converted into a numerical outcome score 430 ("outcome index") that represents the likelihood of a beneficial outcome. In the illustrated embodiment, reference patient dataset 410a has an outcome score of 1, reference patient dataset 410b has an outcome score of 1, reference patient dataset 410c has an outcome score of 9, and reference patient dataset 410d has an outcome score of 2. In embodiments in which a lower outcome score correlates with a higher likelihood of a beneficial outcome, reference patient datasets 410a, 410b, and 410d may be selected. The treatment procedure data from the selected reference patient datasets 410a, 410b, and 410d may then be used to determine at least one surgical procedure (e.g., implant placement, surgical approach) and / or implant design that is likely to produce a beneficial outcome for the treated patient.

[0083] In some embodiments, a method for providing medical care to a patient is provided. The method can include comparing a patient dataset with reference data. The patient dataset and the reference data can include any of the types of data described herein. The method can include using any of the techniques described herein to identify and / or select relevant reference data (e.g., data related to the patient's treatment, such as data from similar patients and / or data from similar therapeutic treatments). A treatment plan can be generated based on the selected data using any of the techniques described herein. The treatment plan can include one or more therapeutic treatments (e.g., surgical procedures, treatment instructions, models or other virtual representations of treatments), one or more medical devices (e.g., implanted devices, instruments for delivering the devices, surgical kits), or a combination thereof.

[0084] In some embodiments, a system for generating a treatment plan is provided. The system can compare a patient dataset with multiple reference patient datasets using any of the techniques described herein. For example, a subset of the multiple reference patient datasets can be selected based on similarity and / or treatment outcome, or any other technique as described herein. A treatment plan can be generated based at least in part on the selected subset using any of the techniques described herein. The treatment plan can include one or more treatment procedures, one or more medical devices, or any other aspect of the treatment plan described herein, or a combination thereof.

[0085] In further embodiments, the system is configured to use historical patient data. The system can select historical patient data for developing or selecting a treatment plan, designing a medical device, etc. Historical data can be selected based on one or more similarities between the current patient and previous patients to develop a normative treatment plan designed for a desired outcome. The normative treatment plan can be tailored to the current patient to increase the likelihood of a desired outcome. In some embodiments, the system can analyze and / or select a subset of historical data to generate one or more treatment procedures, one or more medical devices, or a combination thereof. In some embodiments, the system can use a subset of data from one or more groups of previous patients with beneficial outcomes to generate a historical reference data set used, for example, to design, develop, or select a treatment plan, medical device, or combination thereof.

[0086] 5 is a flow diagram illustrating a method 500 for providing patient-specific medical care in accordance with another embodiment of the present technology. Method 500 can begin at step 502 by receiving a patient dataset for a particular patient in need of treatment. The patient dataset can include data representing the patient's condition, anatomy, medical condition, symptoms, medical history, preferences, intra-operative data, and / or any other information or parameters related to the patient. For example, patient dataset 850 can include surgical intervention data, treatment outcome 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 medical condition, occupation, activity level, organizational information, health assessment, 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.), or the like. The patient dataset may also include image data such as camera images, magnetic resonance imaging (MRI) images, ultrasound images, computer-aided tomography (CAT) scan images, positron emission tomography (PET) images, x-ray images, and the like. In some embodiments, the patient dataset includes data representing one or more of patient identification number (ID), age, sex, 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 by 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 step 502 also stores one or more software modules (e.g., data analysis module 116 and / or treatment planning module 118 shown in FIG. 1, or additional software modules for performing various operations of method 500).Additional details regarding the collection and receipt of patient data sets are described below with respect to Figures 6-7D.

[0087] In some embodiments, the received patient dataset may include disease indices 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 tilt, thoracic kyphosis, etc.), and / or pelvic parameters. The disease indices may include micro-measurements (e.g., measures associated with a particular segment or individual segments of the patient's spine) and / or macro-measurements (e.g., measures associated with multiple segments of the patient's spine). In some embodiments, the disease indices are not included in the patient dataset, and method 500 includes determining (e.g., automatically determining) one or more of the disease indices based on the patient image data, as described below.

[0088] Once the patient dataset is received in step 502, method 500 may continue in step 503 by creating a virtual model of the patient's native anatomy (also referred to as "pre-operative anatomy"). The virtual model may be based on image data included in the patient dataset received in step 502. For example, the same computing system that received the patient dataset in step 502 may analyze the image data in the patient dataset to generate a virtual model of the patient's native anatomy. The virtual model may be a two-dimensional or three-dimensional visual representation of the patient's native anatomy. The virtual model may include one or more regions of interest and may include some or all of the patient's anatomy within the regions of interest (e.g., any combination of tissue types, including, but not limited to, bony structure, cartilage, soft tissue, vascular tissue, neural tissue, etc.). As a non-limiting example, the virtual model may include a visual representation of the patient's spinal region, including some or all of the sacrum, lumbar, thoracic, and / or cervical regions. In some embodiments, the virtual model includes soft tissue, cartilage, and other non-bone structures. In other embodiments, the virtual model includes only the patient's bony structure. Examples of virtual models of native anatomical configurations are described below with respect to Figures 8A and 8B. In some embodiments, method 500 can optionally omit creating a virtual model of the patient's native anatomy in step 503 and proceed directly from step 502 to step 504.

[0089] In some embodiments, the computing system that generated the virtual model in step 502 may also determine (e.g., automatically determine or measure) one or more disease indicators for the patient based on the virtual model. For example, the computing system may analyze the virtual model to determine the patient's pre-operative 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 indicators may include micro-measurements (e.g., indicators associated with a particular segment or individual segments of the patient's spine) and / or macro-measurements (e.g., indicators associated with multiple segments of the patient's spine).

[0090] Method 500 may continue at step 504 by creating a virtual model of the patient's corrected anatomical configuration (which may also be referred to herein as a "planned configuration," "optimized shape," "post-operative anatomical configuration," and / or "target result"). For example, the computing system may use the aforementioned analytical procedures to determine a "corrected" or "optimized" anatomical configuration for a particular patient that represents an ideal surgical outcome for that particular patient. This may be performed, for example, by analyzing multiple reference patient datasets to identify post-operative anatomical configurations of similar patients who had beneficial post-operative outcomes (e.g., based on the similarity of the reference patient datasets to the patient datasets and / or whether the reference patients had beneficial treatment outcomes), as described in detail above with respect to FIGS. 1-4C. This may also include applying one or more mathematical rules that define an optimal anatomical outcome (e.g., positional relationships between anatomical elements) and / or target (e.g., acceptable) post-operative metrics / design criteria (e.g., adjusting the anatomy so that the post-operative sagittal vertical axis is less than 7 mm, the post-operative Cobb angle is less than 10 degrees, etc.). The target post-operative metrics may include, but are not limited to, target coronal parameters, target sagittal parameters, target pelvic intrinsic angles, target Cobb angles, target shoulder slopes, target iliopectineal angles, target coronal balance, target Cobb angles, target lordotic angles, and / or target intervertebral space heights. The difference between the native anatomy and the corrected anatomy may be referred to as the "patient-specific correction," or "target correction."

[0091] After the modified anatomical configuration is determined, the computing system can generate a two-dimensional or three-dimensional visual representation of the patient's anatomy using the modified anatomical configuration. Similar to the virtual model created in step 503, the virtual model of the patient's modified anatomical configuration can include one or more regions of interest and may include some or all of the patient's anatomy within the regions of interest (e.g., any combination of tissue types, including, but not limited to, bony structures, cartilage, soft tissue, vascular tissue, neural tissue, etc.). As a non-limiting example, the virtual model may include a visual representation of the patient's spinal region in the modified anatomical configuration, including some or all of the sacrum, lumbar, thoracic, and / or cervical regions. In some embodiments, the virtual model includes soft tissue, cartilage, and other non-bony structures. In other embodiments, the virtual model includes only the patient's bony structures. Examples of virtual models of native anatomy are described below with respect to FIGS. 9A-1 through 9B-2.

[0092] At step 504, the patient image may be segmented to isolate separate anatomical elements of the target anatomy. The spatial relationships between the isolated anatomical elements may be altered to generate a target or modified patient condition. These alterations may be selected based on regulatory criteria, financial parameters, etc. Other techniques may be used to generate anatomical configurations based on available patient data.

[0093] Method 500 may continue at step 506 by generating (e.g., automatically generating) a surgical plan to achieve the modified anatomical configuration indicated by the virtual model. The surgical plan may include pre-operative plans, surgical plans, post-operative plans, and / or specific spinal landmarks associated with optimal surgical outcomes. For example, the surgical plan may include specific surgical procedures to achieve the modified anatomical configuration. In the context of spine surgery, the surgical plan may include a specific fusion procedure (e.g., PLIF, ALIF, TLIF, LLIF, DLIF, XLIF, etc.) across a specific range of spinal levels (e.g., L1-L4, L1-L5, L3-T12, etc.). Of course, other surgical procedures to achieve the modified anatomical configuration may be identified, such as non-fusion surgical approaches and orthopedic surgical procedures for other areas of the patient. The surgical plan may also include one or more expected spinal landmarks (e.g., lumbar lordosis, Cobb angle, coronal parameters, sagittal parameters, and / or pelvic parameters) corresponding to the expected post-operative patient anatomy. The surgical plan may be generated by the same or a different computing system that created the virtual model of the modified anatomical configuration. In some embodiments, the surgical plan may also be based on one or more reference patient data sets, as described above with respect to FIGS. 1-4C. In some embodiments, the surgical plan may also be based, at least in part, on surgeon-specific preferences and / or outcomes associated with the particular surgeon performing the procedure. In some embodiments, more than one surgical plan is generated in step 506 to provide the surgeon with multiple options. An example surgical plan is described below with respect to FIG. 10.

[0094] After the virtual model of the modified anatomical configuration is created in step 504 and the surgical plan is generated in step 506, method 500 can continue in step 508 by transmitting the surgical plan, including the virtual model of the modified anatomical configuration and the interactive surgical plan, for review by the surgeon. In some embodiments, the virtual model and surgical plan are transmitted as a surgical plan report, an example of which is described with respect to FIG. 11 . In some embodiments, the same computing system used in steps 502-506 can transmit the virtual model and surgical plan to a computing device (e.g., the client computing device 102 described in FIG. 1 ) for review by the surgeon. This transmission can include transmitting the virtual model and surgical plan directly to the computing device or uploading the virtual model and surgical plan to a cloud or other storage system for subsequent download. While step 508 describes transmitting the surgical plan and virtual model to the surgeon, those skilled in the art will understand from the disclosure herein that an image of the virtual model can be included in the surgical plan transmitted to the surgeon and that the actual model need not be included (e.g., to reduce the transmitted file size). Additionally, the information transmitted to the surgeon in step 508 may include a virtual model of the patient's native anatomy (or an image thereof) in addition to the virtual model of the modified anatomy. In embodiments in which more than one surgical plan is generated in step 506, method 500 may include transmitting the two or more surgical plans to the surgeon for review and selection.

[0095] The surgeon reviews the virtual model and the surgical plan and may approve or reject the surgical plan at step 510 (or, if more than one surgical plan is provided at step 508, may select one of the provided surgical plans). If the surgeon does not approve the surgical plan at step 510, the surgeon may optionally provide feedback and / or suggested changes to the surgical plan (e.g., by adjusting the virtual model or changing one or more aspects of the plan). Accordingly, method 500 may include receiving (e.g., via a computing system) the surgeon's feedback and / or suggested changes. If surgeon's feedback and / or suggested changes are received at step 512, method 500 may continue at step 514 by modifying (e.g., automatically via a computing system) the virtual model and / or the surgical plan based at least in part on the surgeon's feedback and / or proposed changes received at step 512. In some embodiments, the surgeon does not provide feedback and / or suggested changes if he or she rejects the surgical plan. In such embodiments, step 512 may be omitted, and method 500 may continue at step 514 by modifying the virtual model and / or surgical plan (e.g., automatically modifying via a computing system) by selecting a new and / or additional reference patient data set. The modified virtual model and / or surgical plan may then be sent to the surgeon for review. Steps 508, 510, 512, and 514 may be repeated as many times as necessary until the surgeon approves the surgical plan. Although described as the surgeon reviewing, modifying, approving, and / or rejecting the surgical plan, in some embodiments, the surgeon may also review, modify, approve, and / or reject the modified anatomical configuration shown via the virtual model.

[0096] After surgeon approval of the surgical plan is received in step 510, method 500 can continue in step 516 by designing a patient-specific implant (e.g., via the same computing system that performed steps 502-514) based on the revised anatomical configuration and surgical plan. The implant (e.g., implant 154 or 161 in FIG. 1) can be designed by mapping the gaps between anatomical elements and filling at least a portion of the gaps with a medical virtual implant. U.S. Patent Application No. 16 / 569,494 discloses techniques for generating a revised patient pathology, mapping the space, designing the implant, and manufacturing the implant. U.S. Patent Application No. 16 / 569,494 is incorporated by reference in its entirety.

[0097] A patient-specific implant may be specifically designed to, when implanted in a particular patient, shift the patient's anatomy to occupy a modified anatomical configuration (e.g., transform the patient's anatomy from a native anatomical configuration to a modified anatomical configuration). A patient-specific implant may be designed to, when implanted, cause the patient's anatomy to occupy the modified anatomical configuration for the expected life of the implant (e.g., 5+ years, 10+ years, 20+ years, 50+ years, etc.). In some embodiments, a patient-specific implant is designed based solely on a virtual model of the modified anatomical configuration and / or without reference to pre-operative patient images.

[0098] The patient-specific implant can be any of the implants described herein or any of the patent references incorporated herein by reference. For example, the patient-specific implant can include one or more of screws (e.g., bone screws, spinal screws, pedicle screws, facet screws), interbody implant devices (e.g., interbody implants), cages, plates, rods, intervertebral 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 discs), hip implants, etc. The patient-specific implant design can include data representing 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 the shape, size, material, and / or effective stiffness of the implant (e.g., lattice density, number of struts, location of struts, etc.) An example of a patient-specific implant designed by method 500 is described below with respect to Figures 12A and 12B.

[0099] In some embodiments, designing the implant in step 516 can 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. For example, a virtual 3D model of one or more patient-specific implants can be created based on filling in the blanks between the anatomical elements of a modified patient condition. The virtual 3D model can be converted into 3D manufacturing data for manufacturing the one or more patient-specific implants.

[0100] In some embodiments, the patient-specific implant is designed in step 516 only after the surgeon has reviewed and approved the virtual model with the modified anatomical configuration and the surgical plan. Thus, in some embodiments, the implant design is not sent to the surgeon along with the surgical plan in step 508, and is not manufactured prior to receiving the surgeon's approval of the surgical plan. Without being bound by theory, waiting to design the patient-specific implant until the surgeon has approved the surgical plan may improve the efficiency of method 500 and / or reduce the resources required to perform method 500.

[0101] Method 500 may continue at step 518 by manufacturing a patient-specific implant. The implant may be manufactured using an additive manufacturing technique, such as three-dimensional printing, stereolithography, digital light processing, fused deposition modeling, selective laser sintering, selective laser melting, electron beam melting, laminated object manufacturing, powder bed printing, thermoplastic printing, direct material deposition, or inkjet photoresist printing, or similar techniques, or a combination thereof. Alternatively or additionally, the implant may be manufactured using a subtractive manufacturing technique, such as CNC machining, electrical discharge machining (EDM), grinding, laser cutting, waterjet machining, manual machining (e.g., milling, turning / 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 a manufacturing system executing computer-readable fabrication instructions generated by a computing system at step 516.

[0102] After the implant is manufactured in step 518, method 500 may continue in step 520 by implanting the patient-specific implant into the patient. 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 a portion of the patient-specific surgical procedure.

[0103] Method 500 may be implemented and performed in a variety of ways. In some embodiments, steps 502-516 may be performed by a computing system associated with a first entity, step 518 may be performed by a manufacturing system associated with a second entity, and step 520 may be performed by a surgical provider, surgeon, and / or robotic surgical platform associated with a third entity. During the surgical procedure, method 500 may collect intra-operative data. Any of the foregoing steps may also be implemented as computer-readable instructions stored in memory and executable by one or more processors of an associated computing system. In some implementations, steps 502-514 are performed using the intra-operative data to provide confirmation that the location and position of the implant during the surgical procedure are within pre-operative planning thresholds (e.g., difference thresholds).

[0104] FIG. 6A is a flow diagram illustrating a method 600 for providing confirmation of intra-operative positioning of a surgical implant, in accordance with another embodiment of the present technology.

[0105] Method 600 can begin at step 602 by displaying an interactive plan generated based on patient data. The patient-specific interactive surgical plan (e.g., plan 132 of FIG. 1 , plan 1000 of FIG. 10A , plan 1020 of FIG. 10B , or superimposed image 1060 of FIG. 10C ) includes displayable planned pathologies for the patient and is configured to receive user input. Pre-operative and / or intra-operative pathologies can be used to validate a diagnosis, treatment eligibility, etc., based on pre-operative measurements such as lumbar lordosis, Cobb angle, pelvic proper angle, disc height, coronal offset distance, segmental flexibility, LL-PI greater than a predetermined angle (e.g., 5 degrees, 10 degrees, 15 degrees, etc.), LL-PI discrepancy (e.g., age-adjusted), sagittal vertical axis offset distance, coronal offset distance, coronal angle, bone quality, and other indices disclosed herein. Exemplary displayed interactive plans and displayable pathologies are described in connection with FIGS. 7A-11.

[0106] Method 600 may continue at step 604 by collecting intra-operative data during a procedure involving a patient-specific implant. For example, a device (e.g., a fluoroscopy device, a radiology device, a C-arm device, an ultrasound device, an MRI device, an X-ray device, a tablet, a camera, etc.) may capture intra-operative data (e.g., continuous imaging, images, etc.) of a patient during a procedure to attach an implant to the patient. Method 600 may collect intra-operative data randomly, periodically, continuously, or at designated stages of the procedure to attach the implant. In some implementations, intra-operative data is collected continuously to create a "live" feed of the medical procedure.

[0107] At step 606, method 600 can display the intraoperative data along with the interactive surgical plan. For example, method 600 can overlay the intraoperative data on the preoperative plan to show differences between the intraoperative data and the preoperative plan. The intraoperative and preoperative images can be configured (aligned) to virtually overlay one another. In some embodiments, method 600 can include overlaying a portion of the preoperative image on the intraoperative image. The intraoperative image can be segmented to isolate anatomical elements. The segmented anatomical elements can be overlaid on the preoperative image to show differences between the planned and actual positions of the anatomical elements. Method 600 can use machine learning or other algorithms to identify matching features in the intraoperative and preoperative images. In other embodiments, anatomical elements from the preoperative plan can be overlaid on the intraoperative image. The relative face-to-face positions of anatomical elements in the preoperative image can be compared to their actual positions in the intraoperative image. Method 600 can compensate for loading conditions in the preoperative image. For example, if a patient has a pre-operative upright x-ray, method 600 can alter the relative positions of anatomical elements based on the patient's loading during surgery. For example, if the patient is lying horizontally, method 600 can move anatomical elements in the pre-operative image to match an unloaded or reclining state. Thus, the pre-operative image can be manipulated or altered based on various loading conditions, patient position, etc.

[0108] Method 600 can match landmarks (e.g., anatomical landmarks, implant landmarks, etc.), reference features, etc. to synchronize or nearly synchronize intraoperative and preoperative images. Landmarks may be selected by the system based on individually identifiable anatomical elements. In some embodiments, a user can select and identify a landmark. For example, a user can review a surgical plan and identify one of more landmarks in preoperative images, virtual models, images of an anatomical model, etc. A synchronization routine can be selected based on the desired accuracy of implant placement. If the implant is positioned near neural tissue (e.g., the spinal cord), a user can select a synchronization routine to ensure the implant is properly spaced from the spinal cord. Fixation elements (e.g., bone screws, fixation plates, etc.) can be used to limit or prevent implant migration after surgery. Method 600 can use machine learning or artificial intelligence to align images by zooming, stretching, and / or rotating the images on a display platform (e.g., a user interface, a screen, a virtual model, etc.). In some implementations, method 600 compares the intraoperative data with the preoperative plan and displays an indication of the differences between the intraoperative data and the preoperative plan in the interactive surgical planning (e.g., tags, highlights, boxes, arrows, etc.). In some embodiments, method 600 allows the user to manipulate the images via the display platform. For example, the user can manually zoom, stretch, crop, rotate, or otherwise manipulate the images to achieve the desired synchronization. The user can select images, adjust images, and control synchronization. In some embodiments, method 600 includes analyzing the image manipulations performed by the user. 600 can generate additional planned images by manipulating one or more preoperative virtual models to generate the additional images. This allows the user to review the planned images that match the perspective and scale of the intraoperative images. In fluoroscopic imaging, method 600 can dynamically overlay preoperative planned images onto continuous, real-time fluoroscopic imaging.As the fluoroscopic imaging device is moved, the system can dynamically move the planned images and key them to the fluoroscopic imaging, allowing the surgical team to acquire images of the patient from different perspectives in real time while continuously viewing the target location of the implant.

[0109] At step 608, method 600 can determine whether the position of the implant in the intraoperative data matches the placement in the preoperative plan. Method 600 can determine whether the position of the implant in the intraoperative data matches the placement in the preoperative plan by determining whether the orientation and position of the implant in the patient are the same as in the preoperative plan. The criteria for determining whether the intraoperative data matches the placement can be selected based on the procedure. In some embodiments, the criteria can be generated using machine learning, implemented by a user, or retrieved from a database containing matching recommendations. The criteria can include, for example, deviation, difference, distance between the intraoperative position and the planned position, distance between the implant and anatomic elements (e.g., landmarks, non-target anatomical elements, nerves, etc.), interface (e.g., interface between the implant and anatomic elements, or a combination thereof), etc.

[0110] 6B is a flow diagram illustrating a method 620 for providing confirmation of intra-operative positioning of a surgical implant, in accordance with an embodiment of the present technology. The steps of method 620 may be performed using the treatment plans described in connection with FIGS. 10A-11. Method 620 may begin at step 622 by acquiring one or more images of the patient (e.g., intra-operative images, pre-operative images, etc.). The images may include the planned location of the implant in the patient and the actual location of the implant in place.

[0111] In step 624, the method 620 may calculate measurements of implant placement in the patient to determine whether the installed implant is in the position (e.g., location, orientation, etc.) determined in the pre-operative model (as described in steps 503-516 of FIG. 5). The measurements may include coordinates of the implant within the patient. For example, the measurements may be the distance of the implant from one or more anatomical elements (e.g., bones, organs, joints, etc.), landmarks, reference features (e.g., other implants), or any location on the patient.

[0112] In some implementations, the measurement is a calculation of the difference (e.g., delta, deviation) between the intraoperative data and the preoperative plan / model. The measurement can include the degree of rotation by which the implant in the patient differs from the preoperative plan and / or an indicator distance the implant in the patient needs to move to match the preoperative plan. In some implementations, the measurement includes a calculation of the percentage by which the intraoperative data matches the preoperative plan (e.g., 89%, 96%, etc.). The method 620 can calculate an indicator regarding the completion of installation of the implant in the patient. The completion threshold percentage can be adjusted based on the severity of the patient's condition. The method 620 can notify the healthcare provider when the completion threshold percentage is reached during the installation procedure.

[0113] At step 626, the method 620 can display the measurements on a user interface (e.g., display 122 of FIG. 1 ) for viewing by a user (e.g., a healthcare provider). The method 620 can display pre-operative and intra-operative metrics (e.g., pre-operative patient metrics or measurements 1002 and intra-operative patient metrics 1004 of FIG. 10A ). The method 620 can display a percentage comparison of the intra-operative data to the pre-operative plan (e.g., as shown by notification 1022 of FIG. 10B ). In some implementations, the method 620 displays an indicator regarding the completion of installation of the implant into the patient (e.g., as shown by notification 1024 of FIG. 10B ). The method 620 can display a live comparison of the intra-operative data to the pre-operative plan (e.g., as shown by plan 1000 of FIG. 10A , plan 1020 of FIG. 10B , or superimposed image 1060 of FIG. 10D ) while the healthcare provider is installing the implant into the patient.

[0114] At step 628, method 620 can generate a notification of the results of the comparison of the pre-operative plan to the intra-operative data. Method 620 can notify a healthcare provider if the results differ from the pre-operative model by a threshold amount. For example, if the location of an implant in a patient is a threshold distance from the location of the implant in the surgical plan, the user can receive a notification to adjust the location of the implant before completing the procedure.

[0115] A machine learning algorithm may be used to perform one or more steps of method 600 of FIG. 6A and method 620 of FIG. 6B. For example, the SPC platform 109 of FIG. 1 may include a machine learning model trained using a selected reference patient data set. Patient images may be input into the trained machine learning model to provide confirmation of intraoperative positioning of surgical implants based on the preoperative plan. The machine learning model may be selected based on a design goal, such as optimized patient outcomes.

[0116] 7A-7D further illustrate selected aspects of providing patient-specific medical care, e.g., according to method 500. For example, FIGS. 7A-7D illustrate an example patient dataset 700 (e.g., as received in step 502 of method 500). Patient dataset 700 can include any of the information previously described with respect to patient datasets. For example, patient dataset 700 includes patient information 701 (e.g., patient identification number, patient MRN, patient name, gender, age, body mass index (BMI), date of surgery, surgeon, etc., shown in FIGS. 7A and 7B), diagnostic information 702 (e.g., Oswestry Disability Index (ODI), VAS back score, VAS leg score, pre-operative pelvic intrinsic angle, pre-operative lumbar lordosis, pre-operative PI-LL angle, pre-operative lumbar coronal Cobb, etc., shown in FIGS. 7B and 7C), and image data 703 (e.g., X-ray, CT, MRI, etc., shown in FIG. 7D). In the embodiment shown, the patient dataset 700 is collected by a healthcare provider (e.g., a surgeon, nurse, etc.) using digital and / or fillable reports that may be accessed using a computing device. In some embodiments, the patient dataset 700 may be generated automatically or at least partially automatically based on the patient's digital medical record. In either case, once collected, the patient dataset 700 may be transmitted to a computing system configured to generate a surgical plan for the patient.

[0117] 8A and 8B illustrate an example virtual model 800 of a patient's native anatomy (e.g., as created in step 503 of method 500). In particular, FIG. 8A is a close-up view of the virtual model 800 of the patient's native anatomy, showing the patient's native anatomy of the lower spinal region. The virtual model 800 is a three-dimensional visual representation of the patient's native anatomy. In the illustrated embodiment, the virtual model includes a portion of the spinal column extending from the sacrum to the L4 vertebral level. Of course, the virtual model can include other regions of the patient's spinal column, including the cervical, thoracic, lumbar, and sacrum. While the illustrated virtual model 800 includes only the bony structure of the patient's anatomy, other embodiments may include additional structures, such as cartilage, soft tissue, vascular tissue, and nervous tissue.

[0118] 8B illustrates a virtual model display 850 (referred to herein as "display 850") showing different views of the virtual model 800. The virtual model display 850 includes a three-dimensional view of the virtual model 800, one or more coronal sections 802 of the virtual model 800, one or more axial sections 804 of the virtual model 800, and / or one or more sagittal sections 806 of the virtual model 800. Of course, other views are possible and may be included in the virtual model display 850. In some embodiments, the virtual model 800 may be interactive, allowing a user to manipulate the orientation or perspective of the virtual model 800 (e.g., rotate it), change the depth of the displayed cross sections, select and isolate particular bony structures, etc.

[0119] 9A-1 through 9B-2 illustrate examples of a virtual model of a patient's native anatomy (e.g., as created in step 503 of method 500) and a virtual model of the patient's modified anatomy (e.g., as created in step 504 of method 500). Specifically, FIGS. 9A-1 and 9A-2 are front and side views, respectively, of a virtual model 910 showing a patient's native anatomy, and FIGS. 9B-1 and 9B-2 are front and side views, respectively, of a virtual model 920 showing the same patient's modified anatomy. Referring first to FIG. 9A-1, the front view of virtual model 910 shows that the patient has an abnormal curvature of the spine (e.g., scoliosis), which is marked by a line X that follows the rostrocaudal axis of the spine. Referring next to FIG. 9A-1, a side view of virtual model 910 shows a patient's intervertebral disc collapsed, or with reduced spacing between adjacent vertebral endplates, as marked by an oval Y. FIGS. 9B-1 and 9B-2 show a virtual model 920 modified to account for the abnormal anatomical configuration shown in FIGS. 9A-1 and 9A-2. For example, FIG. 9B-1, a front view of virtual model 920, shows a patient's spine with corrected alignment (e.g., reduced abnormal curvature). This modification is indicated by line X, which also follows the rostrocaudal axis of the spine. FIG. 9B-2, a side view of virtual model 920, shows a patient's spine with restored disc height (e.g., increased spacing between adjacent vertebral endplates), also marked by an oval Y. Line X and oval Y are provided in FIGS. 9A-1-9B-2 to more clearly illustrate the modifications between virtual models 910 and 920 and are not necessarily included in virtual models generated in accordance with the present techniques.

[0120] 10A shows an example of a surgical plan 1000 (e.g., as generated in step 506 of method 500, method 600 of FIG. 6A, or method 620 of FIG. 6B). The surgical plan 1000 may include pre-operative patient indicia or measurements 1002, intra-operative patient indicia 1004, one or more patient images (e.g., patient image 703 received as part of a patient dataset), a virtual model 910 of the patient's native anatomical configuration (e.g., the patient's anatomy pre-operatively) (which may be the model itself or one or more images derived from the model), and / or an intra-operative virtual model 920 of the patient's modified anatomical configuration (e.g., the patient's anatomy intra-operatively) (which may be the model itself or one or more images derived from the model). Pre-operative patient metrics 1002 may include, but are not limited to, lumbar lordosis, Cobb angle, pelvic intrinsic angle, disc height, coronal offset distance, segmental flexibility, LL-PI greater than a predetermined angle (e.g., 5 degrees, 10 degrees, etc.), LL-PI discrepancy (e.g., age-adjusted), sagittal vertical axis offset distance, coronal offset distance, coronal angle, bone quality, and rotational displacement.

[0121] The virtual model 920 of the patient's anatomy during surgery may optionally include one or more implants 1012 shown to be implanted in the patient's spinal region to show how the patient's anatomy will look after surgery. Although four implants 1012 are shown in the virtual model 920, the surgical plan 1000 may include more or fewer implants 1012, including one, two, three, five, six, seven, eight, or more implants 1012.

[0122] The surgical plan 1000 may include additional information beyond that shown in FIG. 10 . For example, the surgical plan 1000 may include pre-operative instructions, surgical instructions, and / or post-operative instructions. The surgical instructions may include one or more specific procedures to be performed (e.g., PLIF, ALIF, TLIF, LLIF, DLIF, XLIF, etc.) and / or one or more specific targets of the surgery (e.g., fusion of spinal levels L1-L4, fixation screws inserted into the lateral aspect of L4, etc.). Although the surgical plan 1000 is shown as a visual report in FIG. 10 , the surgical plan 1000 may also be encoded with computer-executable instructions that, when executed by a processor connected to a computing device, cause the surgical plan 1000 to be displayed by the computing device. In some embodiments, the surgical plan 1000 may also include machine-readable surgical instructions for executing the surgical plan. For example, the surgical plan may include surgical instructions for a robotic surgical platform to perform one or more steps of the surgical plan 1000.

[0123] FIG. 10B illustrates a plan 1020 including preoperative imaging, a preoperative plan, intraoperative images, and postoperative images to enable assessment of surgical goal achievement, according to an embodiment. The plan 1020 may display a notification 1022 of the percentage of the intraoperative data comparison to the preoperative plan (e.g., 89%). The plan 1020 may display a notification 1024 that is an indication of completion of implant installation in the patient (e.g., 93%). The preoperative planning image may be generated based on one or more preoperative images, a virtual model (e.g., a 3D virtual model), and / or other data disclosed herein. The data may be manipulated or altered to compensate for loading conditions, for example, by repositioning features in the virtual model to match the loading conditions during surgery. The planned image in FIG. 10B illustrates the planned positions of the patient's anatomical elements. The planned image may also include additional features from the preoperative image, such as the fixation system in the illustrated preoperative image. A previously implanted fixation system may be used as a marker to match the intraoperative and planned images.

[0124] FIG. 10C illustrates a plan 1040 including pre-operative imaging, pre-operative planning, intra-operative images, and post-operative images to enable assessment of achievement of surgical goals, according to an embodiment.

[0125] 10D shows an image 1060 overlaid to align the pre-operative plan with the intra-operative image to enable assessment of achievement of surgical goals, according to an embodiment. Image 1060 shows a first stage 1062 (pre-operative plan) and a second stage 1064 (intra-operative image) showing the differences between the pre-operative plan and the intra-operative image. This positional information can be used to reposition the implant. The image can include various types of positional information, such as the location of the implant relative to the target planned location, the distance between the implant and anatomical features, a boundary indicating the target location of the patient-specific implant, and / or labeling of patient anatomical elements proximate to the patient-specific implant.

[0126] Image 1060 in FIG. 10D is a radiological image providing a lateral view of the patient. The radiological image may be acquired using an X-ray machine, a fluoroscopic imaging device, or other radiological imaging device. Other types of images may be acquired and compared. For example, image 1060 may include a radiological image from an X-ray machine or a C-arm machine superimposed on a continuous fluoroscopy provided by a fluoroscopic imaging device. In some embodiments, image 1060 may include pre-operative images, images from a virtual model, and intra-operative images. The number, type, and resolution of the images may be selected based on the comparison. In some embodiments, the system may determine a viewpoint for the intra-operative image data and, from this viewpoint, generate one or more reference images of the planned positions of the patient-specific implants. The system may control the imaging device (or provide instructions to the user) to capture the intra-operative image data from a target viewpoint. Matching the viewpoint may facilitate comparison of the image data.

[0127] The system (e.g., system 100 of FIG. 1 ) can overlay the intraoperative data (second stage 1064) on the preoperative plan (first stage 1062) to show differences between the intraoperative data and the preoperative plan. The intraoperative and preoperative images can be configured (aligned) to be virtually overlaid on one another. In some embodiments, the system overlays a portion of the preoperative image on the intraoperative image. The intraoperative image can be segmented to isolate anatomical elements. The segmented anatomical elements can be overlaid on the preoperative image to show differences between the planned and actual positions of the anatomical elements. The system can use machine learning or other algorithms to identify matching features in the intraoperative and preoperative images. In other embodiments, anatomical elements from the preoperative plan can be overlaid on the intraoperative image. The relative face-to-face positions of anatomical elements in the preoperative image can be compared to their actual positions in the intraoperative image. The system can compensate for loading conditions in the preoperative image. For example, if a patient has a pre-operative upright x-ray, the system can alter the relative positions of anatomical elements based on the patient's loading during surgery. For example, if the patient is lying horizontally, method 600 can move the anatomical elements in the pre-operative image to match the unloaded or recumbent state. Thus, the pre-operative image can be manipulated or altered based on various loading conditions.

[0128] The system (e.g., system 100 of FIG. 1) can calculate differences (e.g., delta) between the intraoperative data (second stage 1064) and the pre-operative plan (first stage 1062). The measurements can include the degree of rotation by which the implant in the patient differs from the pre-operative plan and / or the indicated distance the implant in the patient needs to move to match the pre-operative plan. The user interface can display the measurements and the overlaid image to the user. For example, image 1060 of FIG. 10D shows a live comparison between the intra-operative image (second stage 1064) and the pre-operative image (first stage 1062) during a surgical procedure. In some embodiments, a threshold delta can be determined by the system, entered by the user, or the like. The system can notify the user if the measurement exceeds the threshold delta. In some procedures, the threshold delta can be based on the envelope, boundary, or other target feature of the implant treatment, determined by the system, the user, or the like. For example, the user can draw two-dimensional or three-dimensional boundaries on the anatomical image for acceptable implant positions. The system can then determine whether the implant, or a sufficient amount of the implant, is positioned within the boundaries.

[0129] A user (e.g., a healthcare provider such as a surgeon) can manipulate (e.g., zoom, stretch, crop, and / or rotate) the intraoperative images (second stage 1064) to match them with preoperative images (e.g., first stage 1062), images (e.g., images of a virtual model), anatomical renderings, or other images displaying anatomical location information on the device. In some cases, the user can zoom, stretch, and rotate the virtual 3D model (or other preoperative images) to match them with the intraoperative images on the device or other display platform. In some embodiments, the system analyzes the preoperative data and then manipulates the preoperative data (e.g., preoperative images, virtual 3D model, etc.) to align or otherwise synchronize the preoperative and intraoperative data. For example, the system can generate images of the virtual 3D model of the patient's anatomy in a modified configuration so that they match the intraoperative images.

[0130] The system can calculate a completion score for the surgical procedure (e.g., notification 1024 in FIG. 10B) and display this score on the display. In the example of FIG. 10D, the device captures intraoperative images (second stage 1064) and displays the intraoperative images over the preoperative plan (first stage 1062). The system or user can scale and orient the intraoperative images to closely match the preoperative plan, reflecting the locations of anatomical landmarks and implants. This matching can be performed using one or more segmentation programs, best-fit algorithms, image manipulation programs, etc.

[0131] FIG. 10E illustrates intraoperative images and a surgical model displayed on a user interface 1082, according to an embodiment. The system can perform one or more checks to repeatedly check the positions of anatomical features, the position of instruments, and / or the placement and / or positioning of one or more implants, as described in more detail with reference to the system 100 of FIG. 1 . The checks can include, for example, dynamic checks, static checks, etc. The system can acquire and perform checks on image data (e.g., preoperative images, intraoperative images, etc.), reference models, and anatomical models. The images can be acquired by one or more C-arms, X-ray machines, cameras (e.g., cameras that capture sequential photographs for use in sequential position checks), MRI machines, scanners, etc. The images can include images of the patient's anatomy, implants, equipment positioned in or near the patient, etc. Image characteristics (e.g., resolution, number of pixels, etc.) can be selected to enable the system to perform one or more image processing techniques. The system can adjust settings on the imaging equipment to improve the accuracy of image data capture and identification using image processing techniques, etc.

[0132] Images acquired by one or more visualization systems are referred to as "radiographs," "radiographic images," "intraoperative images," and "radiographic-intraoperative images." Images may be configured (adjusted) to be virtually overlaid on a plan, including a preoperative plan, an intraoperative plan, etc. (or vice versa). In some embodiments, the system may acquire a series of images showing one or more implants positioned within the patient's body. The surgeon can then move the implant to a new location. The implant may be imaged again to evaluate the new location. This process may be repeated any number of times, continually or sequentially imaging the implant at various locations within the patient until the implant is in the proper position.

[0133] The system can automatically acquire images of the patient based on, for example, one or more surgical plans, predefined times, etc. Additionally or alternatively, the surgical team can control the imaging equipment to acquire images at desired times. The system can provide instructions for positioning the imaging equipment (e.g., C-arm, X-ray machine, fluoroscopic imager, etc.) to acquire appropriate images for comparison with, for example, surgical plans, pre-operative simulations showing target positions, etc. These instructions can use the imaging equipment used, imaging settings, target orientation / position of the imaging equipment, etc.

[0134] The system can perform any number of implant position checks to verify that the implant is in an acceptable position. The position checks can be non-invasive, image-guided checks to intraoperatively analyze the current position of the implant based on acquired images of the patient. The system can identify the implant in the images and then synchronize the implant data in the surgical plan with the patient images. For example, the system can synchronize a virtual anatomical model in the surgical plan with the radiological images and then compare the physical implant position with the target or acceptable implant position. This process can be repeated until the implant is positioned in an acceptable position within the patient based on the comparison. Images can be repeatedly acquired during the surgical procedure to evaluate implant delivery.

[0135] The system can perform a non-invasive image-guided implant position check by analyzing the image to, for example, identify implant information (e.g., the contour of the implant in a radiological image (image acquired using a camera, C-arm, X-ray, etc.)), identify anatomical information (e.g., the type of anatomical element near the implant, the type of tissue, etc.). The system can then compare the reference implant contour to the imaged implant shape to define the current anatomical orientation of the implant. The reference implant contour may be taken from a set of implant contours (e.g., lateral contour, superior contour, oblique contour, etc.) from various perspectives. These implant contours may be generated from a virtual model of the implant (e.g., a CAD model of the implant) or may be drawn by a user (e.g., drawn via a touchscreen). In some embodiments, the system can generate the implant contour based on the perspective and / or the current anatomical orientation of the implant. In some embodiments, the system can identify one or more image-keying features of the implant. Examples of image-keying features may include, for example, opaque markers, edges, or other features of the implant that may be identified using image processing techniques. The system can retrieve image keying feature information from a database containing implant designs. For example, a patient-specific implant can have an associated virtual model (e.g., a 3D virtual model, a CAD file, etc.), a keying feature file, data to identify the implant, implant orientation determination, unique keying features, etc. The system can match the keying features of the reference image with corresponding features of the implant in the image to determine the location and orientation of the implant within the patient.

[0136] The system can execute one or more synchronization routines using the image data and non-image data to instruct an imaging system (e.g., a camera system, a robotic C-arm imaging system, an X-ray system) and / or to provide instructions for acquiring additional images. For example, the synchronization routine can include matching indicia (e.g., keying features) to synchronize or nearly synchronize images (e.g., images acquired to perform a check) with one or more virtual models, pre-operative plans, intra-operative plans, etc. Additionally or alternatively, the system can manipulate components of the virtual 3D model based on the captured images. For example, components of the virtual 3D model can be manipulated to match radiographs acquired by a camera, X-ray, C-arm, etc. The virtual 3D model (or components thereof) can be manipulated (e.g., by zooming, stretching, cropping, and / or rotating the virtual 3D model) to match the 3D virtual model with the radiograph. The 3D virtual model can include an anatomical model, an implant model, an instrument model, etc., representing the patient's anatomy. In some embodiments, this alignment may be performed using one or more best-fit routines, for example, using one or more edge detection routines, segmentation routines, filtering routines, image recognition routines, or a combination thereof. The system may confirm implant placement by verifying that the implant in the intraoperative image (e.g., a radiograph) is in the same position as the implant's placement in the pre-operative surgical plan. The placement may be scored based on differences between the pre-operative and intraoperative images. The scoring routine may determine the distance between a target position window and the actual position of the implant. If the actual position is within the target position window, the system may indicate that the implant is in the target position. The target position window may be determined using an ML model, input by a user, etc. In some embodiments, the system may confirm that the implant is positioned in the target position based on a particular portion of the implant contacting a target anatomical feature.

[0137] In some embodiments, the system can perform real-time checks on images captured within an augmented reality (AR) application (e.g., continuously captured images acquired using a C-arm device 1088). For example, the system can use a camera function within the AR application to display intra-operative radiographic images on the user interface 1082. The camera function of the AR application displays intra-operative radiographic images on the user interface 1082 without requiring a camera on the user interface 1082 to capture the intra-operative images. As shown in the user interface 1082, radiographic images can be captured prior to implant treatment of the implant. As described in further detail with reference to FIG. 10F , subsequent radiographic images can be captured and displayed on the user interface to show the implant and / or inserter entering the anatomical space, being attached, etc.

[0138] As shown in user interface 1084, implant 1086 is outlined or highlighted in the 3D surgical implant plan displayed within the AR application on user interface 1084. These images can be viewed and / or displayed on a user device (e.g., a smartphone, tablet, other computing device, etc.) configured with the AR application to perform real-time checks against the radiological image. In some embodiments, a user can open the AR application and hold the user device in a manner that allows them to view the radiological image displayed on user interface 1084. The AR application can use this system to identify the viewpoint of the implant and / or the radiological image and match the implant contour (e.g., from a pre-operative three-dimensional (3D) surgical implant plan, etc.) to the radiological image. The AR application can then match the 3D surgical implant plan to the radiological image based on anatomical landmarks (e.g., anatomical elements, tissue types, etc.) or implant contours (e.g., implant projection, etc.) identified in the radiological image. As described herein, a user can reorient the 3D surgical implant plan to match the acquired radiological image (e.g., by zooming, stretching, cropping, and / or rotating the implant plan).

[0139] FIG. 10F shows an image of an implant and inserter device displayed on a user interface, according to an embodiment. As the surgical procedure progresses and subsequent radiographic images are acquired by the C-arm device (as described in FIG. 10E), the AR application can track the progress of the implant's position relative to adjacent anatomical structures and / or the 3D surgical implant plan. For example, as shown in user interface 1092, the 3D surgical plan (such as that displayed in user interface 1084 of FIG. 10E) is placed over or "snapped" to the radiographic image in a semi-transparent overlay. As further displayed in user interface 1092, inserter instrument 1093 and implant 1095 (e.g., attached to or held by the inserter instrument) are radiopaque and can be seen on user interface 1092 as they advance toward target implant location 1091a (shown in orange or a color selected by the user). The implant may then be moved as each radiographic image is acquired to match the anatomy of the 3D surgical implant plan. As shown in the user interface 1094, the inserter 1093 and implant 1095 are advanced generally closer to the implant 1091a of the 3D surgical implant plan. The implant 1091a of the 3D surgical implant plan may change color depending on the implant's optimal placement or proximity to the target placement. The optimal placement may be determined using an ML engine or input by the user.

[0140] As shown in user interface 1094, implant 1091b of the 3D surgical implant is shown in a different color (e.g., yellow or another color selected by the user) to indicate that implant 1095 and inserter instrument 1093 are generally closer to an optimal placement. The optimal placement may be selected by the computer system and / or a user (e.g., a doctor, surgeon, surgical team, etc.) when creating a pre-operative surgical plan. Thereafter, as radiographic images are acquired, the implant displayed and tracked by the radiograph is moved closer to the optimal position. Annotations may be added to the images to provide assistance, such as positioning instructions, physician notes, vitals, implant information, etc. The user interface 1096 shows implant 1095 and inserter instrument 1093 in an optimal (or acceptable) position, so that target implant position 1091c in the 3D surgical implant plan is updated, for example, to green. Other types of imaging may be used for real-time or near-real-time imaging.

[0141] The acceptable position can be determined using an ML engine or input by the user and can be a position within a maximum allowable distance from the optimal position. In some embodiments, when the implant reaches an acceptable position (or optimal position), the 3D surgical plan is updated with one or more confirmation messages (e.g., a sound, a color change, other audible or visual cues, etc.) and / or a final image is acquired and saved to the patient data. Additional measurements can be taken to confirm implant placement. In some embodiments, measurements can be displayed on the user interface 1092 in addition to the 3D surgical plan snapped to the radiographic image. These additional measurements can be, for example, distances between anatomical features, distances between the implant and anatomical features, distances between the intended placement and the actual placement of the implant, distances between devices (e.g., instruments, instruments and implants, etc.), angular positions of devices, etc.

[0142] The system can analyze the surgical plan to determine whether the implant should be repositioned and can generate instructions to move the implant toward the optimal position. These instructions can be output during surgery to assist in implant repositioning. For example, the instructions can be displayed via the user interface 1094 and can include text, annotations (e.g., arrows, boxes, etc.), measurements, drawings / images, and / or surgical steps, and can be superimposed on a displayed image (e.g., a radiographic image). In some embodiments, an optimal or acceptable position of the implant can be inserted or superimposed on the image to show the surgeon the difference between the optimal and current position of the implant. In some embodiments, the optimal position can be indicated using an outline, label, or annotation of the implant. In some embodiments, the system can identify an acceptable position window based on the optimal position, thereby enabling the surgeon to place the implant while allowing fine-tuning to improve results. The system can also perform any number of intraoperative simulations based on the intraoperative images to update the surgical plan, modify the acceptable position window or optimal position, and provide additional feedback to assist with the surgical procedure.

[0143] FIG. 11 provides a series of images illustrating an example of a patient surgical plan report 1100 that includes the surgical plan 1000 and may be sent to the surgeon for review and approval (e.g., as sent in step 508 of method 500). The surgical plan report 1100 may include a multi-page report detailing aspects of the surgical plan 1000. For example, the multi-page report may include a first page 1101 that provides an overview of the surgical plan 1000 (e.g., as shown in FIG. 10), a second page 1102 that shows a patient image (e.g., patient image 703 shown in FIG. 7D , received in step 502), a third page 1103 that shows a close-up of a virtual model of the modified anatomical configuration (e.g., virtual model 920 shown in FIG. 9), and a fourth page 1104 that prompts the surgeon to approve or reject the surgical plan via user input elements 901 (e.g., one or more buttons, a drop-down menu, etc.). The surgical plan report 1100 may include one or more pre-operative indicators for predetermined symptoms.

[0144] Page 2 1102 may include pre-operative indicators 1109 determined based on patient images 1113. The pre-operative indicators 1109 may be used to perform reimbursement analysis, including whether a procedure, kit, instrument, implant, or other treatment-related item or step qualifies for payment or reimbursement. In some embodiments, planned indicators 1118 (page 1101) may be used to verify the validity of a predicted outcome for a given condition to qualify for payment or reimbursement.

[0145] Page 2 1102 may also include reimbursement data 123 and regulatory data 127. Reimbursement data 123 may include the data described in connection with FIG. 10B. Output (e.g., a recommended code) may be labeled on the displayed image 703. Pre-operative indicators 1109 that correlate with the coding may be displayed in bold or otherwise identified. This allows the user to simultaneously view reimbursement information and the physiology associated with those reimbursements. Regulatory data 127 may include images of a virtual model with anatomical features and regulatory-compliant implants. The planned anatomical model (e.g., virtual anatomical model 920 of FIG. 10A) may include implants with configurations approved by regulatory agencies. Thus, the physician can be confident that the implants and planned results are based on technology approved by regulatory agencies.

[0146] In some embodiments, the system can measure anatomical features and generate a virtual model. The system can then generate a regulatory-compliant implant that matches the model. If the physician modifies the model or implant, resulting in a non-compliant treatment or implant, the system can generate a warning indicating that regulatory compliance is not being maintained. Advantageously, page 1102 allows the user to simultaneously view patient images, planned anatomical models, compliance-based planned pathologies, reimbursement data, and regulatory data. Additionally, correlations between various elements of different datasets can be identified to allow the viewer to understand the interrelationships.

[0147] Of course, additional information about the surgical plan may be presented with the report 1100 in the same or a different format. In some embodiments, if the surgeon rejects the surgical plan 1000, the surgeon may be prompted to provide feedback regarding aspects of the surgical plan 1000 that the surgeon would like adjusted.

[0148] The patient surgical plan report 1100 may be presented to the surgeon on a digital display of a computing device (e.g., the client computing device 102 shown in FIG. 1). In some embodiments, the report 1100 is interactive, allowing the surgeon to manipulate various aspects of the report 1100 (e.g., adjust the view of the virtual model, zoom in, zoom out, add annotations, etc.). However, even though the report 1100 is interactive, the surgeon typically cannot directly modify the surgical plan 1000. Rather, the surgeon may provide feedback and suggested changes to the surgical plan 1000, which may be sent back to the computing system that generated the surgical plan 1000 for analysis and improvement.

[0149] FIG. 12A shows an example of a patient-specific implant 1200 (e.g., as designed in step 516 and manufactured in step 518 of method 500), and FIG. 12B shows the implant 1200 implanted in a patient. The implant 1200 can be any orthopedic or other implant specifically designed to cause the patient's body to conform to a previously identified corrected anatomical configuration. The implant 1202 can be based on a design generated by mapping the gaps between segmented anatomical elements of the corrected pathology. The gaps are then filled with a virtual implant. In payment-constrained embodiments, the configuration of the gaps can be selected based on one or more parameters of a reimbursable virtual implant. For example, the implant 1200 can be a cervical fusion implant, a lumbar fusion implant, an artificial disc, an expandable interbody cage, or other implant disclosed herein.

[0150] For example, the system 100 of FIG. 1 can retrieve insurance information about a patient from database 151. The system 100 can then retrieve one or more design parameters from database 151 based on the retrieved insurance information. The treatment model 181 can then use the retrieved design parameters to design a patient-specific implant 1202. The design parameters can be a configuration (e.g., implant footprint shown in dashed lines in FIG. 12A ) for a device approved for use by a regulatory or governmental agency, a payment requirement, an imbursement requirement, etc. The system can notify the user of at least one reimbursement code for review and approval by the user before manufacturing the implant 1202.

[0151] In the illustrated embodiment, the implant 1200 is an interbody device having a first surface (e.g., upper surface) 1202 configured to mate with the lower endplate surface of the superior vertebral body and a second surface (e.g., lower surface) 1204 configured to mate with the upper endplate surface of the inferior vertebral body. The first surface 1202 has a patient-specific topography designed to match (e.g., mate) with the topography of the inferior endplate surface of the superior vertebral body, forming a generally gapless interface between the surfaces. Similarly, the second surface 1204 has a patient-specific topography designed to match or mate with the topography of the superior endplate surface of the inferior vertebral body, forming a generally gapless interface between the surfaces. The implant 1200 may also include a depression 1206 or other feature configured to promote bone ingrowth. Because the implant 1200 is patient-specific and designed to induce geometric variations in the patient, the implant 1200 is not necessarily symmetrical and is often asymmetrical. For example, in the embodiment shown, implant 1200 has a non-uniform thickness such that the plane defined by first surface 1202 is not parallel to central longitudinal axis A of implant 1200. Of course, because the implants described herein, including implant 1200, are patient-specific, the present technology is not limited to any particular implant design or characteristics. Additional features of patient-specific implants that can be designed and manufactured in accordance with the present technology are described in U.S. Patent Application Nos. 16 / 987,113 and 17 / 100,396, the disclosures of which are incorporated herein by reference in their entireties.

[0152] The patient-specific medical procedures described herein can include implanting two or more patient-specific implants into a patient to achieve a corrected anatomical configuration (e.g., a multi-site procedure). For example, FIG. 13 illustrates a lower spinal region with three patient-specific implants 1300a-1300c implanted at different spinal levels. More specifically, a first implant 1300a is implanted between the L3 and L4 vertebral bodies, a second implant 1300b is implanted between the L4 and L5 vertebral bodies, and a third implant 1300c is implanted between the L5 vertebral body and the sacrum. Together, the implants 1300a-c can cause the patient's spinal region to assume a previously identified corrected anatomical configuration (e.g., transform the patient's anatomy from a diseased pre-operative configuration to an optimized post-operative configuration). In some embodiments, more or fewer implants are used to achieve the corrected anatomical configuration. For example, in some embodiments, one, two, four, five, six, seven, eight, or more implants are used to achieve the corrected anatomical configuration. In embodiments including two or more implants, the implants do not necessarily have the same shape, size, or function. In practice, multiple implants often have different shapes and topographies to correspond to the target spinal levels at which they are implanted. As also shown in FIG. 13, the patient-specific medical procedures described herein can include treating a patient at multiple target regions (e.g., multiple spinal levels).

[0153] In addition to designing patient-specific medical care based on reference patient datasets, the systems and methods of the present technology can also design patient-specific medical care based on a particular patient's disease progression. Accordingly, in some embodiments, the present technology includes a software module (e.g., a machine learning model or other algorithm) that can be used to analyze, predict, and / or model a particular patient's disease progression. The machine learning model can be trained based on multiple reference patient datasets that include disease progression indicators for each of the reference patients in addition to the patient data described with reference to FIG. 1 . The progression indicators can include measurements of the disease indicators over a period of time. Suitable indicators may include spinopelvic parameters (e.g., lumbar lordosis, pelvic tilt, sagittal vertical axis (SVA), Cobb angle, coronal offset, etc.), disability scores, functional ability scores, flexibility scores, VAS pain scores, etc. The progress of indicators for each reference patient can be correlated with other patient information (e.g., age, sex, height, weight, activity level, diet, etc.) of the particular reference patient.

[0154] In some embodiments, the present technology includes a disease progression module that includes an algorithm, machine learning model, or other software analysis tool for predicting disease progression in a particular patient. The disease progression module can be trained based on a reference patient data set that includes patient information (e.g., age, sex, height, weight, activity level, diet) and disease indicators (e.g., diagnosis, spinopelvic parameters such as lumbar lordosis, pelvic tilt, sagittal vertical axis, Cobb angle, coronal offset, disability score, functional ability score, flexibility score, VAS pain score, etc.). The disease indicators can include values ​​over a period of time. For example, the reference patient data can include values ​​of the disease indicators on a daily, weekly, monthly, bimonthly, yearly, or other basis. By measuring the indicators over a period of time, changes in the indicator values ​​can be tracked as an estimate of disease progression and correlated with other patient data.

[0155] Thus, in some embodiments, the disease progression module can estimate the rate of disease progression for a particular patient. This progression may be estimated by providing an estimated change in one or more disease indicators over a period of time (e.g., X% increase per year in a disease indicator). This rate can be constant (e.g., a 5% increase per year in pelvic tilt) or variable (e.g., a 5% increase in pelvic tilt in year 1, a 10% increase in pelvic tilt in year 2, etc.). In some embodiments, the estimated rate of progression can be transmitted to a surgeon or other healthcare provider, who can review and update the estimate as needed.

[0156] 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 can analyze the patient reference dataset to identify disease progression in individual reference patients who have one or more similarities to the 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 particular patient). Based on this analysis, the disease progression module can predict the rate of disease progression in the absence of surgical intervention (e.g., if no surgical intervention occurs, the patient's VAS pain score may increase by 5%, 10%, or 15% each year; if no surgical intervention occurs, the SVA value may continue to increase by 5% each year, etc.).

[0157] The systems and methods described herein can also generate models / simulations based on estimated rates of disease progression, thereby modeling various outcomes over a desired time period. Additionally, the models / simulations can consider any number of additional diseases or conditions to predict a 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 review by a surgeon and / or incorporated into disease progression estimates. Thus, the technology can generate one or more virtual simulations of predicted disease progression to show how a 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 received physician input for review by healthcare providers, patients, etc.

[0158] In some embodiments, the technology can also predict, model, and / or simulate disease progression based on one or more potential surgical interventions. For example, the disease progression module may simulate how a patient's anatomy may look one, two, five, or ten years after surgery for multiple surgical intervention options. The simulations may also incorporate non-surgical factors, such as the patient's age, height, weight, gender, activity level, and other health conditions, as described above. Based on these simulations, the system and / or surgeon can select the surgical intervention most suitable for long-term effectiveness. These simulations may also be used to determine patient-specific modifications to compensate for predicted disease progression.

[0159] 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 multiple different surgical intervention options or other situations. For example, a disease progression module can generate a model that predicts post-operative disease progression for each of three different surgical interventions. A surgeon or other healthcare provider can review the disease progression models and, based on the review, select the one of the three surgical intervention options that is likely to provide the best long-term outcome for the patient. Of course, as described herein, the selection of the optimal intervention can also be fully automated or semi-automated.

[0160] Based on the modeled disease progression, the systems and methods described herein can also (i) identify the optimal time for surgical intervention and / or (ii) identify the optimal type of surgical procedure for a patient. Thus, in some embodiments, the present technology includes an intervention timing module that includes an algorithm, machine learning model, or other software analysis tool for determining the optimal time for surgical intervention in a particular patient. This determination can be performed, for example, by analyzing patient reference data that includes (i) the individual reference patient's pre-operative disease progression indicators, (ii) the individual reference patient's disease indicators at the time of surgical intervention, (iii) the individual reference patient's post-operative disease progression indicators, and / or (iv) the individual reference patient's scored surgical outcomes. The intervention timing module can compare the particular patient's disease indicators with a reference patient dataset to determine the point in disease progression at which surgical intervention would have the most beneficial outcome for similar patients.

[0161] As a non-limiting example, the reference patient dataset may include data associated with the reference patient's sagittal vertical axis. The data may include (i) the individual patient's sagittal vertical axis value over a period of time prior to the surgical intervention (e.g., the rate and extent to which 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 the 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 foregoing data, the intervention timing module can identify when surgical intervention is most likely to result in a beneficial outcome based on the particular patient's sagittal vertical axis value. Of course, the foregoing indicators are provided merely by way of example, and the intervention timing module can incorporate other indicators (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 to predict when surgical intervention is most likely to result in a beneficial outcome for a particular patient.

[0162] The intervention timing module may also incorporate one or more mathematical rules based on thresholds for various disease indicators. For example, the intervention timing module 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 discrepancy 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 discrepancy greater than 20 degrees. Of course, other thresholds and indicators can be used, and the above are provided merely as examples and in no way limit the present disclosure. In some embodiments, the aforementioned 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 a threshold indicating that surgical intervention is recommended, the intervention timing module 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.

[0163] The present technology may also include a treatment planning module that can identify the type of surgical procedure that is optimal for a patient based on the patient's disease progression. The treatment planning module can be an algorithm, machine learning model, or other software analysis tool that has been trained or otherwise based on multiple reference patient datasets, as described above. The treatment planning module 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 may recommend an anterior fusion procedure, but if the LL / PI mismatch is greater than 20 degrees, the treatment planning module may recommend both anterior and posterior fusion procedures. As another non-limiting example, if the SVA value is between 7 and 15 mm, the treatment planning module may recommend posterior fusion procedures, but if the SVA is greater than 15 mm, the treatment planning module may recommend both posterior and anterior fusion procedures. Of course, other rules can be used, and the above are provided merely as examples and in no way limit the present disclosure.

[0164] Without being bound by theory, incorporating disease progression modeling into the patient-specific medical treatments described herein can further improve the effectiveness of treatment. For example, in many cases, it may be disadvantageous to operate after a patient's disease has progressed to an irreversible or unstable state. However, it may also be disadvantageous to operate too early, before the patient's disease causes symptoms and / or when the patient's disease cannot progress further. Therefore, the disease progression module and / or intervention timing module can help identify the time frame in which surgical intervention in a particular patient is most likely to result in a beneficial outcome for the patient.

[0165] As one skilled in the art will appreciate, any of the software modules previously described may be combined into a single software module to perform the operations described herein. Similarly, software modules may be distributed across any combination of the computing systems and devices described herein and are not limited to the specific arrangements described herein. Thus, any of the operations described herein may be performed by any of the computing devices or systems described herein, unless expressly stated otherwise.

[0166] The foregoing detailed description has illustrated various embodiments of devices and / or processes through the use of 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, it will be understood by those skilled in the art that each function and / or operation included in such block diagrams, flowcharts, or examples can be implemented individually and / or collectively by a wide range of hardware, software, firmware, or virtually any combination thereof. In some embodiments, portions of the subject matter described herein may be implemented by Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), digital signal processors (DSPs), or other integrated forms. 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 virtually any combination thereof, and that designing circuitry and / or writing software and / or firmware code is well within the skill of those skilled in the art in light of this disclosure. Additionally, those skilled in the art will understand that the subject mechanisms described herein can be distributed as a program product in various forms, and that example embodiments of the subject matter described herein apply regardless of the particular type of signal-bearing medium used to actually effect the distribution.Examples of signal bearing media include, but are not limited to, recordable type media such as floppy disks, hard disk drives, CDs, DVDs, digital tape, computer memory, and transmission type media such as digital and / or analog communications media (e.g., fiber optic cables, wave guides, wired communications links, wireless communications links, etc.). [Example]

[0167] The present technology is illustrated, for example, according to various aspects described below. Various embodiments of aspects of the present technology are described as numbered embodiments (1, 2, 3, etc.) for convenience. These are provided as examples and do not limit the present technology. It is noted that any of the dependent embodiments may be combined in any suitable manner and arranged in each independent embodiment. Other embodiments may be presented in a similar manner. 1. A method comprising: (a) acquiring at least one image of an implant located in a patient; (b) identifying the implant in at least one image; (c) synchronizing the virtual anatomical model and the at least one image; (d) comparing the location of the implant in the patient with the location of the virtual implant in the virtual anatomical model based on the synchronized virtual anatomical model and the at least one image; repeating all or a portion of steps (a) through (d) and determining, based on the comparison, that the implant is positioned in an acceptable location within the patient; A method comprising: 2. Synchronizing the virtual anatomical model and at least one image includes manipulating the virtual anatomical model and / or the at least one image of the pre-operative plan according to a best fit routine; all or part of steps (a)-(d) are repeated successively using images of the patient showing the implant in different positions; The method described in Example 1. 3. The method of any of Examples 1-2, wherein manipulating the virtual anatomical model and / or the at least one image includes at least one of zooming, stretching, or rotating the virtual anatomical model to match anatomical features in the virtual anatomical model with corresponding anatomical features in the at least one image. 4. The method of any of Examples 1-3, wherein manipulating the virtual anatomical model and / or the at least one image includes at least one of zooming, stretching, or rotating the at least one image to align the at least one image with the virtual anatomical model. 5. The method of any of Examples 1-4, further comprising determining acceptable positions within the patient using an ML engine, wherein the acceptable positions are input by a user. 6. The method of any of Examples 1-5, further comprising continuously receiving images of the patient and repeatedly performing steps (a)-(d) using respective ones of the received images. 7. Retrieving a patient-specific surgical plan for the patient; generating instructions to move the implant toward a target position in a patient-specific surgical plan; causing commands to be output during surgery to assist in implant repositioning; The method according to any one of Examples 1 to 6, further comprising: 8. At least one image is a perspective image; the synchronization includes performing anatomical alignment between the virtual anatomical model and the fluoroscopic image; The allowable position is the maximum allowable distance from the target position of the implant; The method according to any one of Examples 1 to 7. 9. Obtaining implant type information regarding implants; acquiring intra-operative image data from one or more imaging devices during a surgical procedure; selecting an implant type deviation analysis based on the implant type information; generating a deviation annotation for positioning the implant inserted in the patient based on the deviation analysis of the implant type; displaying an annotation of the deviation via an electronic display; The method according to any one of Examples 1 to 8, further comprising: 10. Implant type deviation analysis Interbody cage analysis for annotation of vertebral endplate-based positioning deviations in fusion surgery; Artificial disc analysis for annotation of vertebral endplate-based positioning deviations of articulating vertebral bodies, or Analysis of screw and rod fixation systems for vertebral body-based positioning of fusion surgery. The method according to any one of Examples 1 to 9, comprising at least one of the following: 11. A computer-implemented method for monitoring the positioning of a patient-specific implant, the method comprising: displaying, via a user interface, a patient-specific interactive surgical plan generated by the surgical planning platform based on one or more images of the patient, the patient-specific interactive surgical plan including pre-operatively planned positions of patient-specific implants within the patient to achieve the modified anatomical configuration; acquiring intra-operative image data from one or more imaging devices during a surgical procedure; identifying an intra-operative location of a patient-specific implant within the patient in the intra-operative image data; determining whether the intraoperative position matches the planned position; In response to determining that the intraoperative position does not match the planned position, displaying the intra-operative position and the planned position via a user interface to identify differences between the intra-operative position and the planned position; 20. A computer-implemented method comprising: 12. Comparing the intraoperative image data with the planned anatomical configuration data; determining patient-specific implant location information based on the comparison; and displaying patient-specific implant position information via a user interface to assess the patient-specific implant position; 12. The computer-implemented method of Example 11, further comprising: 13. Location information, patient-specific implant position relative to the planned target position; the patient-specific distance between the implant and anatomical features; A boundary indicating the patient-specific implant target location, or Labeling of patient anatomy adjacent to patient-specific implants 13. The computer-implemented method of any one of Examples 11-12, comprising at least one of: 14. Determining a viewpoint of intraoperative image data; generating, from this viewpoint, one or more reference images of the planned positions of the patient-specific implants; comparing the one or more reference images with the intra-operative image data to identify one or more differences between the intra-operative position and the planned position of the patient-specific implant; 14. The computer-implemented method of any of Examples 11-13, further comprising: 15. Generating one or more images of the planned location of the patient-specific implant that match the intraoperative image of the intraoperative image data; overlaying at least one of the images of the planned positions on the intraoperative image to indicate differences between the intraoperative position and the planned position; 15. The computer-implemented method of any of Examples 11-14, further comprising: 16. The computer-implemented method of any of Examples 11-15, wherein the one or more images of the planned location of the patient-specific implant include one or more images of a virtual three-dimensional model representing the patient's anatomy, an image of the patient generated by an imaging device, and / or a scan of the patient. 17. The surgical planning platform determining one or more measurements of the difference between the intraoperative position and the planned position; displaying, via a user interface, one or more measurements of the difference between the intra-operative position and the planned position; 17. The computer-implemented method of any one of Examples 11 to 16, configured to execute: 18. The method further comprising: displaying a comparison of the patient-specific interactive surgical plan and the intraoperative image data, the comparison comprising: overlaying the intraoperative image data onto a patient-specific interactive surgical plan; creating one or more differences between the intraoperative position and the planned position of the patient-specific implant; 18. The computer-implemented method of any of Examples 11 to 17, comprising: 19. Determining progress indicators for a surgical procedure to attach a patient-specific implant to a patient based on intraoperative position; Displaying a progress indicator of the surgical procedure via a user interface; 19. The computer-implemented method of any of Examples 11-18, further comprising: 20. Selecting reference patient data sets each including one or more pre-operative patient images and one or more intra-operative images; training a machine learning model using the selected reference patient dataset; inputting intraoperative image data of the patient into a trained machine learning model to determine whether the intraoperative position matches the planned position; 20. The computer-implemented method of any of Examples 11-19, further comprising: 21. Overlaying intraoperative image data onto a patient-specific interactive surgical plan; orienting and scaling the intraoperative image data based on the location of one or more anatomical landmarks of the patient; 21. The computer-implemented method of any of Examples 11-20, further comprising: 22. Obtaining one or more preoperative indicators of a given condition; and obtaining one or more post-operative metrics to confirm the validity of the target outcome for a given symptom; 22. The computer-implemented method of any of Examples 11-21, further comprising: 23. A computer-implemented method for monitoring the positioning of an implant, the method comprising: acquiring intra-operative image data from one or more imaging devices during a surgical procedure; identifying an intra-operative location of an implant within the patient in the intra-operative image data; generating a comparison image of the intra-operative position of the implant relative to the planned position of the implant according to the surgical plan; displaying the comparison image via a display device to view the intra-operative position of the implant relative to the planned position of the implant; 20. A computer-implemented method comprising: 24. Determining position information for repositioning the implant to position the implant at a target position according to the surgical plan; Annotating the comparison images with location information; 24. The computer-implemented method of Example 23, further comprising: 25. Synchronizing the planned position image data and the intraoperative image data; generating a comparison image based on the synchronized planned position image data and intraoperative image data; 25. The computer-implemented method of any one of Examples 23-24, further comprising: 26. A computer-implemented method for monitoring the positioning of an implant, the method comprising: acquiring intra-operative image data from one or more imaging devices during a surgical procedure; determining whether the surgical procedure is progressing according to the surgical plan of the procedure; generating deviations from the annotations of the surgical plan to position one or more items being inserted into the patient in response to determining that the surgical procedure is not progressing according to the surgical plan; displaying, via the display, deviations from the surgical plan annotations for viewing by a user; 20. A computer-implemented method comprising: 27. The computer-implemented method of Example 26, further comprising displaying surgical plan annotations superimposed on the intraoperative image of the intraoperative image data, wherein the one or more items include one or more implants and / or surgical instruments. 28. The computer-implemented method of any of Examples 26-27, further comprising displaying a comparison image via the display to view the intraoperative position of the implant relative to the planned position of the implant, wherein the deviation from the annotations of the surgical plan includes one or more measurements, spacing indicators, or labels of incorrect positioning. 29. A computing system comprising: one or more processors; one or more memories storing instructions; Equipped with These instructions, when executed by one or more processors, cause a computing system to perform the process of any one of the methods of Examples 1-28. Computing system. 30. A non-transitory computer-readable medium storing instructions that, when executed by a computing system, cause the computing system to perform the operations of any one of the methods of Examples 1-28.

[0168] Those skilled in the art will recognize that it is common within the art to describe devices and / or processes in the manner set forth herein and then use technical techniques to integrate 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 integrated into a data processing system with a reasonable amount of experimentation. Those skilled in the art will recognize that a typical data processing system typically 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; a computing entity, such as an operating system, drivers, a graphical user interface, and application programs; one or more interaction devices, such as a touchpad or touchscreen; and / or a control system, including feedback loops and control motors (e.g., feedback for detecting position and / or velocity; control motors for moving and / or adjusting components and / or quantities). A typical data processing system may be implemented utilizing any suitable commercially available components, such as components typically found in data computing / communication systems and / or network computing / communication systems.

[0169] The subject matter described herein sometimes shows various components contained within or connected with 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 may be implemented. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively “associated” such that a desired functionality is achieved. Thus, any two components herein that are combined to achieve a particular function may be considered to be “associated” with each other such that the desired functionality is achieved without regard to the architecture or intermediate components. Similarly, any two components so associated may also be considered to be “operably connected” or “operably coupled” to each other to achieve the desired functionality, and any two components so associated may also be considered to be “operably couplable” to each other to achieve the desired functionality. Specific examples of operably couplable components include, but are not limited to, components that are physically connected and / or physically interact, and / or components that can wirelessly interact and / or wirelessly interact, and / or components that logically interact and / or logically interact.

[0170] 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 below. U.S. Patent Application No. 16 / 048,167, filed July 27, 2017, 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, "SYSTEMS AND METHODS FOR MULTI-PLANAR ORTHOPEDIC ALIGNMENT," filed December 1, 2018; U.S. Patent Application No. 16 / 352,699, "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANT FIXATION," filed March 13, 2019; U.S. Patent Application No. 16 / 383,215, "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANT FIXATION," filed April 12, 2019; U.S. Patent Application No. 16 / 569,494, "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANTS," filed September 12, 2019; U.S. Patent Application No. 62 / 773,127, "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANTS," filed November 29, 2018; U.S. Patent Application No. 62 / 928,909, filed October 31, 2019, entitled "SYSTEMS AND METHODS FOR DESIGNING ORTHOPEDIC IMPLANTS BASED ON TISSUE CHARACTERISTICS"; U.S. Patent Application No. 16 / 735,222 (now U.S. Patent No. 10,902,944), 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, "PATIENT-SPECIFIC VERTEBRAL IMPLANTS WITH POSITIONING FEATURES," filed November 20, 2020; U.S. Patent Application No. 17 / 124,822, filed December 17, 2020, entitled "PATIENT-SPECIFIC MEDICAL PROCEDURES AND DEVICES, 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 / 978,746, filed November 1, 2022, entitled "PATIENT-SPECIFIC SPINAL INSTRUMENTS FOR IMPLANTING IMPLANTS AND DECOMPRESSION PROCEDURES"; International Patent Application No. PCT / US2021 / 012065, "PATIENT-SPECIFIC MEDICAL PROCEDURES AND DEVICES, AND ASSOCIATED SYSTEMS AND METHODS," filed January 4, 2021; International Patent Application No. PCT / US22 / 48729, "PATIENT-SPECIFIC ARTHROPLASTY DEVICES AND ASSOCIATED SYSTEMS AND METHODS," filed November 2, 2022; U.S. Patent Application No. 18 / 113,573, filed February 23, 2023, entitled "PATIENT-SPECIFIC IMPLANT DESIGN AND MANUFACTURING SYSTEM WITH A DIGITAL FILING CABINET MANAGER"; U.S. Patent Application No. 17 / 878,633, filed August 1, 2022, entitled "NON-FUNGIBLE TOKEN SYSTEMS AND METHODS FOR STORING AND ACCESSING HEALTHCARE DATA"; U.S. Patent No. 11,806,241, issued November 7, 2023, entitled "SYSTEM FOR MANUFACTURING AND PRE-OPERATIVE INSPECTING OF PATIENT-SPECIFIC IMPLANTS"; 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 No. 11,793,577, issued October 24, 2023, entitled "TECHNIQUES TO MAP THREE-DIMENSIONAL HUMAN ANATOMY DATA TO TWO-DIMENSIONAL HUMAN ANATOMY DATA"; International Patent Application No. PCT / US22 / 48729, "PATIENT-SPECIFIC ARTHROPLASTY DEVICES AND ASSOCIATED SYSTEMS AND METHODS," filed November 2, 2022; U.S. Patent Application No. 18 / 113,573, filed February 23, 2023, entitled "PATIENT-SPECIFIC IMPLANT DESIGN AND MANUFACTURING SYSTEM WITH A DIGITAL FILING CABINET MANAGER"; U.S. Patent Application No. 17 / 878,633, filed August 1, 2022, entitled "NON-FUNGIBLE TOKEN SYSTEMS AND METHODS FOR STORING AND ACCESSING HEALTHCARE DATA"; U.S. Patent No. 11,806,241, issued November 7, 2023, entitled "SYSTEM FOR MANUFACTURING AND PRE-OPERATIVE INSPECTING OF PATIENT-SPECIFIC IMPLANTS"; 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," and U.S. Patent No. 11,793,577, issued October 24, 2023, entitled "TECHNIQUES TO MAP THREE-DIMENSIONAL HUMAN ANATOMY DATA TO TWO-DIMENSIONAL HUMAN ANATOMY DATA."

[0171] All of the patent and application references herein, including the above-identified patents and applications, are incorporated by reference in their entirety. Additionally, the embodiments, features, systems, devices, materials, methods, and techniques described herein may, in an embodiment, be applied to or used in connection with any one or more of the embodiments, features, systems, devices, or other methods.

[0172] The ranges disclosed herein encompass any and all overlaps, subranges, and combinations thereof. Words such as "at most," "at least," "greater than," "less than," and "between" include the recited numbers. Numbers preceded by terms such as "approximately," "about," and "substantially" as used herein include the recited number (e.g., about 10% = 10%) and also represent an amount close to the recited amount that still performs the desired function or achieves the desired result. For example, the terms "approximately," "about," and "substantially" may refer to an amount within less than 10%, less than 5%, less than 1%, less than 0.1%, and less than 0.01% of the recited amount.

[0173] From the foregoing, it will be understood that various embodiments of the present disclosure have been described herein for purposes of illustration, and that various modifications may 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 comprising: (a) acquiring at least one image of an implant located in a patient; (b) identifying the implant in the at least one image; (c) synchronizing the virtual anatomical model and the at least one image; (d) comparing a position of the implant in the patient with a position of a virtual implant in the virtual anatomical model based on the synchronized virtual anatomical model and the at least one image; (e) repeating steps (a)-(d) and determining, based on the comparison, that the implant is positioned in an acceptable location within the patient; A method comprising:

2. synchronizing the virtual anatomical model and the at least one image includes manipulating the virtual anatomical model and / or the at least one image of a pre-operative plan according to a best fit routine; Steps (a)-(d) are repeated using an image of the patient showing the implant in another position; The method of claim 1.

3. 3. The method of claim 2, wherein manipulating the virtual anatomical model and / or the at least one image comprises at least one of zooming, stretching, or rotating the virtual anatomical model to match anatomical features in the virtual anatomical model with corresponding anatomical features in the at least one image.

4. 3. The method of claim 2, wherein manipulating the virtual anatomical model and / or the at least one image comprises at least one of zooming, stretching, or rotating the at least one image to align the at least one image with the virtual anatomical model.

5. The method of claim 1 , further comprising determining the acceptable positions within the patient using an ML engine, the acceptable positions being input by a user.

6. The method of claim 1 , further comprising receiving successive images of the patient and repeatedly performing steps (a) through (d) using respective ones of the images.

7. Retrieving a patient-specific surgical plan for the patient; generating instructions to move the implant toward a target position in the patient-specific surgical plan; causing the instructions to be output intra-operatively to assist in repositioning the implant; The method of claim 1 further comprising:

8. the at least one image is a perspective image; the synchronization includes performing anatomical alignment between the virtual anatomical model and the fluoroscopic image; the allowable position is the maximum allowable distance from the target position of the implant; The method of claim 1.

9. obtaining implant type information for the implant; acquiring intra-operative image data from one or more imaging devices during a surgical procedure; selecting an implant type deviation analysis based on the implant type information; generating deviation annotations for positioning the implant inserted in the patient based on the deviation analysis of the implant type; displaying an annotation of said deviation via an electronic display; The method of claim 1 further comprising:

10. said implant type deviation analysis comprising: Interbody cage analysis for annotation of vertebral endplate-based positioning deviations in fusion surgery; Artificial disc analysis for annotation of vertebral endplate-based positioning deviations of articulating vertebral bodies, or Analysis of screw and rod fixation systems for vertebral body-based positioning of fusion surgery. The method of claim 9 , comprising at least one of:

11. 1. A system comprising: one or more processors; one or more memories storing instructions; wherein the instructions, when executed by the one or more processors, cause the system to: (a) acquiring at least one image of an implant located in a patient; (b) identifying the implant in the at least one image; (c) synchronizing the virtual anatomical model and the at least one image; (d) comparing a position of the implant in the patient with a position of a virtual implant in the virtual anatomical model based on the synchronized virtual anatomical model and the at least one image; repeating steps (a) through (d) and determining based on the comparison that the implant is positioned in an acceptable location within the patient; A system that runs a process including

12. synchronizing the virtual anatomical model and the at least one image includes manipulating the virtual anatomical model and / or the at least one image of a pre-operative plan according to a best fit routine; Steps (a)-(d) are repeated using an image of the patient showing the implant in another position; The system of claim 11.

13. 13. The system of claim 12, wherein manipulating the virtual anatomical model and / or the at least one image comprises at least one of zooming, stretching, or rotating the virtual anatomical model to match anatomical features in the virtual anatomical model with corresponding anatomical features in the at least one image.

14. 13. The system of claim 12, wherein manipulating the virtual anatomical model and / or the at least one image comprises at least one of zooming, stretching, or rotating the at least one image to align the at least one image with the virtual anatomical model.

15. the process further comprising determining the allowable positions within the patient using an ML engine; the acceptable positions are input by a user; The system of claim 11.

16. the process further comprising receiving successive images of the patient and repeatedly performing steps (a) through (d) using respective ones of the images. The system of claim 11.

17. The process comprises: Retrieving a patient-specific surgical plan for the patient; generating instructions to move the implant toward a target position in the patient-specific surgical plan; causing the instructions to be output intra-operatively to assist in repositioning the implant; The system of claim 11 further comprising:

18. the at least one image is a perspective image; the synchronization includes performing anatomical alignment between the virtual anatomical model and the fluoroscopic image; the allowable position being the maximum allowable distance from the target position of the implant; The system of claim 11.

19. The process comprises: obtaining implant type information for the implant; acquiring intra-operative image data from one or more imaging devices during a surgical procedure; selecting an implant type deviation analysis based on the implant type information; generating deviation annotations for positioning the implant inserted in the patient based on the deviation analysis of the implant type; displaying an annotation of said deviation via an electronic display; The system of claim 11 further comprising:

20. said implant type deviation analysis comprising: Interbody cage analysis for annotation of vertebral endplate-based positioning deviations in fusion surgery; Artificial disc analysis for annotation of vertebral endplate-based positioning deviations of articulating vertebral bodies, or Analysis of screw and rod fixation systems for vertebral body-based positioning of fusion surgery.

20. The system of claim 19, comprising at least one of:

21. A non-transitory computer-readable medium storing instructions that, when executed by a computing system, cause the computing system to: (a) acquiring at least one image of an implant located in a patient; (b) identifying the implant in the at least one image; (c) synchronizing the virtual anatomical model and the at least one image; (d) comparing a position of the implant in the patient with a position of a virtual implant in the virtual anatomical model based on the synchronized virtual anatomical model and the at least one image; repeating steps (a) through (d) and determining based on the comparison that the implant is positioned in an acceptable location within the patient; A non-transitory computer-readable medium for causing a computer to perform operations including:

22. synchronizing the virtual anatomical model and the at least one image includes manipulating the virtual anatomical model and / or the at least one image of a pre-operative plan according to a best fit routine; Steps (a) through (d) are repeated using an image of the patient showing the implant in another position; 22. The non-transitory computer-readable medium of claim 21.

23. 23. The non-transitory computer-readable medium of claim 22, wherein manipulating the virtual anatomical model and / or the at least one image comprises at least one of zooming, stretching, or rotating the virtual anatomical model to match anatomical features in the virtual anatomical model with corresponding anatomical features in the at least one image.

24. 23. The non-transitory computer-readable medium of claim 22, wherein manipulating the virtual anatomical model and / or the at least one image comprises at least one of zooming, stretching, or rotating the at least one image to align the at least one image with the virtual anatomical model.

25. The operation is Retrieving a patient-specific surgical plan for the patient; generating instructions to move the implant toward a target position in the patient-specific surgical plan; causing the instructions to be output intra-operatively to assist in repositioning the implant; 22. The non-transitory computer-readable medium of claim 21, further comprising:

26. the at least one image is a perspective image; the synchronization includes performing anatomical alignment between the virtual anatomical model and the fluoroscopic image; the allowable position is the maximum allowable distance from the target position of the implant; 22. The non-transitory computer-readable medium of claim 21.

27. The operation is obtaining implant type information for the implant; acquiring intra-operative image data from one or more imaging devices during a surgical procedure; selecting an implant type deviation analysis based on the implant type information; generating deviation annotations for positioning the implant inserted in the patient based on the deviation analysis of the implant type; displaying an annotation of said deviation via an electronic display; and wherein the implant type deviation analysis further comprises: Interbody cage analysis for annotation of vertebral endplate-based positioning deviations in fusion surgery; Artificial disc analysis for annotation of vertebral endplate-based positioning deviations of articulating vertebral bodies, or Analysis of screw and rod fixation systems for vertebral body-based positioning of fusion surgery.

22. The non-transitory computer-readable medium of claim 21, comprising at least one of:

Images