Multi-stage patient-specific surgical planning, and systems and methods for creating and implementing such planning.

Abstract

This specification describes systems and methods for determining and implementing multi-stage patient-specific surgical plans. Multi-stage treatment plans may include a comprehensive outline of specific surgical interventions performed over a set period (e.g., 5, 10, or 20 years). Surgical interventions can be specifically selected, timed, and sequenced to yield a higher probability of achieving favorable patient outcomes and improved quality of life.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims the priority of U.S. Application No. 18 / 120,979, filed on March 13, 2023, the entire disclosure of which is incorporated herein by reference.

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

Background Art

[0003] In a variety of situations, including spinal surgery, hand surgery, shoulder and elbow surgery, total joint reconstruction (arthroplasty), cranial reconstruction, pediatric orthopedic surgery, foot and heel surgery, musculoskeletal tumors, sports medicine surgery, and orthopedic trauma, surgical procedures for implanting orthopedic implants are used to correct many different diseases. Spinal surgery itself encompasses a variety of procedures and targets, such as one or more of the cervical, thoracic, lumbar, or sacral vertebrae, and may be performed to treat spinal deformities or degenerations and / or related back pain, leg pain, or other body pain. Common spinal deformities that may be treated using orthopedic implants include scoliosis, lordosis, or (high - or low - degree) kyphosis, and irregular spinal curvatures such as spondylolisthesis. Other spinal disorders that can be treated using orthopedic implants include osteoarthritis, lumbar or cervical intervertebral disc degeneration diseases, lumbar stenosis, and cervical stenosis.

Summary of the Invention

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

[0005] [Figure 1] This is a network connection diagram showing a system for providing patient-specific medical care according to an embodiment of this technology. [Figure 2] This figure shows a computing device suitable for use in relation to the system shown in Figure 1, according to an embodiment of this technology. [Figure 3] This flowchart illustrates a method for providing patient-specific medical care according to an embodiment of this technology. [Figure 4] This flowchart illustrates a method for determining a multi-stage patient-specific surgical plan according to an embodiment of this technology. [Figure 5A] This figure shows a typical multi-stage surgical plan generated by an embodiment of this technology. [Figure 5B] This figure shows a typical multi-stage surgical plan generated by an embodiment of this technology. [Figure 6] This flowchart illustrates another method of providing patient-specific medical care according to an embodiment of this technology. [Figure 7A] This figure shows a representative dataset that can be used and / or generated in connection with the method described herein, according to embodiments of this technology. [Figure 7B]This figure shows a representative dataset that can be used and / or generated in connection with the method described herein, according to embodiments of this technology. [Figure 7C] This figure shows a representative dataset that can be used and / or generated in connection with the method described herein, according to embodiments of this technology. [Figure 8] This flowchart illustrates another method of providing patient-specific medical care according to an embodiment of this technology. [Figure 9] This is a partial schematic diagram showing a surgical facility and related computing system for providing patient-specific medical care according to an embodiment of this technology. [Figure 10A] This figure shows a typical patient-specific implant that can be used and / or produced in connection with the method described herein, according to embodiments of this technology. [Figure 10B] This figure shows a typical patient-specific implant that can be used and / or produced in connection with the method described herein, according to embodiments of this technology. [Figure 11] This figure shows the spinal segments of a patient after several patient-specific implants have been placed according to an embodiment of this technology. [Modes for carrying out the invention]

[0006] This technology relates to systems and methods for planning and performing medical procedures and / or devices. For example, in many of the embodiments disclosed herein, a method of providing medical care includes generating a multi-stage patient-specific treatment plan for a patient. The multi-stage treatment plan may include a comprehensive outline of specific surgical interventions to be performed over a set period of time (e.g., 5, 10, 20 years, etc.). The surgical interventions can be specifically selected, timed, and ordered to bring about a higher probability of achieving a favorable patient outcome and an improved quality of life. As an additional benefit, because the multi-stage plan is "prospective," the plan allows patients, caregivers, surgeons, and / or other healthcare providers to better understand the long-term treatment plan and likely prognosis for a particular patient before the first surgical procedure is performed.

[0007] The multi-stage treatment plans described herein may include a first treatment stage comprising a first surgical procedure at a first target site and a second treatment stage comprising a second surgical procedure at a second target site. The second surgical procedure is generally at least partially based on the first surgical procedure and / or vice versa. For example, the second surgical procedure may be less invasive or less interventional than if the patient had chosen not to undergo the first procedure. The second surgical procedure may also be based on the patient's predicted long-term response to the first surgical procedure. The first and second treatment stages are separated in time by a fixed or adaptive period, as detailed below, but typically by at least six months.

[0008] The embodiments of this disclosure will be described in more detail below with reference to the accompanying drawings, where similar figures represent similar elements throughout several drawings, illustrating exemplary embodiments. However, the embodiments of the claims can be implemented in many different forms and should not be construed as being limited to the embodiments described herein. The embodiments described herein are non-limiting embodiments and are merely examples of other possible embodiments.

[0009] The words “comprising,” “having,” “containing,” and “including,” and other forms thereof, are intended to be equivalent in meaning and to be open-ended in that the matters following any one of these terms are not intended to be exhaustive or limited to the items listed.

[0010] As used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context explicitly requires otherwise.

[0011] While the disclosures herein primarily describe systems and methods for treatment planning in the context of orthopedics, the technology is equally applicable to medical devices in other fields (e.g., other types of surgical practice). Furthermore, although many embodiments herein describe systems and methods for implantable devices, the technology is equally applicable to other types of medical devices (e.g., non-implantable devices).

[0012] The headings are for convenience only and should not be used to interpret the scope of this technology.

[0013] A. Selection and embodiment of a system for patient-specific surgical planning and patient-specific implant design. Figure 1 is a network diagram showing a system 100 for providing patient-specific medical care according to an embodiment of the present technology. As detailed herein, system 100 is configured to generate a multi-stage treatment plan for a patient. Depending on the embodiment, system 100 is configured to generate a treatment plan for a patient with an orthopedic or spinal disease or disorder, such as trauma (e.g., fracture), cancer, deformity, degeneration, pain (e.g., back pain, leg pain), abnormal spinal curvature (e.g., scoliosis, lordosis, kyphosis), abnormal spinal displacement (e.g., spondylolisthesis, lateral displacement, axial displacement), osteoarthritis, lumbar disc degeneration, cervical disc degeneration, lumbar or cervical stenosis, or a combination thereof. The treatment plan may include surgical information, technical recommendations (e.g., device and / or equipment recommendations), and / or medical device designs. For example, a multi-stage medical plan may include two or more surgical procedures (e.g., surgical procedures or interventions) performed in a specific order with a specific temporal separation. Each surgical procedure may also include at least one medical device (e.g., an implantable medical device (also referred herein as “implant” or “implantable device”) or implant delivery device) for use with each stage of the multi-stage medical plan. Thus, depending on the embodiment, the treatment plan may also be called a “multi-stage treatment plan,” “multi-stage surgical plan,” “surgical plan,” “patient-specific surgical plan,” “patient-specific treatment plan,” etc.

[0014] Depending on the embodiment, System 100 generates a multi-stage treatment plan customized for a particular patient or group of patients, also referred herein as a “patient-specific” or “personalized” treatment or surgical plan. A patient-specific surgical plan may include at least one patient-specific surgical procedure and / or at least one patient-specific medical device designed and / or optimized for the patient’s specific characteristics (e.g., condition, anatomical structure, pathology, state, medical history). For example, a patient-specific medical device is not a ready-made device but can be specifically designed and manufactured for a particular patient. However, it should be understood that a patient-specific surgical plan may also include embodiments that are not customized for a particular patient. For example, a patient-specific or personalized surgical procedure may include one or more instructions, parts, steps, etc. that are not patient-specific. Similarly, a patient-specific or personalized medical device may include one or more components that are not patient-specific and / or can be used with non-patient-specific instruments or devices. Personalized implant designs can be used for the manufacture or selection of patient-specific technologies, including medical devices, instruments, and / or surgical kits. For example, a personalized surgical kit may include one or more patient-specific devices, patient-specific instruments, non-patient-specific technologies (e.g., standard instruments, devices, etc.), instructions for use, patient-specific treatment plan information, or a combination thereof.

[0015] 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 known in the art. As detailed herein, the client computing device 102 may include one or more processors and memory for storing instructions that can be executed by the one or more processors to perform the methods described herein. The client computing device 102 may be associated with a healthcare provider treating a patient (e.g., a surgeon, medical administrator, hospital system, etc.). Figure 1 shows a single client computing device 102, but in another embodiment, the client computing device 102 may be implemented as a client computing system that instead includes multiple computing devices, so that the operations described herein with respect to the client computing device 102 can instead be performed by a client computing system and / or multiple client computing devices.

[0016] The client computing device 102 is configured to receive a patient dataset 108 associated with a patient being treated. The patient dataset 108 can include data representing the patient's condition, anatomical structure, pathology, medical history, preferences, and / or any other information or parameters related to the patient. For example, the patient dataset 108 can include medical history, surgical intervention data, treatment prognosis data, progress data (e.g., physician notes), patient feedback (e.g., feedback obtained using quality of life questionnaires, surveys), clinical data, provider information (e.g., physicians, hospitals, surgical teams), patient information (e.g., demographic attributes, gender, age, height, weight, type of pathology, occupation, activity level, tissue information, health score, comorbidities, health-related quality of life (HRQL)), vital signs, diagnostic results, drug information, allergies, image data (e.g., camera images, magnetic resonance imaging (MRI) images, ultrasound images, computed tomography (CAT) scan images, positron emission tomography (PET) images, X-ray images), diagnostic device information (e.g., manufacturer, model number, specifications, user-selected settings / configurations, etc.). In some embodiments, the patient dataset 108 includes data representing one or more of a patient identification number (ID), age, gender, body mass index (BMI), lumbar lordosis, Cobb angle, pelvic incidence angle, disc height, segmental flexibility, bone quality, rotational displacement, and / or treatment level of the spine.

[0017] The client computing device 102 is also configured to enable a user (e.g., a surgeon) to review one or more multi - stage surgical plans of a patient to be treated. Specifically, the client computing device 102 can include a surgical plan review software module 123 (the "review module 123"). The review module 123 can include computer - executable instructions for generating, displaying, and / or implementing a surgical plan review program or platform 125 (the "review program 125") that facilitates review by the surgeon or user of one or more patient - specific multi - stage surgical plans via the client computing device 102.

[0018] The review module 123 can be stored in the memory (not shown) of the client computing device 102 in the form of computer - readable or computer - executable instructions. In other embodiments, the review module 123 can be stored remotely from the client computing device 102 (e.g., in the cloud or on a remote server) and can be implemented on the client computing device 102 via a remote (e.g., wireless) connection. In yet other embodiments, a portion of the review module 123 can be stored locally on the client computing device 102 and other portions of the review module 123 can be stored remotely.

[0019] The client computing device 102 is operationally connected to the server 106 via a communication network 104, thus enabling data transfer between the client computing device 102 and the server 106. The communication network 104 may be a wired and / or wireless network. In the case of wireless, the communication network 104 can be implemented using communication technologies such as visible light communication (VLC), worldwide interoperability for microwave access (WiMAX), long-term evolution (LTE), wireless local area network (WLAN), infrared (IR) communication, public switched telephone network (PSTN), radio waves, and / or other communication technologies known in the art.

[0020] Server 106, sometimes also called a “therapy support network” or “prescription 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 for storing instructions that can be executed by one or more processors to perform some or all of the methods described herein. Depending on the embodiment, Server 106 may be implemented as a distributed “cloud” computing system or mechanism across any suitable combination of hardware and / or virtual computing resources.

[0021] The client computing device 102 and the server 106 can individually or jointly perform some or all of the various methods described herein for providing patient-specific medical care. For example, some or all of the steps of the methods described herein can be performed by the client computing device 102 alone, by the server 106 alone, or in combination with the client computing device 102 and the server 106. Therefore, while certain operations are described herein in relation to the server 106, it should be understood that these operations can also be performed by the client computing device 102, and vice versa, unless the context requires a different interpretation.

[0022] The server 106 includes at least one database 110 configured to store useful reference data for the treatment planning method described herein. The reference data may include historical and / or clinical data from the same patient or other patients, data collected from the patient's previous surgical and / or other treatments by the same healthcare provider or other healthcare providers, data relating to medical device design, data collected from research groups or survey groups, data from clinical databases, data from academic institutions, data from implant manufacturers or other medical device manufacturers, data from image analysis, data from simulations, clinical trials, demographic data, treatment data, prognosis data, mortality data, etc.

[0023] In some embodiments, database 110 includes multiple reference patient datasets, each patient reference dataset associated with a corresponding reference patient. For example, a reference patient may be a patient who has previously received treatment or a patient currently receiving treatment. Each reference patient dataset may include data representing the corresponding reference patient's status, anatomical structure, pathology, medical history, disease progression, preferences, and / or any other information or parameters related to the reference patient, such as any of the data described herein with respect to patient dataset 108. In some embodiments, a reference patient dataset may include preoperative data, intraoperative data, and / or postoperative data. For example, a reference patient dataset may include data representing one or more of the following: patient ID, age, gender, BMI, lumbar lordosis, Cobb angle, pelvic intrinsic angle, intervertebral disc height, segmental flexibility, bone quality, rotational displacement, and / or spinal treatment level.

[0024] In another embodiment, the reference patient dataset may include treatment data relating to at least one surgical procedure performed on the reference patient, including a description of the surgical procedure or intervention (e.g., surgical technique, bone resection, surgical procedure, reduction procedure, implantation or other device placement). The treatment data may further include whether any additional surgical procedures or interventions were required following the initial surgical procedure, what those additional procedures or interventions were, the timing of the additional procedures or interventions, and the scored prognosis of the additional procedures or interventions. The treatment data may also include medical device design data for at least one medical device used to treat the reference patient (e.g., a medical device implanted at the time of the surgical procedure or intervention), including physical properties (e.g., size, shape, volume, material, mass, weight), mechanical properties (e.g., stiffness, strength, coefficient, hardness), and / or biological properties (e.g., bone integration, cell adhesion, antimicrobial properties, antiviral properties). The reference patient dataset may also include prognostic data representing the outcome of treatment for the reference patients, such as reduction anatomical indicators, presence of fusion, HRQL, pain level, activity level, return to work, complications, recovery time, efficacy, mortality, and / or follow-up surgery.

[0025] Depending on the embodiment, server 106 receives at least a portion of reference patient datasets from multiple healthcare provider computing systems (e.g., systems 112a to 112c, collectively 112). Server 106 is connectable to the healthcare provider computing systems 112 via one or more communication networks (not shown). Each healthcare provider computing system 112 can be associated with a corresponding healthcare provider (e.g., a physician, 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 to 114c, collectively 114) associated with reference patients treated by the corresponding healthcare provider. The reference patient datasets 114 may include, for example, electronic medical records, electronic health records, biomedical datasets, etc. The reference patient datasets 114 are receivable by server 106 from the healthcare provider computing systems 112 and can be reformatted to different formats for storage in database 110. Optionally, the reference patient dataset 114 may be processed (e.g., cleaned) to ensure that the expressed patient parameters are more likely to be useful in the treatment planning method described herein.

[0026] As detailed herein, the server 106 can be configured to include one or more algorithms that generate multi-stage patient-specific surgical planning data (e.g., treatment procedures, target anatomical reductions, medical devices, etc.) based on reference data. Depending on the embodiment, the patient-specific data is generated based on the interrelationship between the patient dataset 108 and the reference data. Optionally, the server 106 can predict prognosis, including recovery time, efficacy based on clinical endpoints, likelihood of success, predicted mortality, and predicted follow-up surgeries. Depending on the embodiment, the server 106 can continuously or periodically analyze patient data (including patient data obtained during patient stay) to determine near real-time or real-time risk scores, mortality predictions, etc.

[0027] Depending on the embodiment, the server 106 includes one or more modules for performing one or more steps of the patient-specific treatment planning method described herein. For example, in the illustrated embodiment, the server 106 includes a data analysis module 116, a treatment planning module 118, a disease progression module 120, and an intervention timing module 121. In another embodiment, one or more of these modules may be combined with each other or omitted. Thus, while certain operations are described herein in relation to a particular one or more modules, this is not intended to be limiting, and in another embodiment, such operations may be performed by one or more different modules.

[0028] The data analysis module 116 comprises one or more algorithms for identifying a subset of reference data from database 110 that is likely to be useful in formulating patient-specific treatment plans. For example, the data analysis module 116 can compare patient-specific data (e.g., patient dataset 108 received from client computing device 102) with reference data from database 110 (e.g., reference patient dataset) to identify similar data (e.g., one or more similar patient datasets within a reference patient dataset). The comparison can be based on one or more parameters such as age, gender, BMI, lumbar lordosis, pelvic intrinsic angle, and / or treatment level. The parameters can be used to calculate a similarity score for each reference patient. The similarity score can represent the statistical relationship between patient dataset 108 and the reference patient dataset. Thus, similar patients can be identified based on whether the similarity score is above, below, or at a predetermined threshold. For example, as detailed below, the comparison can be performed by substituting values ​​for each parameter and calculating the total difference between the patient and each reference patient. Reference patients whose total difference is below the threshold can be considered similar patients.

[0029] The data analysis module 116 may further comprise one or more algorithms for selecting a subset of reference patient datasets based, for example, on similarity to patient dataset 108 and / or the 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 similar patient datasets based on whether or not those similar patient datasets contain data indicating a favorable or desired treatment outcome. Prognostic data may include data representing one or more prognostic parameters such as reduction anatomical indicators, presence of fusion, HRQL, activity level, complications, recovery time, efficacy, mortality, or follow-up surgery. In some embodiments, as will be described in more detail below, the data analysis module 116 calculates a prognosis score by substituting values ​​for each prognosis parameter. A patient may be considered to have a favorable prognosis if their prognosis score is above, below, or at a predetermined threshold.

[0030] In some embodiments, the data analysis module 116 selects a subset of reference patient datasets based at least in part on user input (e.g., from clinicians, surgeons, physicians, or healthcare providers). For example, user input can be used to identify similar patient datasets. In some embodiments, it is possible for a healthcare provider or physician to select similarity weights and / or prognosis parameters to adjust similarity scores and / or prognosis scores based on clinician input. In further embodiments, a healthcare provider or physician can select (or define) a set of similarity parameters and / or prognosis parameters to be used to generate similarity scores and / or prognosis scores, respectively.

[0031] Depending on the embodiment, the data analysis module 116 may include 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 healthcare provider parameters (e.g., based on healthcare provider rankings / scores such as hospital / physician expertise, number of procedures performed, hospital rankings) and / or medical resource parameters (e.g., surgical equipment such as diagnostic devices, facilities, and surgical robots), or other non-patient-related information that can be used to predict the prognosis and risk profile of current healthcare provider procedures. For example, reference patient datasets with images recorded from similar diagnostic devices may be aggregated to reduce or limit irregularities resulting from differences between diagnostic devices. Furthermore, data from similar healthcare providers (e.g., healthcare providers with conventionally similar prognoses, physician expertise, surgical teams, etc.) may be used to develop patient-specific treatment plans for a particular healthcare provider. Depending on the embodiment, reference healthcare provider datasets, hospital datasets, physician datasets, surgical team datasets, post-treatment datasets, and other datasets may be used. For example, a patient-specific surgical plan for performing surgery in a battlefield can be based on reference patient data from similar battlefield surgeries and / or datasets associated with battlefield surgeries. In another embodiment, a patient-specific surgical plan can be generated based on an available robotic surgical system. The reference patient dataset can be selected based on patients who have undergone surgery using a comparable robotic surgical system under similar conditions (e.g., size and capabilities of the surgical team, hospital resources, etc.).

[0032] The treatment planning module 118 comprises one or more algorithms that generate at least one surgical plan (e.g., a preoperative plan, an intraoperative plan, a postoperative plan, etc.) based on the output from the data analysis module 116. In some embodiments, the treatment planning module 118 is configured to create and / or implement at least one predictive model for generating a patient-specific treatment plan, also known as a “prescription model”. The predictive model can be created using clinical knowledge, statistics, machine learning, AI, neural networks, etc. In some embodiments, the output from the data analysis module 116 is analyzed (e.g., using statistics, machine learning, neural networks, AI) to identify interrelationships between datasets, patient parameters, healthcare provider parameters, medical resource parameters, treatment procedures, medical device designs, and / or treatment outcomes. These interrelationships can be used to create at least one predictive model that predicts the likelihood that a surgical plan will result in a favorable outcome for that particular patient. The predictive model can be validated, for example, by inputting data into the model and comparing the model’s output to the expected output.

[0033] In some embodiments, the treatment planning module 118 is configured to generate a surgical plan based on previous treatment data from a reference patient. For example, the treatment planning module 118 may receive a selected subset of reference patient datasets and / or similar patient datasets from the analysis module 116 and determine or identify treatment data from the selected subset. Treatment data may 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 the corresponding patient's preferred or desired treatment outcome. The treatment planning module 118 may analyze the treatment procedure data and / or medical device design data to determine the optimal treatment protocol for the patient being treated. For example, values ​​may be assigned to treatment procedures and / or medical device designs and aggregated to produce a treatment score. A multi-stage patient-specific surgical plan can be determined by selecting one or more surgical plans based on the score (e.g., higher or highest score, lower or lowest score, score above or below a specified threshold, or score at or near a specified threshold). This personalized patient-specific surgical plan may be based, at least in part, on patient-specific techniques or patient-specific selection techniques.

[0034] Alternatively, or in combination with the above, the treatment planning module 118 can generate surgical plans based on interrelationships between datasets. For example, the treatment planning module 118 can correlate treatment procedure data and / or medical device design data from similar patients with favorable outcomes (identified, for example, by the data analysis module 116). Interrelationship analysis may include converting interrelationship coefficient values ​​into values ​​or scores. These values / scores can be analyzed by aggregation, filtering, or otherwise to determine one or more statistical significances. These interrelationships can be used to determine the optimal surgical procedure and / or medical device design that is most likely to result in a favorable outcome for the treated patient.

[0035] Alternatively, or in combination with the above, the treatment planning module 118 may generate surgical plans using one or more AI technologies. AI technologies 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 technologies may include, but are not limited to, case-based reasoning, rule-based systems, artificial neural networks, decision trees, support vector machines, regression analysis, Bayesian networks (e.g., naive Bayes classifiers), genetic algorithms, cellular automata, fuzzy logic systems, multi-agent systems, swarm intelligence, data mining, machine learning (e.g., supervised learning, unsupervised learning, reinforcement learning), and hybrid systems.

[0036] In some embodiments, the treatment planning module 118 generates surgical plans using one or more trained machine learning models. Various types of machine learning models, algorithms, and techniques are suitable for use with this technique. 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 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).

[0037] In some embodiments, a machine learning model (e.g., a neural network or a Naive Bayes classifier) ​​can be trained on a training dataset using a supervised learning method (e.g., gradient descent or stochastic gradient descent). The training dataset can contain pairs of generated "input vectors" and their associated corresponding "response vectors" (generally referred to as targets). The current model is run on the training dataset, producing 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 specific learning algorithm used, the model parameters are adjusted. Model fitting can include both variable selection and parameter estimation. The fitted model can be used to predict observed responses on a second dataset called a validation dataset. The validation dataset can provide an unbiased assessment of the model fit on the training dataset when tuning the model parameters. The validation dataset can also be used for regularization by stopping training early, for example, if the error increases on the validation dataset, as the increase in error is an indication of overfitting to the training dataset. Depending on the embodiment, the error in the validation dataset may fluctuate during training; therefore, flexible rules may be used to determine when overfitting truly begins. Finally, the test dataset can be used to give an unbiased assessment of the final model fit on the training data.

[0038] To generate surgical plans, a patient dataset 108 can 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, can also be input into the trained machine learning model. The trained machine learning model can then calculate whether various candidate treatment procedures and / or medical device designs are likely to result in a favorable outcome for the patient. Based on these calculations, the trained machine learning model can select at least one surgical plan for the patient. Depending on the embodiment, the trained machine learning model can determine candidate procedures (or candidate surgical plans), analyze the candidate procedures, select a candidate surgical plan or a part thereof, score the plans, and / or generate a surgical plan for the patient. Each surgical plan can be scored (e.g., scored based on favorable outcomes, probability of outcome, etc.) and ranked according to its score. The trained machine learning model can determine a set of surgical plans that meet the selection criteria for a user's plan review. Selection criteria can be based on, for example, regulatory requirements, reimbursement criteria, healthcare / provider expertise, available surgical devices, manufacturing capabilities, exclusion criteria, or a combination thereof. The user can input one or more selection criteria to control the types and / or features of surgical plans for comparison. In embodiments where multiple trained machine learning models are used, the models can be run sequentially or in parallel to compare results and can be periodically updated using the training dataset. The treatment planning module 118 can use one or more machine learning models based on the predictive accuracy scores of the models. The treatment planning module 118 can be periodically or sequentially retrained using patient data such as postoperative image data and physician notes. For example, the treatment planning module 118 can be retrained after the completion of each stage of the treatment plan.

[0039] The patient-specific surgical plan generated by the treatment planning module 118 may include a multi-stage surgical plan. For example, as detailed below, the multi-stage plan described herein may include a first treatment stage including a first surgical procedure at a first target site and a second treatment stage including a second surgical procedure at a second target site. The first surgical procedure is generally at least partially based on and / or vice versa. For example, the second surgical procedure may be less invasive or less interventional compared to the patient choosing not to undergo the first procedure. The first and second treatment stages are separated in time by a fixed or adaptive period, as detailed below, but typically by at least six months. Both the first and second surgical procedures may include at least one patient-specific medical device (e.g., an implant or implant delivery device) that is implanted at the time of the corresponding surgical procedure and / or used to perform the corresponding surgical procedure. The patient-specific surgical plan may include the entire surgical procedure or a portion thereof. Furthermore, one or more patient-specific medical devices can be specifically selected or designed for corresponding surgical procedures, thereby enabling the combined use of various components of patient-specific technology to treat the patient.

[0040] Depending on the embodiment, patient-specific surgical procedures (e.g., the first and / or second surgical procedures in a multi-stage surgical plan) include orthopedic procedures such as spinal surgery, hip surgery, knee surgery, jaw surgery, hand surgery, shoulder surgery, elbow surgery, joint reconstruction (arthroplasty), cranial reconstruction, foot surgery, or ankle surgery. Spinal surgeries may include spinal fusion procedures such as posterior lumbar interbody fusion (PLIF), anterior lumbar interbody fusion (ALIF), transverse or transforaminal lumbar interbody fusion (TLIF), lateral lumbar interbody fusion (LLIF), direct lateral lumbar interbody fusion (DLIF), or lateral approach lumbar interbody fusion (XLIF). Depending on the embodiment, patient-specific therapeutic procedures include instructions and / or directions for performing one or more embodiments of patient-specific surgical procedures. For example, patient-specific surgical procedures may include one or more of surgical techniques, reduction techniques, osteotomies, or implant placements.

[0041] Depending on the embodiment, patient-specific medical device design includes the design of orthopedic implants and / or instruments for delivering orthopedic implants. Examples of such implants include, but are not limited to, screws (e.g., bone screws, spinal screws, pedicle screws, intervertebral screws), interbody implant devices (e.g., interbody implants), cages, plates, rods, discs, fusion devices, spacers, rods, expandable devices, stents, brackets, lashing devices, scaffolds, fixation devices, anchors, nuts, bolts, rivets, connectors, tethers, fasteners, joint replacement devices, hip implants, etc. Examples of instruments include, but are not limited to, screw guides, cannulas, ports, catheters, insertion devices, etc.

[0042] 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, coefficient, hardness), and / or biological properties (e.g., bone integration, cell adhesion, antimicrobial properties, antiviral properties) of the corresponding medical device. For example, the design of an orthopedic implant may include the shape, size, material, and / or effective stiffness (e.g., lattice density, number of struts, strut placement, etc.) of the implant. Depending on the embodiment, the resulting patient-specific medical device design may be the design of the entire device. Alternatively, the resulting design may be the design of one or more components of the device, rather than the entire device.

[0043] In some embodiments, the design is the design of one or more patient-specific device components that can be used with standard off-the-shelf components. For example, in spinal surgery, a pedicle screw kit may include both standard components and patient-specific, customized components. In some embodiments, the resulting design is the design of a patient-specific medical device that can be used with standard off-the-shelf delivery instruments. For example, an implant (e.g., a screw, screw holder, rod) can be designed and manufactured for that patient, while the instrument for delivering the plant can be a standard instrument. This approach allows the implanted component to be designed and manufactured based on the patient's anatomical structure and / or the surgeon's preference for enhancing the 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 anatomical structure.

[0044] In embodiments where the patient-specific surgical plan includes a specific surgical procedure for implanting a medical device, the treatment planning module 118 can also store various types of implant surgery information, such as implant parameters (e.g., type, dimensions), implant availability, aspects of the preoperative plan (e.g., initial implant configuration, detection and measurement of the patient's anatomical structure, etc.), and FDA requirements for the implant (e.g., specific implant parameters and / or characteristics for compliance with FDA regulations). Depending on the embodiment, the treatment planning module 118 can 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 specific identifiers for mathematical formulas or converted into numerical representations suitable for feeding into trained machine learning models. The treatment planning module 118 can also store information about the patient's anatomical structure, such as two-dimensional or three-dimensional images or models of the anatomical structure, and / or information about the biological, geometric, and / or mechanical properties of the anatomical structure. The anatomical information can be used to communicate implant design and / or placement.

[0045] The disease progression module 120 can be used to analyze, predict, and / or model disease progression in a particular patient. As detailed below, the disease progression module 120 can estimate the rate of disease progression in a patient under a variety of different circumstances, including (a) when no surgical intervention is performed and (b) when a surgical plan (e.g., a surgical procedure identified by the treatment planning module 118) is performed. Thus, the disease progression module 120 may include algorithms, machine learning models, or other software analysis tools for predicting disease progression in a particular patient.

[0046] Depending on the embodiment, the disease progression module 120 includes a machine learning model or other software module that can be trained on multiple reference patient datasets, which include disease progression assessment indicators for each reference patient in addition to the patient data described above. The progression assessment indicators may include measurements of disease assessment indicators over a period of time. Appropriate assessment indicators may include spinal-pelvic parameters (e.g., lumbar lordosis, pelvic tilt, sagittal plane vertical axis (SVA), Cobb angle, coronal plane offset, etc.), disability scores, functional ability scores, flexibility scores, VAS pain scores, etc. The progression of each assessment indicator for each reference patient can be related to other patient information for that particular reference patient (e.g., age, sex, height, weight, activity level, diet, etc.). These disease assessment indicators may include values ​​over a period of time. For example, the reference patient data may include values ​​of disease assessment indicators at daily, weekly, monthly, bimonthly, yearly, or other frequencies. By measuring the assessment indicators over a period of time, changes in the values ​​of the assessment indicators can be tracked as estimates of disease progression and related to other patient data. Similarly, the need for additional surgical procedures and their timing can be predicted.

[0047] In some embodiments, the disease progression module 120 can therefore estimate the rate of disease progression for a particular patient. Progression can be estimated by providing an estimated change in one or more disease assessment indicators over a period of time (e.g., an increase of X% per year in the disease assessment indicator). The rate may be constant (e.g., a 5% increase in pelvic tilt per year) or variable (e.g., a 5% increase in pelvic tilt in the first year, a 10% increase in pelvic tilt in the second year, etc.). In some embodiments, the estimated rate of progression can be transmitted to the surgeon or other healthcare provider as part of the surgical plan, as detailed below. The estimated rate of progression can also be used to determine the timing of a first surgical procedure and / or a second surgical procedure, also as detailed below.

[0048] As a non-limiting example, a particular patient, a 55-year-old male, may have an SVA value of 6 mm. The disease progression module 120 can analyze the patient reference dataset to identify disease progression in individual reference patients who share one or more similarities with this particular patient (e.g., individual patients among the reference patients who have an SVA value of approximately 6 mm and are roughly the same age, weight, height, and / or sex as the patient). Based on this analysis, the disease progression module 120 can predict the rate of disease progression without surgical intervention (e.g., the patient's VAS pain score may increase by 5%, 10%, or 15% per year without surgical intervention, or the SVA value may continue to increase by 5% per year without surgical intervention). The disease progression module 120 can also predict the rate of disease progression or the onset of further conditions if a first surgical procedure is performed (e.g., the patient's VAS pain score may remain constant for 5 years after surgery and then increase by 5% per year without a second surgical procedure).

[0049] Therefore, as detailed in Section B below, the multi-stage surgical treatment plans and / or associated patient-specific implants described herein can be based at least in part on the estimated rate of disease progression, thereby enabling the modeling of different prognoses over a desired period of time. Furthermore, the model / simulation can take into account any number of additional diseases or conditions to predict the patient's overall health, mobility, etc. These additional diseases or conditions can be used in combination with other patient health factors (e.g., height, weight, age, activity level, etc.) to generate a patient health score that reflects the patient's overall health. The patient health score can be displayed for surgeon review and / or incorporated into the estimation of disease progression. Thus, the technology can generate one or more virtual simulations of predicted disease progression to show how the patient's anatomical structure is expected to change over time. Physician input can be used to generate or modify the virtual simulations. The technology can generate one or more post-treatment virtual simulations based on physician input received for review by healthcare providers, patients, etc.

[0050] Depending on the embodiment, the technology may also predict, model, and / or simulate disease progression based on one or more possible surgical plans, including multi-stage surgical plans. For example, the disease progression module 120 can simulate how a patient's anatomical structure and / or spinal assessment indices will change at 1, 2, 5, or 10 years postoperatively for several different surgical plans. The simulation may also incorporate non-surgical factors such as the patient's age, height, weight, sex, activity level, and other health conditions, as described above. The system and / or surgeon can use this disease progression to help select which surgical plan will yield the best long-term effectiveness, as described below. These simulations can also be used to determine patient-specific reductions that compensate for predicted disease progression.

[0051] Therefore, depending on the embodiment, multiple (e.g., two, three, four, five, six, or more) disease progression models are simulated to provide disease progression data for several different multi-stage surgical plans. For example, the disease progression module can generate models that predict postoperative disease progression for each of three different multi-stage surgical plans. The surgeon or other healthcare provider can review the disease progression models and, based on that review, select which of the three multi-stage surgical plans is most likely to provide the patient with the best long-term prognosis.

[0052] Based on modeled disease progression, the systems and methods described herein can also (i) identify recommended timing for surgical intervention and / or (ii) identify recommended types of surgical procedures for a patient. Depending on the embodiment, the technique therefore includes an intervention timing module 121, which includes an algorithm, machine learning model, or other software analysis tool for determining the optimal timing of surgical intervention in a particular patient. This can be done, for example, by analyzing patient reference data including (i) preoperative disease progression assessment indices for individual reference patients, (ii) disease status assessment indices at the time of surgical intervention for individual reference patients, (iii) postoperative disease progression assessment indices for individual reference patients, and / or (iv) scored surgical outcomes for individual reference patients. The intervention timing module 121 can compare the disease status assessment indices of a particular patient with the reference patient dataset to determine the point of disease progression at which surgical intervention yielded the most favorable outcome for similar patients. For a multi-stage surgical plan including a first surgical procedure and a second surgical procedure, the intervention timing module 121 can also identify the point in disease progression at which the first surgical procedure should be performed and the point in disease progression at which the second surgical procedure should be performed.

[0053] As a non-limiting example, the reference patient dataset may include data associated with the reference patient's sagittal vertical axis. This data may include (i) the individual patient's sagittal vertical axis values ​​over a period prior to the surgical intervention (e.g., how quickly and to what extent the sagittal vertical axis values ​​changed), (ii) the individual patient's sagittal vertical axis at the time of the first surgical procedure, (iii) the change in the sagittal vertical axis after the first surgical procedure, (iv) the degree of success of the first surgical procedure (e.g., based on pain, quality of life, or other factors), (v) the length of time between the first and second surgical procedures, if necessary, and (vi) the degree of success of the second surgical procedure. Based on the above data, the intervention timing module 121 can identify, based on the sagittal vertical axis values ​​of a particular patient, at what point in time the first surgical procedure is most likely to yield the most favorable outcome, and can predict when a second surgical procedure will be necessary and / or when it may improve the patient's long-term prognosis. Naturally, the above evaluation indicators are provided for illustrative purposes only, and the intervention timing module 121 may incorporate other evaluation indicators (e.g., lumbar lordosis, pelvic tilt, sagittal plane vertical axis, Cobb angle, coronal plane offset, disability score, functional ability score, flexibility score, VAS pain score) instead of or in combination with the sagittal plane vertical axis value to predict the point in time when a surgical intervention has the highest probability of resulting in a favorable outcome for that particular patient.

[0054] The intervention timing module 121 may also incorporate one or more mathematical rules based on threshold values ​​for various disease assessment indicators. For example, the intervention timing module 121 may indicate that surgical intervention is necessary if one or more disease assessment indicators exceed a predetermined threshold or if any other criteria are met. Typical thresholds indicating the possibility of surgical intervention include an SVA value greater than 7 mm, a discrepancy of more than 10 degrees between lumbar lordosis and pelvic intrinsic angle, a Cobb angle greater than 10 degrees, and / or a combination of a Cobb angle greater than 20 degrees and an LL / PI discrepancy. Naturally, other thresholds and assessment indicators can be used, and the above are merely examples. Depending on the embodiment, the above rules can be adjusted to suit a specific patient population (for example, for men over 50 years of age, 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 121 may provide the patient with an estimate of when the patient's assessment indicators exceed one or more thresholds, thereby providing the patient with an estimate of when surgical intervention may become recommended.

[0055] In some embodiments, the treatment planning module 118 identifies one or more types of surgical procedures for a patient, at least in part, based on the patient's disease progression as determined using the disease progression module 120 and / or the intervention timing module 121. The treatment planning module 118 may also incorporate one or more mathematical rules for identifying surgical procedures. In a non-limiting embodiment, if the LL / PI mismatch is between 10 and 20 degrees, the treatment planning module 118 may recommend anterior fixation, and if the LL / PI mismatch is greater than 20 degrees, the treatment planning module may recommend both anterior and posterior fixation. In another non-limiting embodiment, if the SVA value is between 7 mm and 15 mm, the treatment planning module may recommend posterior fixation, and if the SVA is greater than 15 mm, the treatment planning module may recommend both posterior and anterior fixation. Naturally, other rules are also available, and the above are merely examples.

[0056] While not bound by theory, incorporating disease progression modeling into patient-specific surgical plans as described herein can further improve the effectiveness of procedures and / or provide surgeons with more data to evaluate various surgical plans. For example, in many cases, it may be disadvantageous to perform surgery after a patient's condition has progressed to an irreversible or unstable state. However, it may also be disadvantageous to perform surgery too early, such as before the patient's disease causes symptoms and / or when the patient's disease is unlikely to progress further. Therefore, the disease progression module 120 and / or the intervention timing module 121 can make it easier to identify the time window in a particular patient where a surgical intervention is most likely to result in a favorable prognosis for that patient. The disease progression module 120 and / or the intervention timing module 121 can also help predict when a second surgical intervention will be needed if a first surgical intervention has been performed. In this way, the disease progression module 120 and / or the intervention timing module 121 can provide a holistic overview of the likely prognosis if a given surgical plan is performed.

[0057] The surgical plan generated by the treatment planning module 118 can be transmitted to a client computing device 102 via the communication network 104 for output to a user (e.g., clinician, surgeon, healthcare provider, patient). Depending on the embodiment, the client computing device 102 may include a display 122 for outputting the treatment plan, or may be operably coupled to the display 122. The display 122 may include a graphical user interface (GUI) for visually illustrating various aspects of the surgical plan. For example, the display 122 may show various aspects of the surgical procedure performed on the patient, such as surgical technique, treatment level, reduction technique, tissue resection and / or implant placement. To facilitate visualization, the surgical plan may include a virtual model of the surgical procedure that can be displayed via the display 122. Display 122 may also display additional aspects of the surgical plan, such as a first surgical procedure, predicted postoperative patient evaluation indicators after the first surgical procedure, predicted disease progression evaluation indicators after the first surgical procedure, a second surgical procedure, predicted postoperative patient evaluation indicators after the second surgical procedure, and predicted disease progression evaluation indicators after the second surgical procedure. In another embodiment, display 122 may show the design of a medical device to be implanted in the patient according to the transmitted surgical plan, such as a two-dimensional or three-dimensional model of the device design. Display 122 may also show patient information, such as a two-dimensional or three-dimensional image or model of the patient's anatomical structure where the surgical procedure is performed and / or the device is implanted.

[0058] Depending on the embodiment, one or more aspects of a surgical plan are displayed using a surgical plan review program 125. For example, the review program 125, which may be implemented as a mobile phone application, can display one or more aspects of a surgical plan (e.g., a first surgical procedure, a second surgical procedure, a virtual model of the patient's anatomical structure, implants, etc.) (e.g., via a display 122). The review program 125 may further provide an interactive interface that allows the surgeon to select from different patients, select from different surgical plans for the same patient, compare surgical plans for the same patient, review the status of the surgical plans, provide feedback on the proposed surgical plans, accept the surgical plans, reject the surgical plans, and so on. The review program 125 may also further allow the surgeon or other user to select from different views of a virtual model of the patient's anatomical structure and / or different views of patient-specific implants used in the surgical plan.

[0059] A surgical plan may include one or more images, such as a virtual model or a viewable virtual model. The virtual model can be a multidimensional (e.g., 2D or 3D) virtual model and may include, for example, computer-aided design (CAD) data, material data, surface modeling, and manufacturing data. CAD data may include, for example, 3D modeling data (e.g., part files, assembly files, libraries, part / object identifiers, etc.), model geometry, object representation, parameter data, object representation, topology data, surface data, assembly data, and metadata. The system can generate instruments using a predicted intraoperative anatomical model, a postoperative or reduction anatomical model, a surgical plan (e.g., a single-stage or multi-stage surgical plan), a virtual model of an implant, implant design parameters, and a virtual model of the patient's anatomical structure. The above examples are U.S. Patent No. 11,793,577 and U.S. Patent Applications No. 16 / 048,167, 16 / 242,877, 16 / 207,116, 16 / 352,699, 16 / 383,215, 16 / 569,494, 16 / 699,447, 16 / 735,222, 16 / 987,113, 16 / 990,810, and 17 These are described in issues / 085,564, 17 / 100,396, 17 / 342,329, 17 / 518,524, 17 / 531,417, 17 / 835,777, 17 / 851,487, 17 / 867,621, 18 / 373,899 and 17 / 824,242, each of which is incorporated herein by reference in its entirety.

[0060] The treatment planning module 118 can generate a virtual model (e.g., a 3D model) that includes a first set of anatomical element models having high-fidelity surface topology for designing one or more patient-specific implants. The 3D model may include a second set of anatomical element models for measuring spinal evaluation indices to predict the surgical outcome associated with the implantation of one or more patient-specific implants. In some cases, one or more of the anatomical element models in the second set have less feature data than all or some of the anatomical element models in the first set. The system can generate the 3D model by positioning, oriented and / or scaling the anatomical element models of the first set of anatomical elements for incorporation into the second set of anatomical elements. The 3D model can be modified to generate new 3D models representing different stages of the treatment plan. The new 3D models can be modified based on acquired patient data, user input, etc. Depending on the embodiment, one or more 3D models are generated to represent each stage of the treatment plan.

[0061] Depending on the embodiment, the system can generate an X-ray fidelity anatomical element model based on one or more upright X-ray images of a second multidimensional image data set. The system can generate a tomographic fidelity anatomical element model based on a set of tomographic images of a first multidimensional image data set, including tomographic fidelity (e.g., polygon count of the number of polygons or triangles used to represent the surface of the model, edge and curve smoothness, surface quality (surface roughness, continuity, and presence of defects or artifacts that may affect the fidelity of the model), geometric detail (inclusion of fine features, complex structures and smaller elements), and consistency with the image). The X-ray fidelity anatomical element model has a resolution below threshold fidelity, while the tomographic fidelity anatomical element model has a fidelity above threshold fidelity, a resolution above threshold resolution, and so on. The upright X-ray images may be postoperative image captures taken after the patient has partially or completely recovered from the surgical stage.

[0062] The system can generate a 3D multi-region spine model of a patient (including, for example, the cervical and thoracic regions of the patient's spine) based on a first multi-dimensional image data and a 3D partial spine model (including, for example, a lumbar model with surface topology data for designing one or more implants) that matches the corresponding region of the captured spine in the second multi-dimensional image data. The system can generate a 3D multi-region simulation model by combining the anatomical elements of the 3D partial spine model with the 3D multi-region spine model. The system can replace lower-fidelity anatomical element models (based on, for example, X-ray images) of the 3D multi-fidelity spine model with corresponding higher-fidelity anatomical elements (based on, for example, CT images or MRI analysis) of the 3D partial spine model's anatomical elements. The system can collect additional multi-dimensional image data under other load conditions and generate replacement virtual models of anatomical elements for placement in the 3D virtual model. An example of a 3D multi-region spine model is described in U.S. Patent Application No. 18 / 373,899, which is incorporated herein by reference in its entirety.

[0063] In some embodiments, the medical device design generated by the treatment planning module 118 can be transmitted from the client computing device 102 and / or server 106 to the manufacturing system 124 for the production of the corresponding medical device. The manufacturing system 124 can be located on-site or off-site. On-site manufacturing can reduce the number of patient visits and / or the time until surgery is possible, while off-site manufacturing may be useful for producing complex devices. Off-site manufacturing facilities may have dedicated manufacturing equipment. In some embodiments, more complex device components can be manufactured off-site, while simpler components can be manufactured on-site.

[0064] Various types of manufacturing systems are suitable for use in the embodiments described herein. For example, manufacturing system 124 can be configured for additive manufacturing, such as three-dimensional (3D) printing, stereolithography (SLA), digital light processing (DLP), fused deposition modeling (FDM), selective laser sintering (SLS), selective laser melting (SLM), selective heat sintering (SHM), electron beam melting (EBM), sheet additive manufacturing (LOM), powder bed printing (PP), thermoplastic printing, direct material deposition (DMD), inkjet photoresin printing, or similar techniques, or combinations thereof. Alternatively or in combination with the above, manufacturing system 124 can be configured for subtractive (conventional) manufacturing, such as CNC machining, electrical discharge machining (EDM), grinding, laser cutting, waterjet machining, manual machining (e.g., milling, turning), or similar techniques, or combinations thereof. The manufacturing system 124 can manufacture one or more patient-specific medical devices based on manufacturing instructions or data (e.g., CAD data, 3D data, digital blueprints, stereolithography data, or other data suitable for the various manufacturing techniques described herein). Different components of system 100 can generate at least some of the manufacturing data used by the 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.). The manufacturing system 124 can analyze the manufacturability of an implant design based on the received manufacturing data. The implant design can be finalized by making changes to the geometry, surfaces, etc., and then generating manufacturing instructions. Depending on the embodiment, the server 106 generates at least a portion of the manufacturing data, which is then transmitted to the manufacturing system 124.

[0065] The manufacturing system 124 can generate CAM data, print data (e.g., powder bed print data, thermoplastic print data, photoresin data, etc.), and may include additive manufacturing equipment, subtractive manufacturing equipment, heat treatment equipment, etc. Additive manufacturing equipment can be 3D printers, stereolithography devices, digital light processing devices, melt deposition modeling devices, selective laser sintering devices, selective laser melting devices, electron beam melting devices, sheet additive manufacturing devices, powder bed printers, thermoplastic printers, direct material deposition devices, inkjet photoresin printers, or similar technologies. Subtractive manufacturing equipment can be CNC machines, electrical discharge machines, grinders, laser cutters, water jet machines, manual machines (e.g., milling machines, lathes), or similar technologies. Both additive and subtractive technologies can be used to manufacture implants with complex geometries, surface finishes, material properties, etc. The generated manufacturing instructions can be configured to cause the manufacturing system 124 to manufacture patient-specific orthopedic implants that match or are therapeutically identical to the patient-specific design. Depending on the embodiment, to simplify manufacturing, patient-specific medical devices may include features, materials, and designs that are shared across multiple designs. For example, deployable patient-specific medical devices for different patients may have similar internal deployment mechanisms but different deployment configurations. Depending on the embodiment, the components of the patient-specific medical device may be selected from a set of available prefabricated components, which may be modified based on manufacturing orders or data.

[0066] The surgical plans described herein can be performed by a surgeon, a surgical robot, or a combination thereof, thus allowing for treatment flexibility. Depending on the embodiment, the surgical procedure can 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. Depending on the embodiment, the treatment planning module 118 generates control commands configured to cause a surgical robot (e.g., a robotic surgical system, a navigation system, etc.) to perform the surgical procedure partially or completely. The control commands can be transmitted to the robotic device by a client computing device 102 and / or a server 106.

[0067] Following the treatment of a patient according to the surgical plan, the progress of treatment can be monitored over one or more periods to update the data analysis module 116, the treatment planning module 118, the disease progression module 120, and / or the intervention timing module 121. Post-treatment data can be added to the reference data stored in the database 110. Post-treatment data can be used to train machine learning models to formulate patient-specific treatment plans, patient-specific medical devices, or combinations thereof.

[0068] It should be understood that the components of system 100 can be configured in many different ways. For example, in another embodiment, the database 110, data analysis module 116, treatment planning module 118, disease progression module 120, and / or intervention timing module 121 can be components of client computing device 102 rather than server 106. In another embodiment, the database 110, data analysis module 116, treatment planning module 118, disease progression module 120, and / or intervention timing module 121 can be deployed across multiple different servers, computing systems, or other types of cloud computing resources rather than a single server 106 or client computing device 102.

[0069] Furthermore, depending on the embodiment, System 100 can operate with many other computing system environments or configurations. Examples of computing systems, environments, and / or configurations suitable for use with this technology include, but are not limited to, personal computers, server computers, handheld or laptop devices, mobile phones, wearable electronic devices, tablet devices, multiprocessor systems, microprocessor-based systems, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices.

[0070] Figure 2 shows a computing device 200 suitable for use in conjunction with the system 100 of Figure 1, according to one embodiment. The computing device 200 can be incorporated into various components of the system 100 of Figure 1, such as a client computing device 102 or a server 106. The computing device 200 includes one or more processors 210 (e.g., a CPU, GPU, HPU, etc.). The processor 210 can be a single processing unit or multiple processing units within a device, or it can be distributed across multiple devices. The processor 210 can be coupled to other hardware devices using a bus, such as a PCI bus or a SCSI bus. The processor 210 can be configured to execute one or more computer-readable program instructions, such as program instructions to do any of the methods described herein.

[0071] The computing device 200 may include, for example, one or more input devices 220 that provide input to the processor 210 in order to notify the processor 210 of actions from the user of the device 200. Actions can be communicated by a hardware controller that interprets signals received from the input devices and transmits the information to the processor 210 using a communication protocol. The input devices 220 may include, for example, a mouse, keyboard, touchscreen, infrared sensor, touchpad, wearable input device, camera-based or image-based input device, microphone, or other user input device.

[0072] The 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 (for example, images having voxels showing radiation density units or Hounsfield units representing tissue density in a given area). Depending on the embodiment, the display 230 may provide the user with graphical or textual visual feedback. The processor 210 may communicate with the display 230 via a hardware controller for the device. Depending on the embodiment, the display 230 may include an input device 220 as part of the display 230, such as when the input device 220 includes a touchscreen or has an eye-direction monitoring system. In another embodiment, the display 230 is separate from the input device 220. Examples of display devices include LCD display screens, LED display screens, projection, holographic, or augmented reality displays (for example, head-up displays or head-mounted devices).

[0073] Optionally, other I / O devices 240, such as network cards, video cards, audio cards, USB, FireWire or other external devices, cameras, printers, speakers, CD-ROM drives, DVD drives, disk drives or Blu-ray drives, can also be connected to the processor 210. Other I / O devices 240 may also include input ports for information from directly connected medical devices, such as imaging devices including MRI machines, X-ray machines, and CT machines. Other I / O devices 240 may further include input ports for receiving data from these types of devices, from other sources such as via a network, or from previously captured data stored, for example, in a database.

[0074] Depending on the embodiment, the computing device 200 may also include a communication device (not shown) that can communicate with network nodes wirelessly or via a wired connection. The communication device may communicate with another device or server over the network using, for example, the TCP / IP protocol. The computing device 200 may use the communication device to distribute its operations across multiple network devices, including imaging equipment, manufacturing equipment, and so on.

[0075] The computing device 200 may include memory 250 that is either in a single device or distributed across multiple devices. Memory 250 may include one or more devices from a variety of hardware devices for volatile and non-volatile storage, and may include 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 detached from the underlying hardware, and therefore memory is non-transient. In some embodiments, memory 250 is a non-transient computer-readable storage medium that stores, for example, programs, software, data, etc. In some embodiments, memory 250 may include program memory 260 that stores programs and software such as an operating system 262, one or more therapeutic support modules 264, and other application programs 266. The treatment support module 264 may include one or more modules configured to perform the various methods described herein (for example, the data analysis module 116 and / or treatment planning module 118 described with respect to Figure 1). The memory 250 may also include a data memory 270 which may contain, for example, reference data, configuration data, settings, user options or preferences, which can be supplied to the program memory 260 or any other element of the computer device 200.

[0076] B. Selected methods for modeling and formulating multi-stage surgical plans This technology includes a system and method for generating patient-specific surgical plans, including multi-stage patient-specific surgical plans, and for sending patient-specific surgical plans to the surgeon or other healthcare provider for review, feedback, modification, and / or approval. As described below, multi-stage patient-specific surgical plans may include, among others, surgical plans having two or more distinct treatment stages, each having a specific surgical or other medical intervention.

[0077] Figure 3 is a flowchart illustrating a method 300 for providing patient-specific medical care according to one embodiment of the present technology. Part or all of the method 300 can be performed by various computing systems or software modules, including, for example, the computing system described above with respect to Figures 1 and 2. The method 300 can be initiated in block 302 by receiving a patient dataset of a specific patient in need of treatment. The patient dataset may include data representing the patient's condition, anatomical structure, pathology, symptoms, medical history, preferences, and / or any other information or parameters related to the patient. For example, a patient dataset may include surgical intervention data, treatment prognosis data, progression data (e.g., surgeon's notes), patient feedback (e.g., quality of life questionnaires, feedback obtained using questionnaires), clinical data, patient information (e.g., demographic attributes, sex, age, height, weight, type of disease, occupation, activity level, tissue information, health score, comorbidities, health-related quality of life (HRQL)), vital signs, diagnostic results, drug information, allergies, and diagnostic device information (e.g., manufacturer, model number, specifications, user selection settings / configuration, etc.). A patient dataset may also include image data such as camera images, magnetic resonance imaging (MRI) images, ultrasound images, computed tomography (CAT) scan images, positron emission tomography (PET) images, and X-ray images. Depending on the embodiment, the patient dataset may include data representing one or more of the following: patient identification number (ID), age, gender, body mass index (BMI), lumbar lordosis, Cobb angle, pelvic intrinsic angle, intervertebral disc height, segmental flexibility, bone quality, rotational displacement, and / or spinal treatment level. The patient dataset may be received by a server, computing device, or other computing system. For example, depending on the embodiment, the patient dataset may be received by server 106 as shown in Figure 1.Depending on the embodiment, the computing system that receives the patient dataset in block 302 also stores one or more software modules (for example, the data analysis module 116, treatment planning module 118, disease progression module 120 and / or intervention timing module 121 shown in Figure 1, or additional software modules for performing various operations of method 300).

[0078] Depending on the embodiment, the received patient dataset may include disease assessment indices such as lumbar lordosis, Cobb angle, coronal plane parameters (e.g., coronal plane balance, global coronal plane balance, coronal plane pelvic tilt, etc.), sagittal plane parameters (e.g., pelvic intrinsic angle, sacral gradient, thoracic kyphosis, etc.), and / or pelvic parameters. Disease assessment indices may include micro-measurements (e.g., assessment indices related to specific or individual segments of the patient's spine) and / or macro-measurements (e.g., assessment indices related to multiple segments of the patient's spine). Depending on the embodiment, disease assessment indices may not be included in the patient dataset, and Method 300 may include determining (e.g., automatically determining) one or more of the disease assessment indices based on patient imaging data, as described below. Depending on the embodiment, the received patient data may include functional motor skills test scores (e.g., step test, 6-meter walk test, chair stand test, timed up and go test, etc.). The patient datasets received may include additional subjective test scores that reflect aspects of the patient's condition, such as pain tests (e.g., Visual Analog Scale (VAS) pain scores, low back pain assessment scores, etc.), disability tests (e.g., Oswestry Disability Index scores, Quebec Low Back Disability Test scores, etc.), and quality of life tests (e.g., quality of life scale scores).

[0079] Method 300 can be continued in block 304 by generating a multi-stage surgical plan based at least in part on the patient dataset received in block 302. As detailed below, the multi-stage surgical plan may include two or more treatment stages. Each treatment stage may include a target site or region of interest to perform a surgical procedure and one or more specific procedures or interventions performed in the region of interest. The surgical plan may also include predictive postoperative data associated with the implementation of the first and second treatment stages. For example, the surgical plan may include predictive or target postoperative anatomical placements, shown as two-dimensional or three-dimensional virtual models, for both the first and second treatment stages. Depending on the embodiment, the surgical plan may also include additional predictive postoperative analysis results, such as predicted disease progression after the first and / or second treatment stages, predicted patient satisfaction after the first and / or second treatment stages, predicted patient mobility after the first and / or second treatment stages, predicted patient pain after the first and / or second treatment stages, and predicted patient quality of life after the first and / or second treatment stages. Depending on the embodiment, the generation of a multi-stage surgical plan in block 304 includes performing some or all of the operations described later with reference to Figure 4.

[0080] After a surgical plan is selected in block 304, method 300 can continue by sending the multi-stage surgical plan to the surgeon in block 306. In some embodiments, the same computing system used in blocks 302 and 304 can send the surgical plan to a computing device (e.g., the client computing device 102 shown in Figure 1) for the surgeon's review. This may include sending the multi-stage surgical plan directly to the computing device or uploading the first and second surgical plans to the cloud or other storage system for later download. The multi-stage surgical plan can be digitally processed and displayed as a surgical report on one or more display screens for easier review, editing, annotation, etc. Thus, in some embodiments, the surgical plan can be stored on the client computing device 102 in Figure 1 as a computer executable instruction that can be executed via the surgical plan review module 123. As a result, the surgical plan can be reviewed using the surgical plan review program 125 shown in Figure 1.

[0081] The surgeon can review the multi-stage surgical plan and, in block 308, approve or disapprove the surgical plan. For example, to determine whether the surgeon considers the surgical plan acceptable, the surgeon may review the multi-stage surgical plan using the surgical plan review program 125 (Figure 1). This may include, for example, reviewing the surgical plan target site, surgical procedure, target / predicted postoperative anatomical arrangement, and predicted postoperative patient evaluation indicators.

[0082] In some embodiments, the surgeon may not approve the multi-stage surgical plan in block 308. In such embodiments, the surgeon may optionally provide feedback and / or suggested modifications to the surgical plan (e.g., by adjusting a virtual model or changing one or more aspects of the plan, or by adding comments or further requested modifications to the surgical plan). Thus, method 300 may optionally include receiving surgeon feedback and / or suggested modifications in block 310 (e.g., via a computing system). This may include, for example, modifying the target site of the surgical intervention, the surgical procedure, the date of surgery, the sequence of the surgical procedure, and / or the target postoperative anatomical arrangement. If surgeon feedback and / or suggested modifications are received in block 310, method 300 may continue in block 312 by revising the multi-stage surgical plan (e.g., automatically by a computing system) based at least in part on the surgeon feedback and / or suggested modifications received in block 310. In some embodiments, the surgeon does not provide feedback and / or suggested modifications if they reject the surgical plan. In such embodiments, block 310 can be omitted, and method 300 can continue in block 312 by selecting new and / or additional reference patient datasets and revising the multistage surgical plan (e.g., automatically by a computing system) and / or generating a new candidate multistage surgical plan. The revised and / or new multistage surgical plans can then be sent to the surgeon for review. The operations in blocks 306, 308, 310, and 312 can be repeated as many times as necessary until the surgeon selects and approves a particular surgical plan.

[0083] After obtaining surgeon approval for the surgical plan in block 308, method 300 can be continued in block 314 by designing one or more patient-specific implants (using the same computing system used in blocks 302-308) based on the selected surgical plan. In some embodiments, a first surgical procedure is associated with one or more first patient-specific implants, and a second surgical procedure is associated with one or more second patient-specific implants. Patient-specific implants can be designed based on the target site and surgical procedure included in the selected surgical plan. Patient-specific implants can also be specifically designed so that, when implanted in the target site of that particular patient using the identified surgical procedure, they cause the patient's anatomical structure to occupy a target postoperative anatomical configuration associated with the first and / or second surgical procedures (for example, by deforming the patient's anatomical structure from the patient's natural anatomical configuration to a reduced anatomical configuration). Patient-specific implants, once implanted, can be designed to occupy a reduced anatomical placement within the patient's anatomical structure for the expected lifespan of the implant (e.g., 5 years, 10 years, 20 years, 50 years, etc.). In some embodiments, patient-specific implants are designed based solely on a virtual model of the reduced anatomical placement and / or without reference to preoperative patient images.

[0084] Patient-specific implants may be any of the implants described herein or any patent references incorporated herein by reference. For example, patient-specific implants may include one or more screws (e.g., bone screws, spinal screws, pedicle screws, intervertebral screws), intervertebral implant devices (e.g., intervertebral implants), cages, plates, rods, discs, fusion devices, spacers, rods, expandable devices, stents, brackets, lashing devices, scaffolds, fixation devices, anchors, nuts, bolts, rivets, connectors, tethers, fasteners, joint replacement devices (e.g., artificial intervertebral discs), hip implants, etc. Patient-specific implant designs may 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, coefficient, hardness), and / or biological properties (e.g., bone integration, cell adhesion, antimicrobial properties, antiviral properties). For example, the design of an orthopedic implant may include implant shape, size, material, and / or effective stiffness (e.g., lattice density, strut count, strut location, etc.). Examples of patient-specific implants that can be designed with Block 314 include U.S. Patent Applications 16 / 048,167, 16 / 242,877, 16 / 207,116, 16 / 352,699, 16 / 383,215, 16 / 569,494, 16 / 699,447, 16 / 735,222, 16 / 987,113, 16 / 990,810, and 17 / 085,5 These are described in issues 64, 17 / 100,396, 17 / 342,329, 17 / 518,524, 17 / 531,417, 17 / 835,777, 17 / 851,487, 17 / 867,621, 18 / 384,762, 18 / 455,881, and 17 / 824,242, each of which is incorporated herein by reference in its entirety.

[0085] In some embodiments, the implant design in block 314 may optionally include generating fabrication instructions for manufacturing the implant. For example, a computing system may generate computer-executable fabrication instructions that, when executed by a manufacturing system, cause the manufacturing system to manufacture the implant. In embodiments where a multi-stage surgical plan requires one or more first implants and one or more second implants, fabrication instructions may optionally be generated simultaneously for both the one or more first implants and the one or more second implants. Alternatively, fabrication instructions may be generated for one or more first implants to be used in conjunction with a first surgical procedure, and the design for one or more second implants associated with a second surgical procedure may be stored for later use (for example, closer to the date of the second surgical procedure) to generate fabrication instructions for one or more second implants.

[0086] In some embodiments, patient-specific implants are designed in block 316 only after the surgeon has selected a surgical plan. Therefore, in some embodiments, the implant design is not sent to the surgeon along with the surgical plan in block 308, nor is it manufactured, before receiving surgeon approval of the surgical plan. While not bound by theory, waiting for the design of patient-specific implants until after surgeon approval of the surgical plan can increase the efficiency of method 300 and / or reduce the resources required to perform method 300. In other embodiments, one or more patient-specific implants can be designed and included in the surgical plan sent to the surgeon in block 306. Therefore, in some embodiments, the operations in block 314 may be included in block 304.

[0087] Method 300 can be continued in block 316 by manufacturing a patient-specific implant. In embodiments in which the multi-stage surgical plan includes one or more first implants and one or more second implants, both the one or more first implants and the one or more second implants can be manufactured substantially simultaneously as an option. Alternatively, it may be advantageous to manufacture only the one or more first implants associated with the first surgical procedure first. The one or more second implants can be manufactured at a later date as the date of the second surgical procedure approaches.

[0088] Regardless of whether the operation in block 316 involves the manufacture of either the first or second implant, the implant can be manufactured using additive manufacturing techniques such as 3D printing, stereolithography, digital light processing, fusion deposition modeling, selective laser sintering, selective laser melting, electron beam melting, sheet additive manufacturing, powder bed printing, thermoplastic printing, direct material deposition, or inkjet photoresin printing, or similar techniques or combinations thereof. Alternatively or in addition to the above, the implant can be manufactured using subtractive manufacturing techniques such as CNC machining, electrical discharge machining (EDM), grinding, laser cutting, waterjet machining, manual machining (e.g., milling, turning), or similar techniques or combinations thereof. The implant can be manufactured by any suitable manufacturing system (e.g., manufacturing system 124 shown in Figure 1 or manufacturing system 930 described later with respect to Figure 9). In some embodiments, the implant is manufactured by a manufacturing system that executes computer-readable manufacturing instructions generated by a computing system in block 316.

[0089] After the implant has been manufactured in block 316, method 300 can be continued in block 318 by initiating treatment according to a multi-stage surgical plan. This may include, for example, preparing for and performing a first surgical procedure. The aspects of the surgical plan, such as some or all of the first and second surgical procedures, can 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 partially by a robotic surgical platform, the surgical plan may include computer-readable control instructions configured to cause the surgical robot to perform at least partially patient-specific surgical procedures.

[0090] Method 300 can be implemented and performed in various ways. Depending on the embodiment, the operations in blocks 302-314 may be performed by a computing system associated with a first entity, block 316 may be performed by a manufacturing system associated with a second entity, and block 318 may be performed by a surgical provider, surgeon, and / or robotic surgical platform associated with a third entity. Any of the above blocks may be implemented as computer-readable instructions stored in memory and executable by one or more processors of the associated computing system.

[0091] Figure 4 is a flowchart illustrating a method 400 for generating a multi-stage surgical plan according to an embodiment of the present technology. Method 400, in whole or in part, can be performed by various computing systems or software modules, including, for example, the computing systems described above with respect to Figures 1 and 2. Method 400 can be initiated by receiving a patient dataset in block 402. The operations performed in block 402 may be the same as, or at least generally similar to, the operations performed in block 302 of Method 300 as described with respect to Figure 3. Thus, the patient dataset may include any of the information described above with reference to block 302 of Method 300.

[0092] Method 400 can be continued by determining a first treatment stage for the patient in block 404. The action of determining the first treatment stage may include several sub-actions. For example, the action of determining the first treatment stage in block 404 may include determining a first target site involved in the first surgical stage in block 404a. For example, in the context of spinal surgery, one or more vertebral levels may be identified for surgical intervention. Depending on the embodiment, the vertebral levels are cervical levels (e.g., C1-C5), thoracic levels (e.g., T1-T12), lumbar levels (e.g., L1-L5), and / or sacrum. Depending on the embodiment, the first target site includes a specific range of vertebral levels involved in the surgery (e.g., L1-L3, L3-L5, L4-T12, C1-C3, etc.). The first target site may include two, three, four, five or more cervical levels. Naturally, the target sites mentioned above are merely examples, and this technology is not limited to the anatomical sites described above. In fact, depending on the embodiment, as described throughout this “Modes for Carrying Out the Invention,” the first target site may include anatomical structures other than the spine, such as the hips, knees, ankles, shoulders, elbows, wrists, hands, jaws, skulls, or other anatomical parts.

[0093] The first target site can be determined by reviewing the patient's imaging data. In some embodiments, a computing system (e.g., server 106 in Figure 1) and / or one or more software modules (e.g., treatment planning module 118 in Figure 1) can review and analyze the patient's imaging data and automatically identify the first target site. In such embodiments, a trained machine learning program or other software-based program can analyze the patient's imaging data, identify anatomical features, extract measurements from the patient's imaging data, compare the extracted measurements to reference data (e.g., predetermined thresholds or ranges associated with a "healthy" patient normalized for age, sex, gender, etc.), and / or identify anatomical regions that are candidates for surgical reduction. Alternatively or in addition to the above, the first target site can be identified and / or confirmed by other appropriate means, such as by a technician or healthcare provider reviewing the imaging data and identifying anatomical deformities.

[0094] The action of determining the first treatment stage in block 404 may also include determining the first surgical procedure to be performed in block 404b. The first surgical procedure may be associated with a first target site. In the context of spinal surgery, typical surgical procedures include spinal fusion, artificial disc replacement, vertebroplasty, vertebral osteoplasty, laminectomy / decompression, discectomy, arthrodetic resection, foraminotomy, or other spinal surgical procedures. Examples of spinal fusion include posterior lumbar interbody fusion (PLIF), anterior lumbar interbody fusion (ALIF), transverse or transforaminal lumbar interbody fusion (TLIF), lateral lumbar interbody fusion (LLIF), direct lateral lumbar interbody fusion (DLIF), or lateral approach lumbar interbody fusion (XLIF). Thus, depending on the embodiment, determining the first surgical procedure may include determining a specific approach to the spine (e.g., anterior, lateral, posterior, etc.). The above is merely an example, and this technology may include identifying any type of spinal or surgical procedure in block 304b.

[0095] The first surgical procedure can be determined using any of the methods and systems described herein. For example, in some embodiments, the server 106 and / or associated software module (e.g., treatment planning module 118) in Figure 1 can identify one or more surgical procedures based, for example, user input, received or extracted patient data and / or identified target sites. For example, if the server 106 determines that the patient has L3–L5 intervertebral disc degeneration, the system may recommend a PLIF procedure to fuse L2–T12. Alternatively, the system may recommend artificial disc replacement at L3–L4 and L4–L5 to reduce degeneration while preserving movement. The first surgical procedure can also be determined using other methods and systems.

[0096] Depending on the embodiment, the operation in block 404b may include reviewing and / or analyzing multiple types of surgical procedures and / or surgical steps in order to identify a first surgical procedure. Depending on the embodiment, types of surgical procedures and / or surgical steps can be selected (or excluded from being included) based on, for example, user input, insurance coverage of the procedure or step, healthcare provider parameters (based on healthcare provider ranking / scores such as hospital / physician expertise, number of similar procedures performed, hospital ranking for the procedure), medical resource parameters (e.g., surgical equipment such as diagnostic equipment, facilities, surgical robots), and / or other non-patient-related information (e.g., information that can be used to score, predict, and / or rank the prognosis and risk profile of the current healthcare provider's procedures).

[0097] The operation in block 404 to determine the first treatment stage may also include in block 404c determining when the first surgical procedure identified in block 404b should be performed. Depending on the embodiment, this may include simulating or estimating the patient's disease progression (for example, using the disease progression module 120 of system 100 described with reference to Figure 1) and identifying at which stage of progression the first surgical procedure is most likely to have a positive prognosis. This may include, for example, comparing specific patient data with reference patient data to determine at which stage patients in similar circumstances had the most favorable surgical outcome.

[0098] In some embodiments, determining when the first surgical procedure should be performed involves identifying a recommended date or range of days for performing the first surgical procedure to obtain the most positive prognosis. This may be done by reviewing patient data and comparing the patient data with reference patient data, as described above. For example, in some patients with chronic degenerative conditions, immediate surgical intervention may be effective in preventing further disease progression. In such embodiments, the timing of the first surgical procedure can be set as substantially immediate (e.g., within 30 days, 60 days, 90 days, etc.). On the other hand, in some patients with acute injuries (e.g., spinal cord injury), delayed surgical intervention may be advantageous, for example, to allow inflammation to be suppressed before surgery. In such embodiments, the timing of the first surgical procedure can be set within a certain time range (e.g., 3 to 12 months, 3 to 6 months) or at a future point in time (e.g., 3 months, 4 months, 5 months, 6 months, etc.). Thus, the first treatment stage can have a fixed timing based on the passage of time.

[0099] Alternatively, rather than specifying a particular time for surgery, the determination of when the first surgical procedure should be performed may include identifying that one or more prognosis-dependent health indicators need to be changed before initiating the first treatment phase. This may include, for example, identifying one or more threshold health indicators. In such embodiments, patient indicators can be tracked over time (e.g., after approval of a multi-stage surgical plan), and the first surgical procedure can be performed after the threshold indicators are met. For example, in a patient with degenerative disc disease, the threshold indicators may be disc height at specific vertebral levels (e.g., 10 mm at L3–L4, 9 mm at L4–L5, 8 mm at L5–S1, etc.). In such embodiments, when the patient's disc height falls below the threshold (e.g., when the patient's L3–L4 disc height is less than 10 mm), it may be indicated that the patient is ready for the first surgical procedure. In another embodiment, in a patient with a bulge associated with acute spinal cord injury, the threshold evaluation index may be the amount of bulge (e.g., a reduction of 60%, 70%, 80%, or 90% of the current state). After the patient's bulge reduction meets the threshold, it may be indicated that the patient is ready to undergo the first surgical procedure. In yet another embodiment, in a morbidly obese patient, the threshold evaluation index may be the target weight or body mass index ("BMI"), and the first treatment phase begins only after the patient's weight and / or BMI falls below that threshold evaluation index. Thus, the first treatment phase may have adaptive timing based on the patient's lesion and / or health changes. In yet another embodiment, the threshold evaluation index may include a disability score (e.g., VAS, ODI, SRS22, etc.) or any other quantitative or qualitative evaluation index specified in this "Modes for Carrying Out the Invention".

[0100] To determine threshold evaluation metrics, a computing system can simulate the likely prognosis if a patient receives the first stage of treatment in their current lesion / condition, improved lesion / health, and / or worsened lesion / health condition. For example, if a patient is morbidly obese, the computing system can simulate the likely prognosis if the patient receives the first stage of treatment at their current weight, 90% of their current weight, 80% of their current weight, 110% of their current weight, etc. Each simulation can provide a predicted prognosis and a probability of success. The prognosis can be scored against the likelihood of the corresponding threshold being met (for example, the prognosis can be weighted based on the recognition that the more weight the patient needs to lose, the less likely they are to meet the threshold). The scores can then be ranked to determine an appropriate threshold evaluation metric value that is both achievable and provides a higher probability of achieving a favorable prognosis. In some embodiments, multiple simulations at different patient evaluation metrics (e.g., patient weight) can be shared with the patient and / or patient healthcare provider to show how changes in the patient's overall health may affect the patient's prognosis. For example, to encourage a patient to lose weight, the patient may be given an expected improvement in spinal lesions associated with different amounts of weight or BMI reduction.

[0101] In some embodiments, the timing of the first surgical procedure may include a combination of fixed timing (e.g., based on the passage of time) and adaptive timing (e.g., based on changes in the patient's lesion or condition). For example, the operation in block 404c may instruct that the first surgical procedure should be performed when either (a) a threshold evaluation metric is met or (b) a certain amount of time has elapsed. That is, the first surgical procedure may be instructed to be performed after either (a) or (b) occurs first. In another embodiment, the operation in block 404c may instruct that the first surgical procedure should only be performed when both (a) a threshold evaluation metric is met and (b) a certain amount of time has elapsed.

[0102] There may be additional actions associated with determining the first treatment stage. For example, in some embodiments, the action of determining the first treatment stage may include identifying or planning a first reduction anatomical placement for the patient (the reduction anatomical placement may also be referred to herein as “planned placement,” “optimized shape,” “postoperative anatomical placement,” or “target prognosis”). The first reduction anatomical placement may reflect the desired and / or predicted anatomical structure of the patient if the first treatment stage is performed. In some embodiments, one or more virtual models (e.g., two-dimensional models, three-dimensional models) representing the first reduction anatomical placement can be generated. The virtual models may include some or all of the patient’s anatomical structures within the first target site (e.g., any combination of tissue types, including, but not limited to, bone structure, cartilage, soft tissue, vascular tissue, nerve tissue, etc.). In some embodiments, the first reduction anatomical placement is identified / determined before the surgical procedure and / or the target site. In other words, a computing system or user can model a desirable anatomical prognosis and, based on the desired anatomical prognosis, identify a first surgical procedure and a first target site that will achieve the desired anatomical prognosis after being performed.

[0103] Depending on the embodiment, patient evaluation indices associated with the first reduction anatomical arrangement can also be calculated. In the context of spinal surgery, patient evaluation indices may include, for example, coronal plane parameters, sagittal plane parameters, pelvic parameters, Cobb angle, shoulder inclination, iliopsoas angle, coronal plane balance, lordosis angle, intervertebral space height, or other similar spinal parameters. As described above, patient evaluation indices can be determined before identifying the first surgical procedure and / or the first target site. That is, a computing system or user can use patient evaluation indices to identify surgical procedures and target sites that will achieve those patient evaluation indices after being performed.

[0104] After the first treatment stage has been determined, method 400 can be continued by determining a second treatment stage for the patient in block 406. Similar to the determination of the first treatment stage, the determination of the second treatment stage may include several sub-actions. For example, the determination of the second treatment stage may include determining a second target site involved in the second surgical stage in block 406a. The second target site may include any of the anatomical targets identified above with reference to the first target site, and may be determined using any of the techniques described above with reference to the first target site.

[0105] The second target site may be the same as or different from the first target site involved in the first treatment stage. For example, in some embodiments, the second target site may include one or more vertebral levels that are continuous with or overlap with one or more vertebral levels identified as the first target site. In one specific example, if the first target site includes L4-S1, the second target site may include L2-L4. Alternatively, the second target site may be separated from the first target site by, for example, one or more vertebral segments. For example, if the first target site includes L4-S1, the second target site may include T12-L2.

[0106] The operation of determining the second target site in block 406 may also include determining the second surgical procedure to be performed in block 406b. The second surgical procedure may include any of the surgical procedures specified above with reference to the first surgical procedure, and may be specified using any of the techniques described above with reference to the first surgical procedure. In some embodiments, the second surgical procedure may be of the same “type” as the first surgical procedure. For example, if the first surgical procedure was a spinal fixation procedure, the second surgical procedure may also be a spinal fixation procedure at a simply different target site using one or more different surgical approaches (e.g., lateral, anterior, or posterior) or procedures using one or more different devices (e.g., intervertebral cages or fixation plates, or types of implants such as rods or posterior fixation systems). Thus, the second surgical procedure may have different objectives than the first surgical procedure, even if it is of the same “type” as the first surgical procedure. Continuing the description of specific embodiments from above, if the first target site is L4-S1 and the first surgical procedure is fusion in L4-S1, the second surgical procedure may be a fusion procedure in a second target site of L2-L4 or T12-L2. Also, if the first surgical procedure is performed using an anterior approach, the second surgical procedure may be performed using a lateral or posterior approach.

[0107] In other embodiments, the second surgical procedure is of a different “kind” than the first surgical procedure. For example, the first surgical procedure may be an excision (e.g., a bone resection) to remove abnormal or pathological tissue growth, and the second surgical procedure may be a fusion or other spinal surgery at the same level as the first surgical procedure to provide stability and / or reduce the patient’s pain. Of course, the above are illustrative examples only, and the first and second surgical procedures may include any of the procedures specified throughout this “Modes for Carrying Out the Invention.”

[0108] The action of determining the second treatment stage in block 406 may also include determining in block 406c when the second surgical procedure should be performed. Similar to the action described in block 404c for determining when the first surgical procedure should be performed, the determination of when the second surgical procedure should be performed may be based on simulated disease progression and the timing of intervention for a reference patient who had a favorable prognosis. In such embodiments, the simulated progression may take into account that the first treatment stage is performed (for example, to determine progression assuming that the first surgical procedure has been performed at the first target site). That is, the estimated disease progression may incorporate treatments received during the first treatment stage, even if such treatments have not yet been performed in the patient. The simulated progression may also take into account likely changes in the patient's overall health (e.g., weight loss due to increased patient mobility).

[0109] Similar to the above, the timing of the second surgical procedure can be fixed, adaptive, or both. For example, the timing can be based on a fixed period from the date the first surgical procedure is performed. In such embodiments, the fixed period between the first and second surgical procedures can be approximately 6 months to 10 years, such as approximately 6 months, 9 months, 12 months, 2 years, 3 years, 5 years, or 10 years. The timing can also be adaptive, in which case one or more patient evaluation indicators are tracked after the first surgical procedure. When the tracked evaluation indicators meet a certain threshold, the patient becomes eligible for the second surgical procedure.

[0110] There may be additional actions associated with determining the second treatment stage. For example, determining the second treatment stage may include identifying or planning a second target anatomical placement, similar to how determining the first treatment stage may include planning a first target anatomical placement. The second target anatomical placement may reflect the patient's desired and / or predicted anatomical structure if the second treatment stage is performed as directed. One or more virtual models representing the second target anatomical placement can be generated. In some embodiments, the second reduced anatomical placement is identified / determined before the second surgical procedure and / or the second target site. That is, a computing system or user can model a preferred anatomical outcome and, based on the desired anatomical outcome, identify a second surgical procedure and a second target site that will achieve the desired anatomical outcome after being performed. Patient evaluation metrics associated with the second reduced anatomical placement can also be calculated.

[0111] In non-limiting embodiments, a virtual model may include a visual representation of the patient's spinal cord, including part or all of the sacrum, lumbar region, thoracic region, and / or cervical region in the anatomical arrangement of reduction. The virtual model can be designed to incorporate expected anatomical changes associated with the previous treatment stage. For example, if the first treatment stage involves a reduction intervention to the lumbar region and the second treatment stage involves a reduction intervention to the cervical region, the virtual model generated for the second treatment stage may include the expected anatomical reductions that would occur during the first treatment stage. Thus, the second treatment stage can be planned using a virtual model that incorporates any anatomical reductions that may occur during the first stage. While not bound by theory, this is expected to improve the planning of the second treatment stage because anatomical changes during the first treatment stage may affect the planned intervention during the second treatment stage (for example, lumbar reduction may affect cervical alignment). As described above, multi-stage surgical planning may also include planning evaluation metrics for each stage based on stage-specific virtual models. Techniques for generating, scoring, and / or modifying 3D models are described in U.S. Patent Application No. 16 / 569,494, “SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANTS,” filed September 12, 2019; U.S. Patent Application No. 16 / 735,222 (now U.S. Patent No. 10,902,944), filed January 6, 2020, “PATIENT-SPECIFIC MEDICAL PROCEDURES AND DEVICES, AND ASSOCIATED SYSTEMS AND METHODS”; and U.S. Patent Application No. 16 / 242,877, filed January 8, 2019, “SYSTEMS AND METHODS OF ASSISTING A SURGEON WITH SCREW PLACEMENT DURING SPINAL SURGERY,” which are incorporated in their entirety by reference.

[0112] While the above describes determining the first and second treatment stages sequentially, those skilled in the art will see from the disclosure herein that the treatment determined for the second treatment stage is at least partially based on the treatment determined for the first treatment stage, and vice versa. For example, the first surgical procedure may be less invasive, complex, and have a reduced expected recovery time for the treatment instructed to be given during the second treatment stage. Thus, the first surgical procedure may differ from a surgical procedure that would have been instructed without expecting a second surgical procedure. Similarly, the second surgical procedure may be based on the patient's most likely long-term prognosis to address the progression of the patient's condition after and / or as a result of the first surgical procedure. For example, if the first surgical procedure specifies a first particular surgical approach (e.g., lateral, posterior, anterior, etc.), the second surgical procedure may intentionally have a different surgical approach, for example, to minimize possible interference by scar tissue and / or to reduce repeated trauma to the same tissue.

[0113] In addition, depending on the embodiment, the computing system used to generate the first and second treatment stages may also predict whether a particular surgical procedure may cause changes in the patient's anatomical structure at sites other than the first target site. For example, the system may determine, by analyzing reference patient data and / or by analyzing a virtual model of the patient's anatomical structure, that a first surgical procedure fusing two or more sacral vertebral levels may cause misalignment at one or more cervical vertebral levels, or reduced mobility at one or more thoracic vertebral levels. A system and method for predicting the anatomical effects of a surgical intervention at sites distant from the target surgical site is described in U.S. Patent Application No. 63 / 437,975, which is incorporated herein by reference in its entirety. The second treatment stage may be planned particularly on these predicted changes occurring at sites other than the first target site. That is, the second treatment stage may take into account not only the expected changes at the first target site, but also the predicted changes at anatomical sites distant from the first target site. In fact, the second treatment phase can be planned to address one or more changes in an anatomical site detached from the first target site (for example, the second surgical procedure may correct cervical misalignment, improve thoracic mobility, etc.).

[0114] The order of the first and second treatment stages can also be selected based particularly on computer modeling of the patient's response to the first and second surgical procedures. For example, certain surgical procedures described herein involve implant insertion, discectomy, osteoctomy, pedicle screw insertion, and other invasive means that alter the patient's anatomical structure. Depending on the specific surgical intervention to be directed, it may be advantageous to perform certain procedures before others. For example, if pedicle screw insertion during the first specific procedure may affect the ability to perform the second specific surgical procedure, the second specific surgical procedure may be directed to be performed before the first surgical procedure. Such “interferences” between surgical procedures can be identified by modeling the patient’s response to various treatment stages using a virtual model of the patient’s anatomical structure, as described throughout this “Modes for Carrying Out the Invention.”

[0115] Therefore, Method 400 is expected to provide a comprehensive and holistic treatment plan for a specific patient over a long period (e.g., at least 5 years, at least 10 years, at least 20 years, etc.). While not bound by theory, it is expected to (1) result in better patient outcomes by optimizing not only the types of treatments a patient receives, but also the timing and sequence of treatments they receive, and (2) provide patients, surgeons, and other healthcare providers with greater transparency regarding the current and future treatments a patient may require.

[0116] After the first and second treatment stages have been generated in blocks 404 and 406, method 400 can be continued by creating a multi-stage surgical plan in block 408. The multi-stage surgical plan may include the first and second treatment stages. Depending on the embodiment, the multi-stage surgical plan may include an additional treatment stage totaling three, four, five, six or more treatment stages. Each treatment stage may include a surgical procedure or other medical intervention and a recommended time for performing the surgical procedure or other medical intervention.

[0117] The multi-stage surgical plan created in block 408 may also include additional features. Depending on the embodiment, for example, the multi-stage surgical plan may include predicted prognoses for the first and second treatment stages and the probability of success in achieving the predicted prognoses. In such embodiments, the method may include defining the success prognosis for each treatment stage (for example, by selecting a patient evaluation index associated with the success prognosis) and calculating the probability that each stage has a success prognosis. The multi-stage plan may also include predicted disease progression, predicted patient satisfaction, predicted patient mobility, predicted patient pain, predicted patient quality of life, etc., at and / or after the various stages of the multi-stage treatment plan. For example, the surgical plan may include an estimate of disease progression if the patient undergoes the first and second treatment stages. In such embodiments, the surgical plan may include a virtual model (e.g., a two-dimensional or three-dimensional virtual model) of the patient's anatomical structure at various intervals after the first and / or second treatment stages. For example, the surgical plan may include a predictive model of the patient's anatomical structure 6 months, 1 year, 2 years, 3 years, 4 years, 5 years, and / or 10 years after the first and / or second treatment phases. In addition to including a hypothetical model of the predicted patient anatomical structure, the disease progression model may also include, or instead of, a predictive patient assessment index at any of the various postoperative intervals identified above (e.g., any of the patient assessment indices described herein, including coronal parameters, sagittal parameters, pelvic parameters, Cobb angle, shoulder slope, iliopsoas angle, coronal balance, lordosis angle, intervertebral space height, or other similar spinal parameters). Similarly, the surgical plan may include probabilities associated with predicted progression, for example, to account for unexpected patient responses to the first surgical procedure and / or to account for potential unexpected lifestyle changes by the patient (e.g., weight gain).

[0118] In some embodiments, a multi-stage surgical plan can be generated with multiple alternative treatment stages based on previous treatment stages. For example, a multi-stage surgical plan can be generated with multiple alternative second treatment stages. In such embodiments, the multi-stage plan may include evaluation metrics for selecting alternative treatment stages. For example, the multi-stage plan may include a second treatment stage "Option A" that should be selected if the patient evaluation metric meets a specific threshold (e.g., intervertebral disc height greater than 10 mm at a specific spinal level, scar tissue morbidity, spinal mobility, patient weight, patient pain level, patient disability score, etc.), and a second treatment stage "Option B" that should be selected if the patient evaluation metric does not meet that specific threshold by the time the second treatment stage is scheduled to begin. Thus, the multi-stage plan may include selection criteria for selecting subsequent treatment stages based on the results of previous stages that can be selected before the first treatment stage is performed. In some embodiments, a computing system can analyze reference patient data to automatically identify and set one or more of the selection criteria. This enables adaptive customization of treatment based on the results of each stage.

[0119] In some embodiments, the creation of a multi-stage surgical plan may include generating a multi-stage surgical plan report. This may include generating instructions that encode the multi-stage surgical plan, which, when executed by a corresponding computing program (e.g., review module 123 in Figure 1), displays aspects of the multi-stage surgical plan for user review (e.g., by the surgical plan review program 125 in Figure 1). An embodiment of a typical multi-stage surgical plan report will be described later with reference to Figures 5A and 5B.

[0120] After a multi-stage surgical plan has been created in block 408, method 400 can be continued by sending the multi-stage surgical plan to the surgeon for review in block 410. The actions performed in block 410 may be the same as, or at least generally similar to, the actions performed in block 306 of method 300, as described with reference to Figure 3. After method 400, the actions described in blocks 308-318 of method 300 may optionally be performed.

[0121] In some embodiments, the computing system or other suitable computing system used to generate the multi-stage plan may receive updates as the multi-stage plan is being performed. For example, the computing system may receive patient evaluation metrics associated with the patient's actual prognosis for the first treatment stage. The system can analyze whether the multi-stage plan should proceed as planned or whether any modifications to the plan need to be made. In a first embodiment, a patient may develop more sinking than predicted after the first treatment stage, thereby showing poor bone tissue (e.g., weakening of the vertebral endplate). The planned subsequent stages may be modified to compensate for such patient-specific pathology. In a second embodiment, a patient may develop more scar tissue than predicted after the first treatment stage, thereby showing a poor response to a particular surgical approach. The planned subsequent stages may be modified to use a different surgical approach. On the other hand, if a patient develops less scar tissue than predicted after the first treatment stage, thereby showing a favorable response to a particular surgical approach, the planned subsequent stages may be modified to use the same surgical approach. The system can also analyze whether there are any unexpected anatomical effects in areas distant from the first target site (e.g., cervical vertebral misalignment after lumbar fusion), and if so, whether adjustments need to be made to the second treatment phase to compensate for those unexpected anatomical effects.

[0122] Therefore, if the planned reduction of the lesion is not achieved by a certain stage, subsequent stages can be modified or added to facilitate the achievement of the planned reduction of the lesion. In such embodiments, the computing system can update and / or generate a new virtual model of the patient's current anatomical structure and incorporate updated subsequent stages based on the actual results of one or more previous stages. For example, the tissue properties of the virtual model (e.g., bone strength, fracture toughness, scar tissue, etc.) can be based on the results of completed stages, as described above. As described above, in embodiments in which the multi-stage plan includes multiple alternative second treatment stages, the system can also analyze the actual results of the first treatment stage to determine which of the multiple alternative second treatment stages should be performed. Thus, the methods and systems described herein can iteratively analyze the interrelationships between treatment stages, lesion conditions, treatment stage results, patient feedback (e.g., pain scores), and other data.

[0123] C. Typical multi-stage surgical plans Figures 5A and 5B show a typical multi-stage surgical plan 500 ("Plan 500") as displayed by the display 122 according to an embodiment of the present technology. Specifically, Figure 5A shows the first part of Plan 500, and Figure 5B shows the second part of Plan 500. Although shown separately, those skilled in the art will see that the first and second parts can optionally be displayed side by side or as part of a continuous (e.g., scrollable) page, depending on the size and functionality of the display 122.

[0124] Referring together to Figures 5A and 5B, Plan 500 includes patient data 502. In the embodiments shown in the figures, the patient data includes patient name, patient identification number (e.g., MRN), sex, and age, but other patient data may be included in addition to or instead of this data. Plan 500 also includes individual sections associated with the various stages of Plan 500. For example, Plan 500 includes a preoperative section 510 with associated preoperative data, a first treatment stage section 520 with associated first treatment stage data, a three-year progression section 530 with associated disease progression data, and a second treatment stage section 540 with associated second treatment stage data.

[0125] The preoperative section 510 shown in Figure 5A may include patient images 521 showing the preoperative patient anatomical structure. Although shown as an X-ray image, other types of images may be included in other embodiments (e.g., MRI), or the preoperative images 521 may be omitted. The plan 500 further includes a virtual model 512 (e.g., “preoperative virtual model 512”) showing the patient’s preoperative (e.g., natural) anatomical arrangement, along with preoperative patient assessment indices 514. In the embodiment shown in the figure, the preoperative virtual model 512 is a three-dimensional model showing the patient’s lumbar region, but in other embodiments, the preoperative virtual model 512 may be two-dimensional and may include more or less the patient’s anatomical structure.

[0126] The first treatment stage section 520, also shown in Figure 5A, includes an overview of the first treatment stage. For example, section 520 may include a surgical procedure to be performed as part of the first treatment stage, the target site of the surgical procedure, a description of any implants or other devices to be implanted or otherwise used during the surgical procedure, and a recommended date for performing the surgical procedure. The above data can be determined as described above with respect to method 400 in Figure 4. The first treatment stage section 520 may also include a postoperative virtual model 522 showing the patient's predicted postoperative anatomical placement. The postoperative virtual model 522 may optionally show one or more virtual implants 523 implanted at the target site. The first treatment stage section 520 may also include a postoperative patient evaluation index 524, which is a predicted patient evaluation index of the patient if the first treatment plan is performed.

[0127] The 3-year progression section 530 shown in Figure 5B may include data associated with how the patient's condition is predicted to change over time if the first surgical procedure was performed at the first treatment phase. Specifically, section 530 may include a hypothetical model 532 showing the predicted patient anatomical structure 3 years after the first surgical procedure, and patient evaluation metrics 534 associated with the hypothetical model 532. Thus, section 530 provides an estimate of how the patient's condition will change over time after the first treatment phase. While the modeling of the patient anatomical structure 3 years after the first treatment phase and associated evaluation metrics are shown, plan 500 may include estimates for any period after the first treatment phase, such as 6 months, 1 year, 2 years, 3 years, 4 years, 5 years, 10 years, etc. Also, plan 500 may include two or more progression models. For example, plan 500 may include the predicted patient anatomical structure and associated evaluation metrics at defined discrete intervals (e.g., 6-month intervals, 1-year intervals, etc.) starting after the first treatment phase. Plan 500 may also include a progression estimate, which may take place before the first treatment stage or after the second treatment stage. The data shown in Section 530 can be generated using the disease progression module 120, as described, for example, with reference to Figure 1.

[0128] The second treatment stage section 540, also shown in 5B, is generally similar to the first treatment stage section 520 but includes data specific to the second treatment stage. For example, section 540 may include a surgical procedure performed as part of the second treatment stage, the target site of the surgical procedure, a description of any implants or other devices to be implanted or otherwise used during the surgical procedure, and a recommended timing for performing the surgical procedure. The above data can be determined as described above with respect to method 400 in Figure 4. The second treatment stage section 540 may also include a postoperative virtual model 542 showing the predicted postoperative anatomical placement of the patient if the second treatment stage is performed. The postoperative virtual model 542 may optionally show one or more virtual implants 543 implanted at the target site. Finally, the second treatment stage section 540 may include a postoperative patient evaluation index 544, which is a predicted patient evaluation index of the patient if the second treatment stage is performed.

[0129] Plan 500 is shown for illustrative purposes only. Those skilled in the art will see from the disclosure herein that many versions and variations of multi-stage surgical plans can be generated using the systems and methods of the present art. Thus, unless otherwise specified, the present art is not limited to the specific representation of the surgical plan shown in Figures 5A and 5B. Also, although displayed on display 122, Plan 500 can be displayed on other suitable display screens and / or stored by computer executable instructions on a computing device, server or other cloud-based storage system. Plan 500 may also include additional information not shown in Figures 5A and 5B, such as additional treatment stages, additional disease progression models, physician information (e.g., physician-specific expertise, physician-specific previous surgical outcomes, etc.), regulatory information (e.g., regulatory requirements, regulatory reimbursement information, etc.), insurance or payment information (e.g., application for part or all of the surgical plan), reimbursement criteria, healthcare provider expertise, available surgical instruments, manufacturing capabilities, and combinations thereof.

[0130] D. Additional selection methods and systems for modeling and planning multi-stage surgical procedures Figure 6 is a flowchart showing a method 600 for generating a surgical plan, such as a multi-stage surgical plan, according to an embodiment of the present technology. Depending on the embodiment, method 600 is performed in blocks 302 and 304 of method 300 described above. Method 600 may include a data phase 610 and a modeling phase 620. The data phase 610 may include collecting data on the patient to be treated (e.g., pathological data) and comparing the patient data with reference data (e.g., previous patient data such as pathological, surgical, and / or prognostic data). For example, a patient dataset may be received (block 612). The patient dataset may be compared with multiple reference patient datasets to identify one or more similar patient datasets within a plurality of reference patient datasets (block 614). Each of the plurality of reference patient datasets may include data representing one or more of the following: age, gender, BMI, lumbar lordosis, Cobb angle, pelvic intrinsic angle, intervertebral disc height, segmental flexibility, bone quality, rotational displacement, and / or spinal treatment level.

[0131] For example, a subset of multiple reference patient datasets can be selected based on their similarity to the patient dataset of a corresponding reference patient and / or the treatment prognosis of that reference patient. For instance, a similarity score can be generated for each reference patient dataset based on a comparison between the patient dataset and the reference patient dataset. The similarity score can represent the statistical relationship between the patient data and the reference patient dataset. One or more similar patient datasets can be identified, at least partially, based on the similarity score.

[0132] Depending on the embodiment, each patient dataset in the selected subset includes and / or is associated with data indicating a favorable treatment outcome (e.g., a single target outcome, an aggregated outcome score, or a favorable treatment outcome based on outcome thresholding). The data may include, for example, data representing one or more of the following: reduction anatomical assessment indices, presence of fusion, health-related quality of life, activity level, or complications. Depending on the embodiment, the data may be or include a outcome score that can be calculated based on a single target outcome, an aggregated outcome, and / or outcome thresholds.

[0133] Optionally, data analysis phase 610 may include identifying or determining surgical procedure data and / or medical device design data associated with a favorable treatment outcome for at least one patient dataset from a selected subset (for example, for at least one similar patient dataset). Surgical procedure data may include data representing one or more of the following: surgical approaches, reduction procedures, bone resections, or implant placements. At least one medical device design may include data representing one or more of the physical, mechanical, or biological properties of the corresponding medical device. Depending on the embodiment, at least one patient-specific medical device design may include the design of an implant or implant delivery device.

[0134] In modeling phase 620, a surgical plan (e.g., a multi-stage surgical plan) is generated (block 622). The generation step may include creating at least one predictive model (e.g., using statistics, machine learning, neural networks, AI, etc.) based on a selected subset of patient datasets and / or reference patient datasets. The predictive model can be configured to generate a surgical plan. The surgical plan may be broadly similar to any of the surgical plans described herein, including the multi-stage surgical plan described above.

[0135] In some embodiments, the predictive model includes one or more trained machine learning models that generate surgical plans at least partially. For example, the trained machine learning models can determine several candidate surgical plans for treating a patient. Each surgical plan can be associated with a corresponding medical device design. In some embodiments, the surgical plans and / or medical device designs are determined based on surgical procedure data and / or medical device design data associated with a favorable prognosis, as described above with respect to data analysis phase 610. For each surgical plan and / or corresponding medical device design, the trained machine learning model can calculate the probability of achieving a target prognosis for the patient (e.g., a favorable or desirable prognosis). The trained machine learning model can then select one or more surgical plans and / or corresponding medical device designs, at least partially based on the calculated probabilities.

[0136] Method 600 can be implemented and carried out in a variety of ways. Depending on the embodiment, one or more operations of Method 600 (e.g., data phase 610 and / or modeling phase 620) can 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 its components (e.g., client computing device 102 and / or server 106). Alternatively, one or more operations of Method 600 can be performed by a healthcare provider (e.g., a physician, surgeon), a robotic device (e.g., a surgical robot), or a combination thereof.

[0137] Figures 7A–7C show exemplary datasets that can be used and / or generated in connection with the method described herein (for example, data analysis phase 610 described with respect to Figure 6) according to one embodiment of the Art. Figure 7A shows a patient dataset 700 of patients being treated. The patient dataset 700 may include patient ID and several preoperative patient assessment indicators (e.g., age, gender, BMI, lumbar lordosis (LL), pelvic intrinsic angle (PI), and spinal treatment level (level)). Figure 7B shows several reference patient datasets 710. In the embodiment shown in the figure, the reference patient dataset 710 includes a first subset 712 (Study Group X) from a research group, a second subset 717 (Clinic Y) from a clinical database, and a third subset 716 (University Z) from an academic group. In alternative embodiments, the reference patient dataset 710 may include data from other sources as described herein. Each reference patient dataset may include patient ID, multiple preoperative patient assessment metrics (e.g., age, gender, BMI, lumbar lordosis (LL), pelvic proper angle (PI), and spinal treatment level (level)), treatment prognosis data (prognosis) (e.g., presence of fusion (fusion), HRQL, complications), and treatment procedure data (surgical intervention) (e.g., implant design, implant placement, surgical approach).

[0138] Figure 7C shows a comparison between patient dataset 700 and reference patient dataset 710. As previously mentioned, patient dataset 700 can be compared with reference patient dataset 710 to identify one or more similar patient datasets from the reference patient dataset. In some embodiments, patient evaluation indices from reference patient dataset 710 are converted to numerical values ​​and compared with patient evaluation indices from patient dataset 700 to calculate a similarity score 720 ("preoperative similarity") for each reference patient dataset. Reference patient datasets with similarity scores below a threshold can be considered similar to patient dataset 700. For example, in the embodiment shown in the figure, reference patient dataset 710a has a similarity score of 9, reference patient dataset 710b has a similarity score of 2, reference patient dataset 710c has a similarity score of 5, and reference patient dataset 710d has a similarity score of 8. Since each of these scores is below the threshold of 10, reference patient datasets 710a to 710d are identified as similar patient datasets.

[0139] To determine the surgical plan and / or implant design with the highest probability of success, treatment outcome data from similar patient datasets 710a-710d can be analyzed. For example, treatment outcome data from each reference patient dataset can be converted into a numerical prognosis score 730 ("prognosis index") representing the likelihood of a favorable prognosis. In the embodiment shown in the figure, reference patient dataset 710a has a prognosis score of 1, patient dataset 710b has a prognosis score of 1, reference patient dataset 710c has a prognosis score of 9, and reference patient dataset 710d has a prognosis score of 2. In embodiments where lower prognosis scores are associated with a higher likelihood of a favorable prognosis, reference patient datasets 710a, 710b, and 710d can be selected. Treatment procedure data from the selected reference patient datasets 710a, 710b, and 710d can then be used to determine at least one surgical plan (e.g., implant placement, surgical approach) and / or implant design that is most likely to result in a favorable prognosis for the patient being treated.

[0140] Depending on the embodiment, a method is provided for providing medical care to a patient. The method may include comparing a patient dataset with reference data. The patient dataset and reference data may include any of the data types described herein. The method may include identifying and / or selecting relevant reference data (data related to the patient's treatment, such as data on similar patients and / or similar treatment procedures) using any of the techniques described herein. A surgical plan may be generated based on the selected data using any of the techniques described herein. The surgical plan may include one or more treatment procedures (e.g., surgical procedures, instructions for procedures, models or other hypothetical representations of procedures), one or more medical devices (e.g., implantable devices, instruments for delivering devices, surgical kits), or a combination thereof.

[0141] Depending on the embodiment, a system for generating treatment plans is provided. The system can compare a patient dataset with a plurality of reference patient datasets using any of the techniques described herein. A subset of the plurality of reference patient datasets can be selected, for example, based on similarity and / or treatment prognosis, or any other technique described herein. A surgical plan can be generated based at least in part on the selected subset using any of the techniques described herein. The surgical plan may 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.

[0142] In further embodiments, the system is configured to use historical patient data. The system can select historical patient data for purposes such as formulating or selecting surgical plans, designing medical devices, etc. The historical data can be selected based on one or more similarities between the current patient and previous patients to create a guided treatment plan designed for a desired prognosis. The guided treatment plan can be tailored to the current patient to increase the likelihood of the desired prognosis. Depending on the embodiment, the system can analyze and / or select a subset of historical data to generate one or more surgical procedures, one or more medical devices, or a combination thereof. Depending on the embodiment, the system can use, for example, a subset of data from one or more groups of previous patients with favorable prognoses to generate a reference historical dataset used to design, create, and select treatment plans, medical devices, or a combination thereof.

[0143] Figure 8 is a flowchart showing another method 800 for providing patient-specific medical care according to another embodiment of the present technology. Similar to method 600 described with reference to Figure 6, method 800 can be carried out in blocks 302 and 304 of the aforementioned method 300.

[0144] Method 800 can be initiated in block 802 by receiving a patient dataset of a specific patient in need of treatment. Depending on the embodiment, block 802 may be the same as or substantially similar to block 302 of Method 300 in Figure 3A. For example, receiving a patient dataset may include receiving any of patient image data, patient evaluation metrics, or other patient data as described herein.

[0145] After the patient dataset is received in block 802, method 800 can continue in block 803 by creating a virtual model of the patient's natural anatomical arrangement (also called the “preoperative anatomical arrangement”). The virtual model may be based on image data contained in the patient dataset received in block 802. For example, the same computing system that received the patient dataset in block 802 may analyze the image data in the patient dataset to generate a virtual model of the patient's natural anatomical arrangement. The virtual model may be a two-dimensional or three-dimensional virtual representation of the patient's natural anatomical structure. The virtual model may include one or more regions of interest and may include some or all of the patient's anatomical structures within the regions of interest (e.g., any combination of tissue types, including but not limited to bone structures, cartilage, soft tissue, vascular tissue, nerve tissue, etc.). In a non-limiting embodiment, the virtual model may include a virtual representation of the patient's spinal cord, including some or all of the sacrum, lumbar region, thoracic region, and / or cervical region. In some embodiments, the virtual model may include soft tissue, cartilage, and other non-bone structures. In other embodiments, the virtual model may include only the patient's bone structures. Depending on the embodiment, Method 800 may optionally omit the creation of a virtual model of the patient's natural anatomical structure in block 803 and proceed directly from block 802 to block 804.

[0146] Depending on the embodiment, the computing system that generates the virtual model in block 802 may also determine (e.g., automatically determine or measure) one or more disease assessment indicators for a patient based on the virtual model. For example, the computing system may analyze the virtual model to determine the patient's preoperative lumbar lordosis, Cobb angle, coronal plane parameters (e.g., coronal plane balance, global coronal plane balance, coronal plane pelvic tilt, etc.), sagittal plane parameters (e.g., pelvic intrinsic angle, sacral gradient, thoracic kyphosis, etc.), and / or pelvic parameters. Disease assessment indicators may include micro-measurements (e.g., assessment indicators associated with specific or individual segments of the patient's spine) and / or macro-measurements (e.g., assessment indicators associated with multiple segments of the patient's spine).

[0147] Method 800 can be continued in block 804 by creating a virtual model of the patient's reduced anatomical arrangement (which may also be referred to herein as “planned arrangement,” “optimized shape,” “postoperative anatomical arrangement,” or “target prognosis”). For example, a computing system can use the aforementioned analytical procedures to determine the “reduced” or “optimized” anatomical arrangement of a particular patient that represents the ideal surgical prognosis for that particular patient. This can be done, for example, by analyzing multiple reference patient datasets to identify the postoperative anatomical arrangements of similar patients that had a favorable postoperative prognosis (based on, for example, the similarity of the reference patient dataset to the patient dataset, and / or whether the reference patient had a favorable treatment prognosis), as described in detail with respect to Figures 6-7C. This may include applying one or more mathematical rules that define the optimal anatomical prognosis (e.g., positional relationships between anatomical elements) and / or target (e.g., acceptable) postoperative evaluation metrics / design criteria (e.g., adjusting anatomical structures such that the postoperative sagittal vertical axis is less than 7 mm, the postoperative Cobb angle is less than 10 degrees, etc.). Target postoperative evaluation indicators may include, but are not limited to, target coronal plane parameters, target sagittal plane parameters, target pelvic intrinsic angle, target Cobb angle, target shoulder inclination, target iliopsoas angle, target coronal plane balance, target Cobb angle, target spinal lordosis angle, and / or target intervertebral space height. The difference between the natural anatomical arrangement and the reduced anatomical arrangement is sometimes referred to as "patient-specific reduction" or "target reduction."

[0148] After the reduced anatomical arrangement is determined, the computing system can generate a two-dimensional or three-dimensional virtual representation of the patient's anatomical structure having the reduced anatomical arrangement. Similar to the virtual model created in block 803, the virtual model of the patient's reduced anatomical arrangement may include one or more regions of interest and may include some or all of the patient's anatomical structures within the regions of interest (any combination of tissue types, including, but not limited to, bone structures, cartilage, soft tissue, vascular tissue, nerve tissue, etc.). In a non-limiting embodiment, the virtual model may include a virtual representation of the patient's spinal cord within the reduced anatomical arrangement, including some or all of the sacrum, lumbar region, thoracic region, and / or cervical region. 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 bone structures.

[0149] The computing system, in block 803, can generate a multifidelity anatomical representation (e.g., a 3D virtual model) of a patient's anatomical structure or region. The multifidelity anatomical representation can be generated at each stage of the treatment plan. For example, a multifidelity 3D virtual model may include a first set of anatomical elements (e.g., the patient's cervical, thoracic, or lumbar spine) generated based on a first multidimensional image data. The first set of anatomical elements may have a first fidelity for obtaining one or more measurements of an anatomical region. The first fidelity may be above or below a threshold fidelity, and the second fidelity may be above or below the threshold fidelity for implant design. Fidelity can be based on, for example, the number of mesh elements and / or the volume of the mesh anatomical elements, the features of the 3D information (e.g., vertices, edges, polygon faces of surface representation, coded curves and surfaces, material data, etc.), whether the model has surfaces represented by small planes or curves, the level of detail, the type of source image data (e.g., number of images, pixel size, etc.), the contour generation algorithm (e.g., an algorithm for generating anatomical contours based on an image set using a convolutional neural network, machine learning system, etc.), the reconstruction program, voxel parameters, and / or additional fidelity evaluation metrics. Fidelity parameters or evaluation metrics can be scored by the user or system to determine the fidelity of anatomical elements. For example, an anatomical model formed by curves generated based on a set of CT scans (e.g., scans at 1 mm slices, 1.5 mm slices, a 128-slice CT scan, CT dose, etc.) can be assigned a higher fidelity than an anatomical model with flat polygon faces showing contoured surfaces based on X-rays. In another embodiment, an anatomical model generated based on a 256-slice CT scan can be assigned a higher fidelity (e.g., 2, 3, or 4 times higher) than an anatomical model generated based on a 16-slice CT scan. The threshold fidelity can be selected based on the task being performed.Such tasks may include, for example, generating a patient-aligned surface contour, measuring anatomical values / assessment indices (e.g., assessment indices associated with spinal curvature and / or alignment), performing one or more anatomical element analyses (e.g., load-bearing analysis, fracture analysis, etc.), and other tasks disclosed herein. For example, the implant design threshold fidelity for designing a patient-aligned surface contour of an implant may be higher than the measurement threshold fidelity for measuring anatomical features (e.g., intervertebral disc space height, spinal curvature, etc.).

[0150] Depending on the embodiment, the multifidelity 3D virtual model may include a second set of anatomical elements (e.g., representing the lumbar spine of a patient) generated based on a second set of multidimensional image data. The second set of anatomical elements may have a second fidelity for designing one or more implants that fit the second set of anatomical elements. The system can use the multifidelity 3D virtual model to simulate one or more reductions (e.g., spinal reduction, lumbar reduction), decompression procedures, etc. In response to one or more simulated lumbar reductions, the system can generate at least one of the following: a lumbar compensatory response, a pelvic tilt compensatory response, a thoracic compensatory response, or a cervical compensatory response.

[0151] The system can perform a first task (e.g., designing at least one implant surface consistent with the patient surface) on a first region of the 3D virtual model (e.g., using a surgical planning platform) based on a first fidelity level of the first region. The first fidelity level can correspond to a first level of image detail of the patient image in a first set of image data. Furthermore, the system can perform a second task on a second region of the 3D virtual model (e.g., using a surgical planning platform) based on a second fidelity level of the second region. The second fidelity level can correspond to a second level of image detail of the patient image in a second set of image data. The number and type of tasks can be selected based on the procedure to be performed. The surgical planning platform can limit the available tasks to each of several regions of the 3D multi-resolution virtual model based on whether the resolution level of that region is acceptable for the available tasks. The surgical planning platform can assign usage restrictions (e.g., multi-element constraints such as grouped anatomical element constraints and individual element constraints) to the 3D multi-fidelity virtual model based on fidelity analysis of the 3D multi-fidelity virtual model. Different virtual models can be generated to perform different types of simulations.

[0152] Method 800 can be continued in block 806 by generating (e.g., automatically) a surgical plan to achieve the reduced anatomical arrangement shown by the virtual model. The surgical plan may be substantially similar to the surgical plan described herein and may include, for example, multiple treatment stages, preoperative plans, surgical plans, postoperative plans, and / or specific spinal evaluation indices associated with optimal surgical outcomes. For example, the surgical plan may include specific surgical procedures to achieve the reduced anatomical arrangement. In the context of spinal surgery, the surgical plan may include specific fusion surgeries (e.g., PLIF, ALIF, TLIF, LLIF, DLIF, XLIF, etc.) over specific ranges of spinal level (e.g., L1-L4, L1-5, L3-T12, etc.). Other surgical procedures, such as non-fusion surgical approaches and orthopedic procedures for other areas of the patient, may also be specified to achieve the reduced anatomical arrangement. The surgical plan may also include, as described above, one or more expected spinal assessment indices corresponding to the expected postoperative patient anatomical structure (e.g., lumbar lordosis, Cobb angle, coronal plane parameters, sagittal plane parameters, and / or pelvic parameters). The surgical plan can be generated by the same or a different computing system that created the virtual model of the reduction anatomical arrangement. In some embodiments, the surgical plan may also be based on one or more reference patient datasets, as described above with respect to Figures 6-7C. In some embodiments, the surgical plan may also be based at least in part on surgeon-specific preferences and / or prognosis associated with the particular surgeon performing the surgery.

[0153] Figure 9 is a schematic diagram of a surgical setup that includes selected systems and devices usable to provide patient-specific medical care, such as for performing certain operations described above with respect to methods 300, 400, 600, and 800 as described with respect to Figures 3, 4, 6, and 8. As shown in the figure, the surgical setup includes a computing device 902, a computing system 906, a cloud 908, a manufacturing system 930, and a robotic surgical platform 950. The computing device 902 can be a user device such as a smartphone, mobile device, laptop, desktop, personal computer, tablet, phablet, or other such device known in the art. During operation, a user (e.g., surgeon) can use the computing device 902 to collect, retrieve, review, modify, or otherwise interact with patient datasets. The computing system 906 may include any suitable computing system configured to store one or more software modules for purposes such as identifying reference patient datasets, determining patient-specific surgical plans, generating virtual models of patient anatomical structures, and designing patient-specific implants. One or more software modules may include algorithms, machine learning models, artificial intelligence architectures, etc., for performing selected actions. Cloud 908 can be any suitable network and / or storage system and may include any combination of hardware and / or virtual computing resources. Manufacturing system 930 can be any suitable manufacturing system for producing patient-specific implants, including any of those described herein. Robotic surgical platform 950 (also referred to herein as "Platform 950") can be configured to perform or otherwise assist one or more aspects of surgical procedures.

[0154] In a typical operation, the computing device 902, computing system 906, cloud 908, manufacturing system 930, and platform 950 can be used to provide patient-specific medical care, such as by performing the methods described herein. For example, the computing system 906 can receive patient datasets from the computing device 902 (e.g., block 302 of Method 300, block 802 of Method 800, etc.). In some embodiments, the computing device 902 can transmit patient datasets directly to the computing system 906. In other embodiments, the computing device 902 can upload patient datasets to the cloud 908, and the computing system 906 can download or otherwise access patient datasets from the cloud. After the computing system 906 receives the patient dataset, it can create a virtual model of the patient's natural anatomical arrangement (for example, in block 803 of Method 800), create a virtual model of the reduced anatomical arrangement (for example, in block 804 of Method 800), and / or generate a surgical plan to realize the reduced anatomical arrangement (for example, in block 304 of Method 300, block 408 of Method 400, block 806 of Method 800, etc.). The computing system may perform the above operations via one or more software modules, which, depending on the embodiment, include machine learning models or other artificial intelligence architectures. After the surgical plan including any associated virtual models has been created, the computing system 906 can send the surgical plan to the surgeon for review (for example, in block 306 of Method 300, block 410 of Method 400). This may include, for example, sending the surgical plan directly to the computing device 902 for review by the surgeon. In other embodiments, this may include uploading virtual models and surgical plans to cloud 908.Next, the surgeon can download the virtual model and surgical plan from the cloud 908 using computing device 902, or access them by other means.

[0155] As described above, the surgeon can use computing device 902 to review the surgical plan. The surgeon can use computing device 902 to approve or reject the surgical plan and provide any feedback on the surgical plan. The surgeon's approval, rejection, and / or feedback on the surgical plan can be sent to and received by computing system 906 (for example, in blocks 308 and 310 of method 300). Computing system 906 can then revise the virtual surgical plan and associated virtual model (for example, in block 312 of method 300). Computing system 906 can send the revised virtual model and surgical plan to the surgeon for review (for example, by uploading them to cloud 908 or sending them directly to computing device 902).

[0156] The computing system 906 can also design a patient-specific implant based on a selected surgical plan using one or more software modules (for example, in block 314 of method 300). Depending on the embodiment, the software module relies on one or more algorithms, machine learning models or other artificial intelligence architectures to design the implant. After the computing system 906 has designed the patient-specific implant, it can upload the design and / or manufacturing instructions to the cloud 908. The computing system 906 can also create manufacturing instructions (e.g., computer-readable manufacturing instructions) for manufacturing the patient-specific implant. In such embodiments, the computing system 906 can upload the manufacturing instructions to the cloud 908.

[0157] The manufacturing system 930 can download or otherwise access design and / or manufacturing instructions for patient-specific implants from the cloud 908. The manufacturing system can then manufacture the patient-specific implants using additive manufacturing techniques, subtractive manufacturing techniques, or other appropriate manufacturing techniques (for example, in block 316 of method 300).

[0158] The robotic surgical platform 950 can perform or otherwise assist in one or more embodiments of a surgical procedure (for example, in block 318 of method 300). For example, the platform 950 can prepare tissue for incision, perform incision, perform excision, remove tissue, process tissue, perform reduction procedures, deliver implants to a target site, position implants at a target site, adjust implants at a target site, manipulate implants after implantation, fix implants at a target site, remove implants, suture tissue, and so on. Thus, the platform 950 may include one or more arms 955 and tip effectors for holding various surgical instruments (e.g., capture instruments, clips, needles, needle holders, irrigation instruments, suction instruments, staplers, screwdriver assemblies, etc.), imaging devices (e.g., cameras, sensors, etc.), and / or medical devices (e.g., implants 900), and enable the platform 950 to perform one or more embodiments of a surgical plan. Although illustrated as having one arm 955, those skilled in the art will see that the platform 950 can have multiple arms (e.g., two, three, four, or more) and any number of joints, connectors, motors, and degrees of freedom. In some embodiments, the platform 950 may have a dedicated first arm for holding one or more imaging devices, while the remaining arms hold various surgical instruments. In some embodiments, the instruments can be detachably attached to the arms so that they can be selectively replaced before, during, or after the surgical procedure. The arms can be made movable with a variety of ranges of motion (e.g., degrees of freedom) to provide sufficient dexterity for performing various aspects of the surgical procedure.

[0159] The platform 950 may include a control module 960 for controlling the movement of the arm 955. Depending on the embodiment, the control module 960 may include a user input device (not shown) for controlling the movement of the arm 955. The user input device may be a joystick, mouse, keyboard, touchscreen, infrared sensor, touchpad, wearable input device, camera or image-based input device, microphone, or other user input device. A user (e.g., a surgeon) may interact with the user input device to control the movement of the arm 955.

[0160] In some embodiments, the control module 960 includes one or more processors for executing machine-readable surgical instructions, which, when executed, automatically control the movement of the arm 955 to perform one or more aspects of a surgical procedure. In some embodiments, the control module 960 may receive (for example, from the cloud 908) machine-readable surgical instructions that, when executed by the control module 960, specify one or more steps of a surgical procedure, causing the platform 950 to perform one or more steps of the surgical procedure. For example, a machine-readable surgical instruction could instruct the platform 950 to prepare tissue for incision, make an incision, make an excision, remove tissue, treat tissue, perform a reduction procedure, deliver the implant 900 to a target site, position the implant 900 at the target site, adjust the configuration of the implant 900 at the target site, manipulate the implant 900 after it has been implanted, fix the implant 900 at the target site, remove the implant 900, suture the tissue, and so on. Therefore, surgical instructions may include specific instructions to articulate arm 955 to deliver a patient-specific implant or otherwise assist in such delivery.

[0161] Depending on the embodiment, the platform 950 can generate machine-readable surgical instructions (rather than simply receiving them, for example) based on the surgical plan. For example, the surgical plan may include information about the delivery route, instruments, and implantation sites. The platform 950 can analyze the surgical plan and, based on the capabilities of the robotic system (e.g., the configuration and number of robotic arms, effectors, guidance systems, virtualization systems, etc.), create executable surgical instructions for performing patient-specific procedures. This allows the surgical setup shown in Figure 9 to be compatible with a wide range of different types of robotic surgical systems.

[0162] Platform 950 may include one or more communication devices (e.g., VLC, WiMAX, LTE, WLAN, IR communication, PSTN, radio waves, Bluetooth, and / or Wi-Fi operability components) for establishing a connection with Cloud 908 and / or computing device 902 to access and / or download surgical plans and / or machine-readable surgical instructions. For example, Cloud 908 may receive a request for a specific surgical plan from Platform 950 and transmit that surgical plan to Platform 950. After identification, Cloud 908 may transmit the surgical plan directly to Platform 950 for execution. In some embodiments, Cloud 908 may transmit the surgical plan to one or more intermediate network devices (e.g., computing device 902) instead of transmitting it directly to Platform 950. Before transmitting the surgical plan to Platform 950 for execution, a user may review the surgical plan using computing device 902. Additional details relating to the identification, storage, downloading, and access of patient-specific surgical plans are described in U.S. Patent Application No. 19 / 960,810, filed on 11 August 2020, and the entire disclosure thereof is incorporated herein by reference.

[0163] Platform 950 may include additional components not explicitly shown in Figure 9. For example, in various embodiments, Platform 950 may include one or more displays (e.g., LCD display screens, LED display screens, projected, holographic, or augmented reality displays (e.g., head-up display devices or head-mounted devices)), one or more I / O devices (e.g., network cards, video cards, audio cards, USB, FireWire or other external devices, cameras, printers, speakers, CD-ROM drives, DVD drives, disk drives, or Blu-ray devices), and / or memory (e.g., 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). Depending on the embodiment, the above components may be generally similar to the similar components detailed with respect to the computing device 200 in Figure 2.

[0164] While not bound by theory, the use of a robotic surgical platform to perform various aspects of the surgical plans described herein is expected to offer several advantages over conventional surgical techniques. For example, the use of a robotic surgical platform can improve surgical outcomes and / or shorten recovery times by, for example, reducing incision size, reducing blood loss, shortening surgical procedure time, and improving the accuracy and precision of the surgery (e.g., implant placement at the target site). The platform 950 can avoid or reduce user input errors by incorporating one or more scanners to obtain information from, for example, instruments (e.g., instruments with retrieval capabilities), instruments, patient-specific implants 900 (e.g., after the implant 900 has been grasped by the arm 955). The platform 950 can verify the use of appropriate instruments before or during the surgical procedure. If the platform 950 identifies an incorrect instrument or instrument, it can send a warning to the user that a different instrument or instrument needs to be attached. The user can scan the new instrument to confirm that it is appropriate for the surgical plan. Depending on the embodiment, the surgical plan may include instructions for use, a list of equipment, equipment specifications, and replacement equipment. The platform 950 can perform pre- and post-operative check routines based on information from the scanner.

[0165] The system can virtually simulate the first surgical procedure using a three-dimensional virtual model representing the patient's anatomical structure to determine the predicted prognosis of the first stage. Based on the predicted prognosis of the first stage, the system can generate a post-recovery three-dimensional virtual model of the first stage representing the patient's anatomical structure after the predicted first stage (for example, one hour, one day, or a predetermined number of months, such as three to six months, after the surgical procedure).

[0166] The system can virtually simulate a second surgical procedure using a 3D virtual model of the first stage after recovery to determine the predicted prognosis of the second stage. Based on the predicted prognosis of the second stage, the system can generate a 3D virtual model of the second stage after recovery that represents the predicted anatomical structure of the patient in the second stage after recovery (e.g., 6-9 months after the surgical procedure, or any predetermined time). The system can send a multi-stage surgical plan for review by the surgeon. The multi-stage surgical plan may include displayable images of the predicted anatomical structure of the patient after the first stage and the predicted anatomical structure of the patient in the second stage after recovery. The 3D virtual model of the first stage after recovery may show decompression, and the 3D virtual model of the second stage after recovery may show fusion. The system can use the 3D virtual models of the first and second stages after recovery to predict how long the decompression procedure will be effective before a fusion procedure becomes necessary.

[0167] The system can determine one or more predicted post-recovery changes based on the predicted prognosis of the first stage, and modify the predicted post-first-stage anatomical structure of the patient based on these changes. In some embodiments, the 3D virtual model of the first stage after recovery represents the modified predicted post-first-stage anatomical structure of the patient. The system can repeatedly modify the virtually simulated first / second surgical procedures until the predicted prognosis of the first / second stages meets the first / second acceptable criteria. The system can update the 3D virtual models of the first stage after recovery and the 3D models of the second stage after recovery with new images of the patient's anatomical structure (e.g., MRI, X-ray, ultrasound, etc.). For example, the 3D virtual model of the first stage after recovery can be modified or replaced with a model based on post-operative images. The 3D virtual model of the second stage after recovery can be modified or replaced with a model based on post-operative images (e.g., images captured after the completion of the second surgical procedure).

[0168] Figure 10A shows an embodiment of a patient-specific implant 1000 (for example, one designed in block 314 of Method 300 and manufactured in block 316), and Figure 10B shows the implant 1000 implanted in a patient. The implant 1000 can be any orthopedic implant or other implant specifically designed to guide the patient's body into conformity with a pre-identified reduction anatomical arrangement. In the embodiment shown in the figure, the implant 1000 is an intervertebral device having a first (e.g., upper) surface 1002 configured to engage with the inferior endplate surface of the upper vertebral body and a second (e.g., lower) surface 1004 configured to engage with the superior endplate surface of the lower vertebral body. The first surface 1002 may have a patient-specific topography designed to align with (e.g., interlock with) the topography of the inferior endplate surface of the upper vertebral body to form a substantially gapless interface between them. Similarly, the second surface 104 may have a patient-specific topography designed to align with or interlock with the topography of the supraendplate surface of the inferior vertebral body, forming a substantially gapless interface between them. The implant 1000 may also include recesses 1006 or other features configured to promote internal bone growth. Because the implant 1000 is patient-specific and designed to induce morphological changes within the patient, the implant 1000 is not necessarily symmetrical and is often asymmetrical. For example, in the embodiment shown in the figure, the implant 1000 has an uneven thickness such that the plane defined by the first surface 1002 is not parallel to the central longitudinal axis A of the implant 1000. Naturally, since the implants described herein, including implant 1000, are patient-specific, this art is not limited to any particular implant design or feature. Additional features of patient-specific implants that can be designed and manufactured using this art are described in U.S. Patent Applications 16 / 987,111 and 17 / 100,396, the entirety of which is incorporated herein by reference.

[0169] Patient-specific medical procedures described herein may also include the implantation of two or more patient-specific implants within the patient to achieve a reduced anatomical arrangement (e.g., multi-site procedures). This may include the implantation of multiple implants at the same time as the procedure (e.g., during the first treatment phase), and / or the implantation of one or more first implants at the first procedure and one or more second implants at the second procedure. For example, Figure 11 shows a lower spinal cord with three patient-specific implants 1100a-1100c implanted at different vertebral levels. More specifically, the first implant 1100a is implanted between the L3 and L4 vertebral bodies, the second implant 1100b is implanted between the L4 and L5 vertebral bodies, and the third implant 1100c is implanted between the L5 vertebral body and the sacrum. Implants 1100a-1100c can be combined to perform a pre-specified corrective anatomical placement on the patient's spinal cord (for example, transforming the patient's anatomical structure from a pre-operative pathological placement to a post-operative optimized placement). Depending on the embodiment, more or fewer implants may be used to achieve the corrective anatomical placement. For example, depending on the embodiment, one, two, four, five, six, seven, eight or more implants may be used to achieve the corrective anatomical placement. In embodiments involving two or more implants, the implants may not necessarily have the same shape, size, or function. In fact, multiple implants may often have different shapes and topographies to correspond to the target vertebral levels in which they will be implanted. As also shown in Figure 11, patient-specific medical procedures described herein may include treating a patient in multiple target areas (e.g., multiple vertebral levels). [Examples]

[0170] This technology is demonstrated by various embodiments, such as those described below. For convenience, various embodiments of this technology are described as numbered embodiments (1, 2, 3, etc.). These are presented as examples and do not limit the technology. Note that any of the dependent embodiments can be combined in any appropriate manner and incorporated into each independent embodiment. Other embodiments can be presented in a similar manner.

[0171] (Example 1) A computer-based method for providing patient-specific medical care to patients, Receiving patient data and, To generate a multi-stage surgical plan for treating the patient's spine and / or spinal region based on patient data, and to generate a multi-stage surgical plan, Determining a first treatment stage comprising a first surgical procedure, a first target site for the first surgical procedure, and a first period of time during which the first surgical procedure is performed, This includes determining a second treatment stage having a second surgical procedure, a second target site for the second surgical procedure, and a second period for performing the second surgical procedure. The second treatment stage is based at least partially on the predicted prognosis of the first treatment stage. The second treatment stage is temporally separated from the first treatment stage. To generate a multi-stage surgical plan, A computer-aided procedure, including submitting a multi-stage surgical plan for review by the surgeon.

[0172] (Example 2) The computer-aided method according to Example 1, wherein the second surgical procedure is of the same type as the first surgical procedure.

[0173] (Example 3) A computer-aided procedure according to any one of Examples 1 to 2, wherein the first surgical procedure is a first spinal fixation procedure, and the second surgical procedure is a second spinal fixation procedure.

[0174] (Example 4) A computer-aided method according to any one of Examples 1 to 3, wherein the first surgical procedure is a first intervertebral body procedure, and the second surgical procedure is a fixation procedure.

[0175] (Example 5) A computer-aided method according to any one of Examples 1 to 4, wherein the first surgical procedure is of a different type than the second surgical procedure.

[0176] (Example 6) A computer-aided method according to any of Examples 1 to 5, wherein the first target site and the second target site are different.

[0177] (Example 7) A computer-aided method according to any one of Examples 1 to 6, wherein the first target area includes a first range of the vertebra and the second target area includes a second range of the vertebra.

[0178] (Example 8) A computer-aided method according to any of Examples 1 to 7, wherein the first range of the vertebra overlaps with the second range of the vertebra.

[0179] (Example 9) A computer-aided method according to any one of Examples 1 to 8, wherein the first range of vertebrae is separated from the second range of vertebrae by at least one vertebra that is not included in either the first or second range.

[0180] (Example 10) A computer-assisted method according to any of Examples 1 to 9, wherein the first surgical procedure includes an anterior approach and the second surgical procedure includes a posterior approach.

[0181] (Example 11) A computer-assisted method according to any of Examples 1 to 10, wherein the first surgical procedure includes a lateral approach and the second surgical procedure includes a posterior approach.

[0182] (Example 12) A computer-aided procedure according to any one of Examples 1 to 11, wherein a second period for performing a second surgical procedure is set based on a fixed period of time from a first period for performing a first surgical procedure.

[0183] (Example 13) A computer-aided procedure according to any one of Examples 1 to 12, wherein a second period for performing a second surgical procedure is set based on the patient evaluation index exceeding a predetermined threshold.

[0184] (Example 14) A computer-aided method according to any of Examples 1 to 13, wherein the second treatment stage is separated from the first treatment stage by at least approximately 6 months.

[0185] (Example 15) A computer-aided method according to any one of Examples 1 to 14, wherein the second treatment stage is separated from the first treatment stage by at least approximately one year.

[0186] (Example 16) A computer-aided method according to any of Examples 1 to 15, wherein the second treatment stage is separated from the first treatment stage by at least approximately 3 years.

[0187] (Example 17) To generate a multi-stage surgical plan, Simulating the prognosis of a first treatment stage, which includes generating a first virtual model of the predicted patient anatomical structure if the first treatment stage is performed, A computer-aided method according to any one of Examples 1 to 16, further comprising simulating the prognosis of a second treatment stage, which includes generating a second virtual model of the predicted patient anatomical structure if the second treatment stage were performed.

[0188] (Example 18) A computer-aided method according to any one of Examples 1 to 17, further comprising determining the order in which to perform the first treatment stage and the second treatment stage, at least in part, based on the simulated prognosis.

[0189] (Example 19) A computer-aided procedure according to any one of Examples 1 to 18, wherein simulating the prognosis of a first treatment stage includes simulating the prognosis under various patient conditions, and a first surgical procedure, a first target site and / or a first duration are determined at least in part on the simulated prognosis.

[0190] (Example 20) A computer-aided method according to any one of Examples 1 to 19, wherein various patient conditions include at least one different value from patient weight, patient BMI, or patient disability score.

[0191] (Example 21) The method involves generating a first virtual model of the patient's anatomical structure, wherein the first virtual model is associated with a first treatment stage. The method further includes generating a second virtual model of the patient's anatomical structure, wherein the second virtual model is associated with a second treatment stage. A computer-aided method according to any of Examples 1 to 20, wherein the second virtual model includes predictive reduction of patient anatomical structures performed during the first treatment phase.

[0192] (Example 22) A computer-aided method according to any one of Examples 1 to 21, further comprising simulating changes in the patient's anatomical structure over time between a first treatment phase and a second treatment phase.

[0193] (Example 23) A computer-aided procedure according to any of Examples 1 to 22, wherein the submission of a multi-stage surgical plan for review includes submitting simulated changes in the patient's anatomical structure at discrete intervals between the first and second treatment stages.

[0194] (Example 24) A computer-aided method according to any of Examples 1 to 23, wherein the simulated changes are shown using a virtual model of the patient's anatomical structure.

[0195] (Example 25) Determining the second treatment stage is To determine the second treatment stage of the first alternative and the second treatment stage of the second alternative which is different from the second treatment stage of the first alternative, A computer-aided method according to any one of Examples 1 to 24, comprising generating one or more selection criteria for selecting either a first alternative second treatment stage or a second alternative second treatment stage.

[0196] (Example 26) A computer-aided method according to any one of Examples 1 to 25, wherein one or more selection criteria include a patient evaluation index threshold after the first treatment stage.

[0197] (Example 27) This involves defining the successful prognosis for each treatment stage, where the successful prognosis is 7 A computer-aided method according to any one of Examples 1 to 26, further comprising calculating the probability of achieving a successful prognosis at each stage of treatment.

[0198] (Example 28) A non-transient computer-readable medium for storing computer-executable instructions for generating multi-stage surgical plans using a computing system, wherein when an instruction is executed, it is stored in the computing system. Receiving patient data and, To generate a multi-stage surgical plan for treating the patient's spine and / or spinal region based on patient data, the multi-stage surgical plan is A first treatment stage comprising a first surgical procedure, a first target site for the first surgical procedure, and a first period of time for performing the first surgical procedure, A second treatment stage comprising a second surgical procedure, a second target site for the second surgical procedure, and a second period for performing the second surgical procedure, The second treatment phase is based at least partially on the predicted response to the first treatment phase. The second treatment stage is temporally separated from the first treatment stage. To generate a multi-stage surgical plan, A non-transient, computer-readable medium for transmitting multi-stage surgical plans for review by the surgeon.

[0199] (Example 29) When the instruction is executed, the computing system will then further... To simulate the prognosis of the first treatment stage using the first virtual model, A non-transient computer-readable medium according to Example 28, which allows the prognosis of a second treatment stage to be simulated using a second virtual model.

[0200] (Example 30) A non-transient computer-readable medium according to any of Examples 28-29, wherein the instruction, when executed, causes the computing system to further modify at least one of the first or second treatment phases, at least in part, based on the simulated prognosis of the first or second treatment phase.

[0201] (Example 31) A non-transient, computer-readable medium according to any of Examples 28-30, which includes a predictive patient evaluation index associated with the simulation performing a first and second treatment phase.

[0202] (Example 32) A non-transient computer-readable medium according to any of Examples 28 to 31, wherein the time between the first treatment phase and the second treatment phase is determined based on the simulated prognosis of the first and second treatment phases.

[0203] (Example 33) A system for providing patient-specific treatment, One or more processors, It includes memory that stores instructions that cause the system to perform actions when executed by one or more processors, and the actions are To generate a multi-stage surgical plan for treating the patient's spine and / or spinal region based on patient data, the multi-stage surgical plan is A first treatment stage comprising a first surgical procedure, a first target site for the first surgical procedure, and a first period of time for performing the first surgical procedure, A second treatment stage comprising a second surgical procedure, a second target site for the second surgical procedure, and a second period for performing the second surgical procedure, The second treatment phase is based at least partially on the predicted response to the first treatment phase. The second treatment stage is temporally separated from the first treatment stage. To generate a multi-stage surgical plan, A system that includes submitting multi-stage surgical plans for review by the surgeon.

[0204] (Example 34) The system according to Example 33, wherein the operation of generating a multi-stage surgical plan is performed at least partially by a trained machine learning program.

[0205] (Example 35) The system according to any one of Examples 33 to 34, wherein a trained machine learning model compares patient data with reference patient data to determine one or more aspects of a multi-stage surgical plan.

[0206] (Example 36) The system according to any one of Examples 33 to 35, wherein the first treatment stage and the second treatment stage are separated by at least six months in time.

[0207] (Example 37) The operation is, To generate a first virtual model showing the predicted patient anatomical structure after the first treatment stage, This further includes generating a second virtual model showing the predicted patient anatomical structure after the second treatment stage, The system according to any of Examples 33 to 36, wherein the first virtual model and the second virtual model are sent to the surgeon along with the multi-stage surgical plan for review.

[0208] (Example 38) A computer-based method for providing patient-specific medical care to patients, To determine the predicted prognosis for the first stage, the first surgical procedure is virtually simulated using a three-dimensional virtual model representing the patient's anatomical structure, Based on the predicted prognosis of the first stage, a three-dimensional virtual model of the first stage after recovery is generated, representing the anatomical structure of the patient after the predicted first stage. To determine the expected prognosis for the second stage, the second surgical procedure is virtually simulated using a 3D virtual model of the first stage after recovery. Based on the predicted prognosis of the second stage, a three-dimensional virtual model of the second stage after recovery is generated, representing the predicted anatomical structure of the patient in the first stage. A computer-aided procedure for sending a multi-stage surgical plan for review by the surgeon, wherein the multi-stage surgical plan includes displayable images of the patient's anatomical structure after the predicted first stage and the patient's anatomical structure at the predicted second first stage.

[0209] (Example 39) Based on the predicted prognosis of the first stage, one or more predicted post-recovery changes are determined, The method according to Example 38, further comprising modifying the anatomical structure of a patient after a predicted first stage based on one or more predicted post-recovery changes, wherein a three-dimensional virtual model of the first stage after recovery represents the modified predicted post-first stage anatomical structure of the patient.

[0210] (Example 40) The method according to any one of Examples 38 to 39, further comprising repeatedly modifying a virtually simulated first surgical procedure until the predicted prognosis of the first stage meets a first acceptable criterion.

[0211] (Example 41) The method according to any one of Examples 38-40, further comprising repeatedly modifying a virtually simulated second surgical procedure until the predicted prognosis of the second stage meets the second acceptable criterion.

[0212] (Example 42) Receiving one or more acceptable criteria from the user, The method according to any one of Examples 38 to 41, further comprising repeatedly modifying at least one of the first or second surgical procedures until the predicted prognosis at the second stage meets one or more acceptable criteria.

[0213] (Example 43) The criteria for 1 or more acceptable criteria are, Spinal and pelvic parameters, or Curvature score based on spinal-pelvic parameters, The method according to any one of Examples 38 to 42, comprising at least one of the above.

[0214] (Example 44) The method according to any one of Examples 38 to 43, wherein one or more predicted post-recovery changes include at least one predicted disease progression, post-rehabilitation mobility, or a fusion process for adjacent vertebrae to fuse to form one sturdy bone.

[0215] (Example 45) The method according to any one of Examples 38 to 44, wherein the first surgical procedure is a first spinal fixation procedure at a first level along the patient's spine, and the second surgical procedure is a second spinal fixation procedure at a second level along the patient's spine.

[0216] (Example 46) The method according to any of Examples 38 to 45, wherein the first surgical procedure is a first spinal fixation procedure at a first level along the patient's spine, and the second surgical procedure is a pedicle screw / rod procedure along multiple levels of the patient's spine.

[0217] (Example 47) The method according to any one of Examples 38 to 46, wherein a multi-stage surgical plan for surgeon review includes a user input tool for at least one of panning, zooming, and modifying a spinal assessment index to modify a first or second surgical procedure.

[0218] (Example 48) Receiving one or more images of the patient's anatomical structures, Updating the first stage of the recovered 3D virtual model with one or more images, The method according to any one of Examples 38 to 47, further comprising updating the second-stage 3D virtual model after recovery with one or more images.

[0219] (Example 49) The method according to any of Examples 38 to 48, wherein a three-dimensional virtual model of the first stage after recovery shows the decompression procedure, and a three-dimensional virtual model of the first stage after recovery shows the fusion procedure, and a multi-stage surgical plan predicts how long the decompression procedure will be effective before the fusion procedure becomes necessary.

[0220] (Example 49) One or more processors, A system comprising one or more memory units that, when executed by one or more processors, store instructions causing a computing system to perform a process according to any one of the methods described in Examples 1 to 49.

[0221] (Example 50) A non-transient, computer-readable medium that, when executed by a computing system, stores instructions causing the computing system to perform one of the actions of methods 1 through 49.

[0222] (Example 51) A machine learning system configured to perform the operation of one of the methods 1 through 49.

[0223] (Example 52) The machine learning system according to Example 51, comprising a procedure-specific algorithm for individually generating one or more reductions, surgical steps, and / or surgical plans.

[0224] E. Conclusion As those skilled in the art will see, any of the aforementioned software modules can be combined into a single software module to perform the operations described herein. Similarly, software modules can be distributed across any combination of computing systems and devices described herein, and are not limited to the expressly specified configurations. Thus, unless otherwise specified, any of the operations described herein can be performed by any of the computing devices or systems described herein.

[0225] The detailed description above describes various embodiments of the device and / or process using block diagrams, flowcharts and / or examples. To the extent that such block diagrams, flowcharts and / or examples include one or more functions and / or operations, it will be apparent to those skilled in the art that each function and / or operation within such block diagrams, flowcharts or examples can be implemented individually and / or in combination by a wide range of hardware, software, firmware or substantially any combination thereof. Depending on the embodiment, some parts of the subject matter described herein can 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 see that some aspects of the embodiments disclosed herein can be equivalently implemented in an integrated circuit, in whole or in part, 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 substantially any combination thereof, and that designing such circuits and / or writing code for their software and / or firmware would be well within the skill of those skilled in the art in light of this disclosure. Furthermore, those skilled in the art will see that mechanisms of the subject matter described herein can be distributed as program products in various forms, and that exemplary embodiments of the subject matter described herein are applicable regardless of the specific type of signal-carrying medium used to actually carry out distribution.Examples of signal-carrying media include, but are not limited to, recordable media such as floppy disks, hard disk drives, CDs, DVDs, digital tapes, and computer memory, as well as transmission media such as digital and / or analog communication media (e.g., fiber optic cables, waveguides, wired communication lines, wireless communication lines, etc.).

[0226] Those skilled in the art will find it common in the art to describe devices and / or processes as described herein, and then to use engineering practice techniques to incorporate such described devices and / or processes into a data processing system. That is, at least some of the devices and / or processes described herein can be incorporated into a data processing system by a reasonable amount of experimentation. Those skilled in the art will find that a typical data processing system generally includes one or more of the following: a system unit housing, a video display device, memory such as volatile and non-volatile memory, a processor such as a microprocessor and a digital signal processor, computer entities such as an operating system, drivers, a graphical user interface and application programs, one or more interaction devices such as a touchpad or screen, and / or a control system including feedback loops and control motors (e.g., feedback for sensing position and / or velocity, control motors for moving and / or adjusting components and / or quantities). A typical data processing system can be implemented using any suitable commercially available components, such as those commonly found in data computing / communication and / or network computing / communication systems.

[0227] The subject matter described herein may involve different components housed in or connected to other different components. It should be understood that the architectures described in this manner are merely examples, and that many other architectures are actually feasible to achieve the same functionality. Conceptually, any configuration of components to achieve the same functionality is effectively "associated" in such a way that the desired functionality is achieved. Therefore, any two components in this specification combined to achieve a particular functionality, regardless of architecture or intermediate components, can be considered "associated" with each other in such a way that the desired functionality is achieved. Similarly, any two such associated components can also be considered "operably connected" or "operably coupled" with each other to achieve the desired functionality, and any two components that can be associated in this way can also be considered "operably coupled" with each other to achieve the desired functionality. Specific examples of operatically coupled components include, but are not limited to, physically connectable and / or physically interactable components and / or wirelessly interactable and / or wirelessly interacting components and / or logically interacting and / or logically interactable components.

[0228] The embodiments, features, systems, devices, materials, methods, and techniques described herein may, depending on the embodiment, be similar to one or more embodiments, features, systems, devices, materials, methods, and techniques described in the following documents. U.S. Patent Application No. 16 / 048,167, titled "SYSTEMS AND METHODS FOR ASSISTING AND AUGMENTING SURGICAL PROCEDURES," filed on July 27, 2018. U.S. Patent Application No. 16 / 242,877, filed on 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, titled "SYSTEMS AND METHODS FOR MULTI-PLANAR ORTHOPEDIC ALIGNMENT," filed on December 1, 2018. U.S. Patent Application No. 16 / 352,699, titled "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANT FIXATION," filed on March 13, 2019. U.S. Patent Application No. 16 / 383,215, titled "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANT FIXATION," filed on April 12, 2019. U.S. Patent Application No. 16 / 569,494, titled "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANTS," filed on September 12, 2019. U.S. Patent Application No. 16 / 699,447, titled "SYSTEMS AND METHODS FOR ORTHOPEDIC IMPLANTS," filed on November 29, 2019. U.S. Patent Application No. 16 / 735,222, titled "PATIENT-SPECIFIC MEDICAL PROCEDURES AND DEVICES, AND ASSOCIATED SYSTEMS AND METHODS," filed on January 6, 2020. U.S. Patent Application No. 16 / 987,113, titled "PATIENT-SPECIFIC ARTIFICIAL DISCS, IMPLANTS AND ASSOCIATED SYSTEMS AND METHODS," filed on August 6, 2020. U.S. Patent Application No. 16 / 990,810, filed on 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 on October 30, 2020, entitled "SYSTEMS AND METHODS FOR DESIGNING ORTHOPEDIC IMPLANTS BASED ON TISSUE CHARACTERISTICS," U.S. Patent Application No. 17 / 100,396, titled "PATIENT-SPECIFIC VERTEBRAL IMPLANTS WITH POSITIONING FEATURES," filed on November 20, 2020. U.S. Patent Application No. 17 / 342,439, titled "PATIENT-SPECIFIC MEDICAL PROCEDURES AND DEVICES, AND ASSOCIATED SYSTEMS AND METHODS," filed on June 8, 2021. U.S. Patent Application No. 17 / 463,054, titled "BLOCKCHAIN ​​MANAGED MEDICAL IMPLANTS," filed on August 31, 2021. U.S. Patent Application No. 17 / 518,524, titled "PATIENT-SPECIFIC ARTHROPLASTY DEVICES AND ASSOCIATED SYSTEMS AND METHODS," filed on November 3, 2021. U.S. Patent Application No. 17 / 531,417, titled "PATIENT-SPECIFIC JIG FOR PERSONALIZED SURGERY," filed on November 19, 2021. U.S. Patent Application No. 17 / 678,874, filed on February 23, 2022, entitled "NON-FUNGIBLE TOKEN SYSTEMS AND METHODS FOR STORING AND ACCESSING HEALTHCARE DATA", U.S. Patent Application No. 17 / 835,777, titled "PATIENT-SPECIFIC EXPANDABLE INTERVERTEBRAL IMPLANTS," filed on June 8, 2022. U.S. Patent Application No. 17 / 842,242, titled "PATIENT-SPECIFIC ANTERIOR PLATE IMPLANTS," filed on June 16, 2022. U.S. Patent Application No. 17 / 851,487, titled "PATIENT-SPECIFIC ADJUSTMENT OF SPINAL IMPLANTS, AND ASSOCIATED SYSTEMS AND METHODS," filed on June 28, 2022. U.S. Patent Application No. 17 / 856,625, titled "SPINAL IMPLANTS FOR MESH NETWORKS," filed on July 1, 2022. U.S. Patent Application No. 17 / 867,621, titled "PATIENT-SPECIFIC SACROILIAC IMPLANT, AND ASSOCIATED SYSTEMS AND METHODS," filed on July 18, 2022. U.S. Patent Application No. 17 / 868,729, filed on July 19, 2022, entitled "SYSTEMS FOR PREDICTING INTRAOPERATIVE PATIENT MOBILITY AND IDENTIFYING MOBILITY-RELATED SURGICAL STEPS" U.S. Patent Application No. 17 / 978,673, filed on November 1, 2022, entitled "SPINAL IMPLANTS AND SURGICAL PROCEDURES WITH REDUCED SUBSIDENCE, AND ASSOCIATED SYSTEMS AND METHODS", U.S. Patent Application No. 17 / 978,746, filed on November 1, 2022, entitled "PATIENT-SPECIFIC SPINAL INSTRUMENTS FOR IMPLANTING IMPLANTS AND DECOMPRESSION PROCEDURES," U.S. Patent Application No. 18 / 102,444, filed on January 27, 2023, entitled "TECHNIQUES TO MAP THREE-DIMENSIONAL HUMAN ANATOMY DATA TO TWO-DIMENSIONAL HUMAN ANATOMY DATA", U.S. Patent Application No. 18 / 113,573, filed on February 24, 2023, entitled "PATIENT-SPECIFIC IMPLANT DESIGN AND MANUFACTURING SYSTEM WITH A DIGITAL FILING CABINET," U.S. Patent Application No. 63 / 401,429, titled "SYSTEMS AND METHODS FOR GENERATING, DESIGNING, AND / OR MODELING MULTIPLE PATIENT-SPECIFIC SURGICAL PLANS," filed on August 26, 2022. U.S. Patent Application No. 17 / 951,085, titled "SYSTEMS FOR MANUFACTURING AND PRE-OPERATIVE INSPECTING OF PATIENT-SPECIFIC IMPLANTS," filed on September 22, 2022. U.S. Patent Application No. 63 / 420,279, titled "SYSTEMS AND METHODS FOR SELECTING, REVIEWING, MODIFYING AND / OR APPROVING SURGICAL PLANS," filed on October 28, 2022. U.S. Patent Application No. 63 / 387,009, filed on December 12, 2022, entitled "PATIENT-SPECIFIC IMPLANT DESIGN AND MANUFACTURING SYSTEM WITH A REGULATORY AND REIMBURSEMENT MANAGER," U.S. Patent Application No. 63 / 436,860, titled "PATIENT-SPECIFIC SPINAL FUSION DEVICES AND ASSOCIATED SYSTEMS AND METHODS," filed on January 3, 2023. U.S. Patent Application No. 63 / 437,966, filed on January 9, 2023, entitled "SYSTEM FOR EDGE CASE PATHOLOGY IDENTIFICATION AND IMPLANT MANUFACTURING," and, U.S. Patent No. 63 / 437,975, titled "SYSTEM FOR MODELING PATIENT SPINAL CHANGES," was filed on January 9, 2023.

[0229] All of the above-mentioned patents and patent applications are incorporated in their entirety by reference. Furthermore, the embodiments, features, systems, devices, materials, methods and techniques described herein may be applied to or used in connection with any one or more of those embodiments, features, systems, devices or other matters in a particular embodiment.

[0230] The scope disclosed herein also includes any overlap, sub-scopes, and combinations thereof. Expressions such as “at most,” “at least,” “greater than,” “less than,” and “~” include the numbers stated. Numbers preceded by terms such as “approximately,” “about,” and “substantially” as used herein include the numbers stated (e.g., about 10% = 10%) and also represent quantities close to the stated quantity that perform the desired function or achieve the desired result. For example, the terms “approximately,” “about,” and “substantially” may refer to quantities that are less than 10%, less than 5%, less than 1%, less than 0.1%, and less than 0.01% of the stated quantity.

[0231] From the above, it will be clear that various embodiments of this disclosure are described herein for illustrative purposes only, and that various modifications can be made without departing from the scope and spirit of this disclosure. Accordingly, the various embodiments disclosed herein are not intended to be limiting.

Claims

1. A computer-based method for providing patient-specific medical care to patients, Receiving patient data and, Based on the patient data, generating a multi-stage surgical plan for treating the patient's spine and / or spinal region, wherein generating the multi-stage surgical plan is Determining a first treatment stage comprising a first surgical procedure, a first target site for the first surgical procedure, and a first period of time for performing the first surgical procedure; This includes determining a second treatment stage comprising a second surgical procedure, a second target site for the second surgical procedure, and a second period of time for performing the second surgical procedure. The second treatment stage is based at least in part on the predicted prognosis of the first treatment stage, The second treatment stage is separated in time from the first treatment stage. To generate the aforementioned multi-stage surgical plan, A computer-aided procedure, comprising transmitting the aforementioned multi-stage surgical plan for review by the surgeon.

2. The computer-aided method according to claim 1, wherein the second surgical procedure is of the same type as the first surgical procedure.

3. The computer-aided method according to claim 2, wherein the first surgical procedure is a first spinal fixation procedure, and the second surgical procedure is a second spinal fixation procedure.

4. The computer-aided method according to claim 1, wherein the first surgical procedure is a first intervertebral body procedure, and the second surgical procedure is a procedure to provide fixation.

5. The computer-aided method according to claim 1, wherein the first surgical procedure is of a different type from the second surgical procedure.

6. The computer implementation method according to claim 1, wherein the first target site and the second target site are different.

7. The computer-aided method according to claim 6, wherein the first target area includes a first range of the vertebra, and the second target area includes a second range of the vertebra.

8. The computer-aided method according to claim 7, wherein the first range of the vertebra overlaps with the second range of the vertebra.

9. The computer-aided method according to claim 7, wherein the first range of the vertebrae is separated from the second range of the vertebrae by at least one vertebra that is not included in either the first or second range.

10. The computer-assisted method according to claim 1, wherein the first surgical procedure includes an anterior approach and the second surgical procedure includes a posterior approach.

11. The computer-assisted method according to claim 1, wherein the first surgical procedure includes a lateral approach and the second surgical procedure includes a posterior approach.

12. The computer-aided method according to claim 1, wherein the second period for performing the second surgical procedure is set based on a fixed period of time from the first period for performing the first surgical procedure.

13. The computer-aided procedure according to claim 1, wherein the second period for performing the second surgical procedure is set based on the patient evaluation index exceeding a predetermined threshold.

14. The computer-aided method according to claim 1, wherein the second treatment stage is separated from the first treatment stage by at least about six months.

15. The computer-aided method according to claim 1, wherein the second treatment stage is separated from the first treatment stage by at least about one year.

16. The computer-aided method according to claim 1, wherein the second treatment stage is separated from the first treatment stage by at least about three years.

17. To generate the aforementioned multi-stage surgical plan, Simulating the prognosis of the first treatment stage, which includes generating a first virtual model of the predicted patient anatomical structure if the first treatment stage is performed, The computer implementation method according to claim 1, further comprising simulating the prognosis of the second treatment stage, which includes generating a second virtual model of the predicted patient anatomical structure if the second treatment stage were performed.

18. The computer-aided method according to claim 17, further comprising determining the order in which to perform the first treatment stage and the second treatment stage, at least in part, based on the simulated prognosis.

19. The computer-aided method according to claim 17, wherein simulating the prognosis of the first treatment stage includes simulating the prognosis under various patient conditions, and the first surgical procedure, the first target site and / or the first duration is determined at least in part on the simulated prognosis.

20. The computer-aided method according to claim 19, wherein the various patient conditions include at least one different value from patient weight, patient BMI, or patient disability score.

21. To generate a first virtual model of the patient's anatomical structure, wherein the first virtual model is associated with the first treatment stage, The method further includes generating a second virtual model of the patient's anatomical structure, wherein the second virtual model is associated with the second treatment stage. The computer-aided method according to claim 1, wherein the second virtual model includes predictive reduction of patient anatomical structures performed during the first treatment phase.

22. The computer-aided method according to claim 1, further comprising simulating changes in the patient's anatomical structure over time between the first treatment stage and the second treatment stage.

23. The computer-aided method according to claim 22, wherein transmitting the multi-stage surgical plan for review includes transmitting simulated changes in the patient's anatomical structure at discrete intervals between the first treatment stage and the second treatment stage.

24. The computer-aided method according to claim 22, wherein the simulated changes are shown using a virtual model of the patient's anatomical structure.

25. Determining the second treatment stage described above is To determine a first alternative second treatment stage and a second alternative second treatment stage that is different from the first alternative second treatment stage, The computer-aided method according to claim 1, comprising generating one or more selection criteria for selecting either the first alternative second treatment stage or the second alternative second treatment stage.

26. The computer-aided method according to claim 25, wherein the one or more selection criteria include a patient evaluation index threshold after the first treatment stage.

27. To define the successful prognosis for each treatment stage, wherein the successful prognosis is 7 The computer-aided method according to claim 1, further comprising calculating the probability of achieving the aforementioned successful prognosis at each treatment stage.

28. A non-transient computer-readable medium for storing computer-executable instructions for generating multi-stage surgical plans using a computing system, wherein, when the instructions are executed, the computing system... Receiving patient data and, Based on the patient data, a multi-stage surgical plan is generated for treating the patient's spine and / or spinal region, wherein the multi-stage surgical plan is A first treatment stage comprising a first surgical procedure, a first target site for the first surgical procedure, and a first period of time for performing the first surgical procedure, A second treatment stage comprising a second surgical procedure, a second target site for the second surgical procedure, and a second period of time for performing the second surgical procedure, The second treatment stage is based at least in part on the predicted response to the first treatment stage, The second treatment stage is separated in time from the first treatment stage. To generate the aforementioned multi-stage surgical plan, A non-transient, computer-readable medium for transmitting the aforementioned multi-stage surgical plan for review by the surgeon.

29. When the aforementioned instruction is executed, the computing system further: The prognosis of the first treatment stage is simulated using a first virtual model, A non-transient computer-readable medium according to claim 28, which allows the system to simulate the prognosis of the second treatment stage using a second virtual model.

30. The non-transient computer-readable medium according to claim 29, wherein the instruction, when executed, causes the computing system to further modify at least one of the first or second treatment stage based at least in part on the simulated prognosis of the first or second treatment stage.

31. A non-transient computer-readable medium according to claim 29, wherein the simulation includes a predictive patient evaluation index associated with performing the first treatment stage and the second treatment stage.

32. The non-transient computer-readable medium according to claim 29, wherein the time between the first treatment stage and the second treatment stage is determined based on the simulated prognosis of the first treatment stage and the second treatment stage.

33. A system for providing patient-specific treatment, One or more processors, The system includes a memory that stores instructions that, when executed by one or more processors, cause the system to perform an action, and the action is, To generate a multi-stage surgical plan for treating a patient's spine and / or spinal region based on patient data, wherein the multi-stage surgical plan is A first treatment stage comprising a first surgical procedure, a first target site for the first surgical procedure, and a first period of time for performing the first surgical procedure, A second treatment stage comprising a second surgical procedure, a second target site for the second surgical procedure, and a second period of time for performing the second surgical procedure, The second treatment stage is based at least in part on the predicted response to the first treatment stage, The second treatment stage is separated in time from the first treatment stage. To generate the aforementioned multi-stage surgical plan, A system that includes transmitting the aforementioned multi-stage surgical plan for review by the surgeon.

34. The system according to claim 33, wherein the operation of generating the multi-stage surgical plan is performed at least in part by a trained machine learning program.

35. The system according to claim 34, wherein the trained machine learning model compares the patient data with reference patient data to determine one or more aspects of the multi-stage surgical plan.

36. The system according to claim 33, wherein the first treatment stage and the second treatment stage are separated by at least six months in time.

37. The aforementioned operation, To generate a first virtual model showing the predicted patient anatomical structure after the first treatment stage, The method further includes generating a second virtual model showing the predicted patient anatomical structure after the second treatment stage, The system according to claim 33, wherein the first virtual model and the second virtual model are transmitted together with the multi-stage surgical plan for review by the surgeon.

38. A computer-based method for providing patient-specific medical care to patients, In order to determine the predicted prognosis of the first stage, a first surgical procedure is virtually simulated using a three-dimensional virtual model representing the anatomical structure of the patient, Based on the predicted prognosis of the first stage, a three-dimensional virtual model of the first stage after recovery is generated, representing the anatomical structure of the patient after the predicted first stage. In order to determine the expected prognosis of the second stage, the second surgical procedure is virtually simulated using the three-dimensional virtual model of the first stage after recovery, Based on the predicted prognosis of the second stage, a three-dimensional virtual model of the second stage after recovery is generated, representing the predicted anatomical structure of the patient in the second first stage. A computer-aided procedure for sending a multi-stage surgical plan for review by a surgeon, wherein the multi-stage surgical plan includes displayable images of the patient's anatomical structure after the predicted first stage and the patient's anatomical structure at the predicted second first stage.

39. Based on the predicted prognosis of the first stage, one or more predicted post-recovery changes are determined, The method according to claim 38, further comprising modifying the anatomical structure of the patient after the predicted first stage based on one or more predicted post-recovery changes, wherein the three-dimensional virtual model of the first stage after recovery represents the modified predicted post-recovery anatomical structure of the patient after the predicted first stage.

40. The method according to claim 39, further comprising repeatedly modifying a virtually simulated first surgical procedure until the predicted prognosis of the first stage meets a first acceptable criterion.

41. The method according to claim 39, further comprising repeatedly modifying a virtually simulated second surgical procedure until the predicted prognosis of the second stage meets the second acceptable criterion.

42. Receiving one or more acceptable criteria from the user, The method according to claim 39, further comprising repeatedly modifying at least one of the first or second surgical procedures until the predicted prognosis of the second stage satisfies one or more acceptable criteria.

43. The one or more acceptable criteria mentioned above are Spinal and pelvic parameters, or Curvature score based on spinal-pelvic parameters, The method according to claim 42, comprising at least one of the above.

44. The method according to claim 39, wherein the one or more predicted post-recovery changes include at least one predicted disease progression, post-rehabilitation mobility, or a fusion process for adjacent vertebrae to fuse to form one sturdy bone.

45. The method according to claim 38, wherein the first surgical procedure is a first spinal fixation procedure at a first level along the patient's spine, and the second surgical procedure is a second spinal fixation procedure at a second level along the patient's spine.

46. The method according to claim 38, wherein the first surgical procedure is a first spinal fixation procedure at a first level along the patient's spine, and the second surgical procedure is a pedicle screw / rod procedure along multiple levels of the patient's spine.

47. The method according to claim 38, wherein the multi-stage surgical plan for review by the surgeon includes a user input tool for at least one of panning, zooming, and modifying a spinal assessment index to modify the first or second surgical procedure.

48. To receive one or more images of the anatomical structures of the patient, Updating the first stage of the recovered 3D virtual model based on one or more images, The method further includes updating the second-stage three-dimensional virtual model after the recovery based on the one or more images, The method according to claim 38, wherein the three-dimensional virtual model of the first stage after recovery shows a decompression procedure, the three-dimensional virtual model of the first stage after recovery shows a fixation procedure, and the multi-stage surgical plan predicts the period during which the decompression procedure will effectively reduce nerve compression and / or the period during which the fixation procedure should be performed.

49. It is a system, One or more processors, The system includes one or more memories that, when executed by one or more processors, store instructions causing the system to perform a process of providing patient-specific medical care to a patient, and the process is In order to determine the predicted prognosis of the first stage, a first surgical procedure is virtually simulated using a three-dimensional virtual model representing the anatomical structure of the patient, Based on the predicted prognosis of the first stage, a three-dimensional virtual model of the first stage after recovery is generated, representing the anatomical structure of the patient after the predicted first stage. In order to determine the expected prognosis of the second stage, the second surgical procedure is virtually simulated using the three-dimensional virtual model of the first stage after recovery, Based on the predicted prognosis of the second stage, a three-dimensional virtual model of the second stage after recovery is generated, representing the predicted anatomical structure of the patient in the second first stage. A system for sending a multi-stage surgical plan for review by the surgeon, wherein the multi-stage surgical plan includes displayable images of the patient's anatomical structure after the predicted first stage and the patient's anatomical structure at the predicted second first stage.

50. The aforementioned process, Based on the predicted prognosis of the first stage, one or more predicted post-recovery changes are determined, The system according to claim 49, further comprising modifying the predicted post-first stage anatomical structure of the patient based on one or more predicted post-recovery changes, wherein the three-dimensional virtual model of the post-recovery first stage represents the modified predicted post-first stage anatomical structure of the patient.

51. The aforementioned process, The system according to claim 50, further comprising repeatedly modifying a virtually simulated first surgical procedure until the predicted prognosis of the first stage meets a first acceptable criterion.

52. The aforementioned process, The system according to claim 50, further comprising repeatedly modifying a virtually simulated second surgical procedure until the predicted prognosis of the second stage meets a second acceptable criterion.

53. The aforementioned process, Receiving one or more acceptable criteria from the user, The system according to claim 50, further comprising repeatedly modifying at least one of the first or second surgical procedures until the predicted prognosis of the second stage meets one or more acceptable criteria.

54. The one or more acceptable criteria mentioned above are Spinal and pelvic parameters, or Curvature score based on spinal-pelvic parameters, The system according to claim 53, comprising at least one of the following.

55. The system according to claim 50, wherein the one or more predicted post-recovery changes include at least one predicted disease progression, post-rehabilitation mobility, or a fusion process in which adjacent vertebrae fuse to form one sturdy bone.

56. The system according to claim 49, wherein the first surgical procedure is a first spinal fixation procedure at a first level along the patient's spine, and the second surgical procedure is a second spinal fixation procedure at a second level along the patient's spine.

57. The system according to claim 49, wherein the first surgical procedure is a first spinal fixation procedure at a first level along the patient's spine, and the second surgical procedure is a pedicle screw / rod procedure along multiple levels of the patient's spine.

58. The system according to claim 49, wherein the multi-stage surgical plan for review by the surgeon includes a user input tool for at least one of panning, zooming, and modifying a spinal assessment index to modify the first or second surgical procedure.

59. The aforementioned process, To receive one or more images of the anatomical structures of the patient, Updating the first stage 3D virtual model after recovery with one or more of the aforementioned images, The method further includes updating the second-stage three-dimensional virtual model after the recovery with one or more images. The system according to claim 49, wherein the three-dimensional virtual model of the first stage after recovery represents a decompression procedure, the three-dimensional virtual model of the first stage after recovery represents a fixation procedure, and the multi-stage surgical plan predicts how long the decompression procedure will be effective before the fixation procedure becomes necessary.

60. A non-transient computer-readable medium that, when executed by a computing system, stores instructions causing the computing system to perform actions to provide patient-specific medical care to a patient, wherein the actions are In order to determine the predicted prognosis of the first stage, a first surgical procedure is virtually simulated using a three-dimensional virtual model representing the anatomical structure of the patient, Based on the predicted prognosis of the first stage, a three-dimensional virtual model of the first stage after recovery is generated, representing the anatomical structure of the patient after the predicted first stage. In order to determine the expected prognosis of the second stage, the second surgical procedure is virtually simulated using the three-dimensional virtual model of the first stage after recovery, Based on the predicted prognosis of the second stage, a three-dimensional virtual model of the second stage after recovery is generated, representing the predicted anatomical structure of the patient in the second first stage. A non-transient, computer-readable medium for sending a multi-stage surgical plan for review by the surgeon, wherein the multi-stage surgical plan includes displayable images of the patient's anatomical structure after the predicted first stage and the patient's anatomical structure at the predicted second first stage.

61. The aforementioned operation, Based on the predicted prognosis of the first stage, one or more predicted post-recovery changes are determined, A non-transient computer-readable medium according to claim 60, further comprising modifying the anatomical structure of the patient after the predicted first stage based on one or more predicted post-recovery changes, wherein the three-dimensional virtual model of the first stage after recovery represents the modified predicted post-recovery anatomical structure of the patient after the predicted first stage.

62. The aforementioned operation, A non-transient computer-readable medium according to claim 61, further comprising repeatedly modifying a virtually simulated first surgical procedure until the predicted prognosis of the first stage meets a first acceptable criterion.

63. The aforementioned operation, A non-transient computer-readable medium according to claim 61, further comprising repeatedly modifying a virtually simulated second surgical procedure until the predicted prognosis of the second stage meets a second acceptable criterion.

64. The aforementioned operation, Receiving one or more acceptable criteria from the user, A non-transient computer-readable medium according to claim 61, further comprising repeatedly modifying at least one of the first or second surgical procedures until the predicted prognosis of the second stage meets one or more acceptable criteria.

65. The one or more acceptable criteria mentioned above are Spinal and pelvic parameters, or Curvature score based on spinal-pelvic parameters, A non-transient computer-readable medium according to claim 64, comprising at least one of the following.

66. The non-transient computer-readable medium according to claim 61, wherein the one or more predicted post-recovery changes include at least one predicted disease progression, post-rehabilitation mobility, or a fusion process for adjacent vertebrae to fuse to form one sturdy bone.

67. A non-transient computer-readable medium according to claim 60, wherein the first surgical procedure is a first spinal fixation procedure at a first level along the patient's spine, and the second surgical procedure is a second spinal fixation procedure at a second level along the patient's spine.

68. A non-transient computer-readable medium according to claim 60, wherein the first surgical procedure is a first spinal fixation procedure at a first level along the patient's spine, and the second surgical procedure is a pedicle screw / rod procedure along multiple levels of the patient's spine.

69. A non-transient computer-readable medium according to claim 60, wherein the multi-stage surgical plan for review by the surgeon includes a user input tool for at least one of panning, zooming, and modifying spinal assessment indicators to modify the first or second surgical procedure.

70. The aforementioned operation, To receive one or more images of the anatomical structures of the patient, Updating the first stage 3D virtual model after recovery with one or more of the aforementioned images, The method further includes updating the second-stage three-dimensional virtual model after the recovery with one or more images. A non-transient computer-readable medium according to claim 60, wherein the three-dimensional virtual model of the first stage after recovery shows a decompression procedure, the three-dimensional virtual model of the first stage after recovery shows a fixation procedure, and the multi-stage surgical plan predicts how long the decompression procedure will be effective before the fixation procedure becomes necessary.

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