Intelligent surgical instrument selection and suggestion

Abstract

A system and technique for suggesting surgical plan and surgical instrument selection is provided. In some embodiments, the system may be configured to receive a set of inputs for a surgical procedure for a patient. The system may then determine one or more potential plans for the surgical procedure based at least in part on the set of inputs and, in some implementations, on a machine learning model. The system may then receive a selection of a plan from the one or more potential plans, and determine a plurality of surgical instruments corresponding to the plan from the selection. Accordingly, the system may then be configured to provide an output indicative of the plurality of surgical instruments to be loaded into the surgical tray. In some embodiments, the system may be configured to load a plurality of surgical instruments into a surgical tray.

Description

[0001] This application claims subject matter related to U.S. Patent Application No. 18 / 071,500. The entire disclosure of the above application is incorporated herein by reference. BACKGROUND OF THE DISCLOSURE

[0002] The present disclosure generally relates to robotic-assisted surgery and, more particularly, to surgical instrument selection and recommendations for robotic-assisted surgery.

[0003] A surgical robot can assist a surgeon or other healthcare provider in performing a surgical procedure and / or can autonomously perform one or more surgical procedures. One or more surgical instruments or tools can be used to perform the surgical procedure. In some cases, a surgeon or other healthcare provider can manually select one or more surgical instruments or tools before and during the performance of a surgical procedure. Additionally, the number of available surgical instruments or tools for performing a surgical procedure is large, such that a surgeon or other healthcare provider may spend an excessive amount of time manually selecting one or more surgical instruments or tools, potentially lengthening the surgical procedure time. SUMMARY OF THE DISCLOSURE

[0004] Example aspects of the present disclosure include:

[0005] A system for recommending a surgical plan and surgical instrument selection, the system including: a processor; and a memory that stores data for processing by the processor, the data when processed causing the processor to: receive a set of inputs for a surgical procedure for a patient; determine one or more potential plans for the surgical procedure based at least in part on the set of inputs and a machine learning model; receive a selection of a plan from the one or more potential plans; determine a plurality of surgical instruments corresponding to the plan based on the selection; and provide an output indicating the plurality of surgical instruments to be loaded onto a surgical tray.

[0006] In any aspect herein, the data stored in the memory that when processed causes the processor to provide an output indicating the plurality of surgical instruments to be loaded onto a surgical tray further causes the system to: display, via a user interface, a recommendation of the plurality of surgical instruments to be loaded onto the surgical tray.

[0007] In any aspect herein, the data stored in the memory that when processed causes the processor to provide an output indicating the plurality of surgical instruments to be loaded onto a surgical tray further causes the system to: load the plurality of surgical instruments onto the surgical tray.

[0008] Any aspect of the present disclosure, wherein data stored in a memory that, when processed, causes a processor to determine one or more potential plans for a surgical procedure further causes the system to: compare a set of inputs for the surgical procedure with historical data of previously performed surgical procedures to determine the one or more potential plans based at least in part on a similarity index.

[0009] Any aspect of the present disclosure, wherein the memory stores additional data for processing by a processor, the additional data that, when processed, causes the processor to: display, via a user interface, one or more similarity index values for each of the one or more potential plans.

[0010] Any aspect of the present disclosure, wherein for a previously performed surgical procedure, the historical data of the previously performed surgical procedure includes: procedure and instrument flow and anomalies, radiological images and annotations, demographic information, three-dimensional anatomical models, angles, positions, dimensions, implants used relative to the three-dimensional model, instrument availability information, treatment plans, or a combination thereof.

[0011] Any aspect of the present disclosure, wherein the similarity index includes: position coordinate correlation, deformation coefficient, demographic information, three-dimensional model, inventory match of available surgical instruments, or a combination thereof.

[0012] Any aspect of the present disclosure, wherein the memory stores additional data for processing by a processor, the additional data that, when processed, causes the processor to: provide position recommendations for a surgical procedure for a patient based at least in part on a set of inputs.

[0013] Any aspect of the present disclosure, wherein the memory stores additional data for processing by a processor, the additional data that, when processed, causes the processor to: receive one or more changes to the selected plan, and determine a plurality of surgical instruments to be loaded into a surgical tray based at least in part on the one or more changes.

[0014] Any aspect of the present disclosure, wherein the memory stores additional data for processing by a processor, the additional data that, when processed, causes the processor to: provide a cutting cross-sectional view of a surgical procedure for a patient based at least in part on a set of inputs; and provide one or more cutting cross-sectional views for the one or more potential plans based at least in part on historical surgical data corresponding to the one or more potential plans, wherein a selection of a plan from the one or more potential plans is received based at least in part on a comparison of the cutting cross-sectional view of the surgical procedure with the one or more cutting cross-sectional views for the one or more potential plans.

[0015] Any aspect of the present disclosure, wherein the memory stores additional data for processing by the processor, and the additional data, when processed, causes the processor to: receive feedback at least partially based on an output indicating a plurality of surgical instruments to be loaded into a surgical tray after performing a surgical procedure on a patient.

[0016] Any aspect of the present disclosure, wherein the feedback is partially used to train a machine learning model.

[0017] Any aspect of the present disclosure, wherein one or more potential plans are determined at least partially based on a disease state corresponding to a surgical procedure for a patient.

[0018] Any aspect of the present disclosure, wherein the set of inputs for a surgical procedure includes: patient demographics, one or more radiological images, pathological data, or a combination thereof.

[0019] Any aspect of the present disclosure, wherein the output indicating a plurality of surgical instruments to be loaded into a surgical tray includes an order for loading the plurality of surgical instruments into the surgical tray.

[0020] A system for suggesting a surgical plan and surgical instrument selection, the system comprising: a processor; and a memory that stores data for processing by the processor, the data, when processed, causing the processor to: receive a set of inputs for a surgical procedure for a patient; determine one or more potential plans for the surgical procedure at least partially based on the set of inputs; receive a selection of a plan from the one or more potential plans; determine a plurality of surgical instruments corresponding to the plan based on the selection; and provide an output indicating the plurality of surgical instruments to be loaded into a surgical tray.

[0021] Any aspect of the present disclosure, wherein the data stored in the memory that, when processed, causes the processor to determine one or more potential plans for a surgical procedure further causes the system to: compare a set of inputs for the surgical procedure with historical data of previously performed surgical procedures to determine one or more potential plans at least partially based on a similarity index.

[0022] Any aspect of the present disclosure, wherein the memory stores additional data for processing by the processor, and the additional data, when processed, causes the processor to: display, via a user interface, one or more similarity index values for each of the one or more potential plans.

[0023] A system for suggesting a surgical plan and surgical instrument selection, the system comprising: a processor; and a memory that stores data for processing by the processor, the data when processed causing the processor to: receive a set of inputs for a surgical procedure for a patient; determine one or more potential plans for the surgical procedure at least in part based on the set of inputs; receive a selection of a plan from the one or more potential plans; determine a plurality of surgical instruments corresponding to the plan based on the selection; and load the plurality of surgical instruments onto a surgical tray.

[0024] Any aspect among the aspects herein, wherein the plurality of surgical instruments are loaded onto the surgical tray according to an order for performing a surgical procedure for a patient at least in part based on the selected plan.

[0025] Any one aspect is combined with any one or more other aspects.

[0026] Any one or more of the features disclosed herein.

[0027] Any one or more of the features generally disclosed herein.

[0028] Any one or more of the features generally disclosed herein are combined with any one or more other features generally disclosed herein.

[0029] Any one of the aspects / features / embodiments is combined with any one or more other aspects / features / embodiments.

[0030] Use any one or more of the aspects or features disclosed herein.

[0031] It should be understood that any feature described herein can be combined with any other feature described herein to claim protection, regardless of whether the features are from the same described embodiment.

[0032] Details of one or more aspects of the present disclosure are set forth in the following drawings and description. Other features, objects, and advantages of the technology described in the present disclosure will be apparent from the description, drawings, and claims.

[0033] The phrases “at least one,” “one or more,” and “and / or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B, and C,” “at least one of A, B, or C,” “one or more of A, B, and C,” “one or more of A, B, or C,” and “A, B, and / or C” means only A, only B, only C, A and B together, A and C together, B and C together, or A, B, and C together. When each of A, B, and C in the above expressions refers to elements such as X, Y, and Z or element classes such as X1-Xn, Y1-Ym, and Z1-Zo, the phrase is intended to refer to a single element selected from X, Y, and Z, a combination of elements selected from the same class (e.g., X1 and X2), and a combination of elements selected from two or more classes (e.g., Y1 and Zo).

[0034] The term “a” entity means one or more of that entity. Thus, the terms “a,” “one or more,” and “at least one” may be used interchangeably herein. It should also be noted that the terms “comprising,” “including,” and “having” may be used interchangeably.

[0035] The foregoing is a simplified summary of the present disclosure to provide an understanding of some aspects of the present disclosure. This summary of the invention is neither an extensive nor an exhaustive overview of the present disclosure and its various aspects, embodiments, and configurations. It is neither intended to identify the key or important elements of the present disclosure nor to delineate the scope of the present disclosure, but rather to present selected concepts of the present disclosure in a simplified form as an introduction to the more detailed description presented below. As should be understood, other aspects, embodiments, and configurations of the present disclosure may utilize one or more of the features set forth above or described in detail below, either alone or in combination.

[0036] Many additional features and advantages of the present disclosure will become apparent to those skilled in the art upon consideration of the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings incorporated and form a part of this specification to illustrate several examples of the present disclosure. These drawings, together with the description, explain the principles of the present disclosure. The drawings illustrate only how to implement and use the preferred and alternative examples of the present disclosure, and these examples should not be construed as limiting the present disclosure to only the examples illustrated and described. Additional features and advantages will become apparent from the following more detailed description of the various aspects, embodiments, and configurations of the present disclosure, as illustrated by the accompanying drawings referenced below.

[0038] Figure 1 is a block diagram of a system according to at least one embodiment of the present disclosure;

[0039] Figure 2is a diagram of a system according to at least one embodiment of the present disclosure;

[0040] Figure 3 is a diagram of a system according to at least one embodiment of the present disclosure;

[0041] Figure 4 is a flowchart of a method according to at least one embodiment of the present disclosure;

[0042] Figure 5 is a flowchart of a method according to at least one embodiment of the present disclosure; and

[0043] Figure 6 is a flowchart of a method according to at least one embodiment of the present disclosure. Detailed Description

[0044] It should be understood that the various aspects disclosed herein can be combined in combinations different from those specifically presented in the specification and the drawings. It should also be understood that, depending on the example or embodiment, certain actions or events of any of the processes or methods described herein can be performed in a different order and / or certain actions or events can be added, combined, or entirely omitted (e.g., depending on different embodiments of the present disclosure, not all of the described actions or events may be required to implement the disclosed technology). Additionally, although certain aspects of the present disclosure are described as being performed by a single module or unit for clarity, it should be understood that the technology of the present disclosure can be performed by a combination of units or modules associated with, for example, a computing device and / or a medical device.

[0045] In one or more examples, the described methods, processes, and techniques can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions can be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Alternatively or additionally, the functions can be implemented using a machine learning model, a neural network, an artificial neural network, or a combination thereof (either alone or in combination with instructions). The computer-readable medium can include a non-transitory computer-readable medium, which corresponds to a tangible medium such as a data storage medium (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and can be accessed by a computer).

[0046] The instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors (e.g., Intel Core i3, i5, i7, or i9 processors; Intel Celeron processors; Intel Xeon processors; Intel Pentium processors; AMD Ryzen processors; AMD Athlon processors; AMD Phenom processors; Apple A10 or 10X Fusion processors; Apple A11, A12, A12X, A12Z, or A13 Bionic processors; or any other general-purpose microprocessor), graphics processing units (e.g., Nvidia GeForce RTX 2000 series processors, Nvidia GeForce RTX 3000 series processors, AMD Radeon RX 5000 series processors, AMD Radeon RX 6000 series processors, or any other graphics processing unit), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Thus, as used herein, the term "processor" may refer to any of the foregoing structures or any other physical structure suitable for implementing the described techniques. Additionally, these techniques may be fully implemented in one or more circuits or logic elements.

[0047] Before explaining any embodiments of the present disclosure in detail, it is to be understood that the present disclosure is not limited in its application to the construction details and component arrangements set forth in the following description or illustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or carried out in various ways. Additionally, it is to be understood that the terminology and phrases used herein are for the purpose of description and should not be regarded as limiting. The use of "comprising," "including," or "having" and variations thereof herein is intended to cover the items listed thereafter and equivalents thereof, as well as additional items. Furthermore, the present disclosure may use examples to illustrate one or more of its aspects. Unless otherwise expressly stated, the use or listing of one or more examples (which may be indicated by "for example," "by way of example," "such as," or similar language) is not intended and does not limit the scope of the present disclosure.

[0048] The terms proximal and distal are used in their conventional medical meanings in the present disclosure, with proximal being closer to the operator or user of the system and farther from the surgical area of concern within or on the patient's body, while distal is closer to the surgical area of concern within or on the patient's body and farther from the operator or user of the system.

[0049] In some surgical procedures (e.g., robotic-assisted surgery), minimally invasive procedures can be performed to treat pathological fractures of vertebral bodies (e.g., the spine and associated elements) caused by osteoporosis, cancer, benign lesions, or other diseases. For example, minimally invasive procedures can include vertebrectomy (e.g., a surgical procedure involving the removal of all or part of a vertebral body, typically as a way to decompress the spine and nerves), kyphoplasty (e.g., a surgical procedure for treating steroid-induced spinal compression fractures based on inserting an inflatable balloon tamper into a fractured vertebra to restore the height of the collapsed vertebra), vertebroplasty (e.g., a procedure for stabilizing compression fractures in the spine based on injecting bone cement into a vertebra that has cracked or broken), radiofrequency ablation therapy (e.g., a medical procedure that uses heat generated by medium-frequency alternating current to ablate a part of the electrical conduction system of the heart, a mass, or other dysfunctional tissue to treat a range of conditions, including chronic neck and back pain), or another procedure not explicitly listed herein. Additionally or alternatively, the surgical procedures described herein can more generally include spinal surgery, cranial surgery, or another type of surgical procedure.

[0050] The surgical procedure can include multiple steps. In a first step for preoperative setup, specific instruments and accessories (e.g., for bone access, fracture reduction, stabilization, etc.) are arranged, and the patient's position is determined (e.g., to relieve the load on the fractured bone or other area of interest). A second step for imaging setup can include verifying different scans (e.g., magnetic resonance imaging (MRI) scans, computed tomography (CT) scans, etc.) to determine the optimal inclination for accessing the area of interest (e.g., the fractured bone) and the placement of other components for the surgical procedure (e.g., an inflatable balloon, bone cement injection, current generation source, etc.). A third step can be performed for bone access establishment, where the optimal incision location is determined and marked (e.g., with a surgical pen), and a biopsy (e.g., a bone biopsy) can be performed to rule out the possibility of a malignant tumor.

[0051] A fourth step can be performed for fracture reduction, where preparations for the insertion and positioning of the components for the surgical procedure are determined and carried out (e.g., directly under or near the fracture area). Then, a fifth step can include fracture fixation, such as selecting a filler and volumetric gauge, preparing the elements for insertion (e.g., packaging or bone cement), delivering the elements to the area of interest (e.g., delivering cement to a location until the cavity is filled), removing the components for the surgical procedure from the patient's body (e.g., the cannula), and closing the incision. The sixth and final step can include determining the indications and outcomes of the procedure (e.g., if the procedure was successful, such as successfully relieving pain or repairing the fracture). In some cases, navigation techniques can be used that allow real-time visualization of the anatomy corresponding to the area of interest relative to the preoperative plan. Additionally, the navigation can provide visibility for closing the loop during the execution of the preoperative plan.

[0052] It should be understood that the surgical procedures described herein may not include all of the above steps, or may include additional steps not listed. More generally, the surgical hand procedures may include preoperative planning for the surgical procedure to ensure that all the correct surgical instruments and disposable supplies are ready for the surgical procedure, working with radiological imaging to ensure the correct scan format is used, and resolving any communication issues using an integrated third-party system. Additionally, the surgical procedures may include preoperative time to provide system setup and functional verification for the surgical procedure, intraoperative time to troubleshoot and resolve equipment and instrument issues and to provide real-time guidance and training to the operating room staff, and postoperative time to inspect and load any equipment used during the procedure and to review case issues with the operating room staff.

[0053] In any example of the surgical procedures described herein, the surgeon or other healthcare provider must select the appropriate and correct medical device before and during the execution of the surgical procedure. For example, the surgeon or other healthcare provider may determine the appropriate and correct medical device based on the disease state of a given patient, which may depend on various factors such as angle, location, depth, degree of deterioration, mass size, etc. Once the target area and disease state are identified and determined, the surgeon can manually select the surgical instrument to perform the corresponding surgical procedure.

[0054] However, a large number of surgical instruments are available for performing surgical procedures. For example, more than 200 surgical instruments are available for spinal surgery, and more than 100 surgical instruments are available for cranial surgery. Subsequently, the surgeon or other healthcare provider may spend an excessive amount of time (e.g., up to 20% of the entire surgical procedure time, which in some cases may equal approximately 40 to 60 minutes) manually selecting the surgical instruments. Additionally, many of the surgical instruments may have more than one tip or other interchangeable parts, where the surgeon or other healthcare provider must select the tip or other interchangeable part that fits into the verification groove of the corresponding surgical instrument, and then the surgeon or other healthcare provider must manually verify the surgical instrument. The process of verifying the interchangeable parts for the corresponding surgical instrument can be performed for each instrument.

[0055] Additionally, the surgical instruments may not be well marked for being blindly manually picked up during the performance of the surgical operation, thus affecting the ability of the surgeon or other healthcare provider to quickly and effectively pick up the correct surgical instrument. Therefore, planning the surgical operation in an optimized manner may become cumbersome for the surgeon to perform, resulting in a longer surgical procedure planning and execution.

[0056] As described herein, a machine learning model (e.g., an artificial intelligence (AI)-based learning model or algorithm) is provided for recommending and / or automatically loading surgical instruments required for a surgical procedure tray to reduce surgical procedure time. For example, the machine learning model can be used to provide recommendations for a surgical procedure plan, provide recommendations for instrument selection, and / or automatically load surgical instruments in sequence based on various historical parameters of previously performed surgical procedures. In some embodiments, the machine learning models and associated techniques described herein can provide an effective way to automatically recommend appropriate surgical instruments for a particular procedure to save surgeon time and generally reduce the time for the corresponding procedure.

[0057] That is, recommendations for surgical tools, instruments, implants, etc. and procedure recommendations can be provided based on historical data of previously performed surgical procedures. For example, for a previously performed surgical procedure, the historical data of the previously performed surgical procedure can include procedure and instrument flow and anomalies (e.g., which surgical instruments were used, the order in which the surgical instruments were used, any anomalies present, etc.), radiological images and annotations for the surgical procedure (e.g., MRI scans, CT scans, X-rays, etc.), demographic information of the corresponding patient, instrument availability information (e.g., hospital available inventory data indicating which surgical instruments are available), three-dimensional (3D) anatomy model-driven image analysis (e.g., powered by an AI-based learning model), or a combination thereof. Subsequently, a surgical instrument plan, preview, and / or automatic loading of a surgical instrument tray can be provided in the order of the instruments to be used during the surgical procedure (e.g., taking into account anomalies to reduce the surgical time of an image-guided surgical procedure).

[0058] Embodiments of the present disclosure provide solutions to one or more of the following problems: (1) extended surgical procedure duration, (2) increased patient exposure to anesthesia and / or radiation, and (3) higher likelihood of misdiagnosis or incorrect performance of a surgical procedure. For example, the techniques described herein can shorten the instrument selection process for a surgical procedure, which results in a shorter procedure duration, can reduce the patient's anesthesia dose and time, reduce radiation exposure (e.g., to confirm implant positioning), and facilitate faster recovery. Additionally, these techniques can be driven by an intelligent learning model that is normalized and optimized to meet clinical needs, thereby reducing the likelihood of misdiagnosis, and considering the complexity of the surgical procedure, the patient can benefit both in terms of time and cost.

[0059] In addition, predictive diagnostic decision-making can benefit surgeons by providing a clear planned path and can provide surgeons with the opportunity to explore various options at the planning level, which can reduce unforeseen surprises during surgery. Automatic evaluation of historical parameters extracted from procedures can also provide an excellent solution for analyzing historical procedures and drawing inferences with clear insights. Automatic instrument recommendations can eliminate the cognitive burden during operating theater (OT) setup and preoperative planning and can provide a faster workflow transition (e.g., to a navigation task). In some embodiments, the machine learning models provided herein can utilize 3D model-driven correlations to consider all aspects of the anatomical region of interest to correctly analyze key structures (e.g., including any deformities) before providing instrument recommendations that best fit a given surgical scenario.

[0060] Figure 1 FIG. 4 is a block diagram of a system 100 according to at least one embodiment of the present disclosure. The system 100 can include one or more inputs 102 that are used by a processor 104 to generate one or more outputs 106. The processor 104 can be a computing device or part of a different device. Additionally, the processor 104 can be any processor described herein or any similar processor. The processor 104 can be configured to execute instructions or data stored in a memory that can cause the processor 104 to perform one or more computational steps using or based on the inputs 102 to generate the outputs 106.

[0061] As described herein, the inputs 102 can include a set of surgical parameters 108 for a surgical procedure for a patient. For example, the set of surgical parameters 108 can include patient demographics, one or more radiological images, pathological data, or a combination thereof.

[0062] Subsequently, the processor 104 may use the set of surgical parameters 108 to predict the exact disease state of the patient and the surgical procedure based on a machine learning model 110 (e.g., a machine learning algorithm, an AI-based algorithm or model, etc.). For example, for each of the previously performed surgical procedures, the machine learning model 110 may be created based on the available historical data of the previously performed surgical procedures, which includes procedure and instrument flow and anomalies (e.g., which surgical instruments were used, the order of using surgical instruments, any anomalies present, etc.), radiological images and annotations (e.g., MRI scans, CT scans or images, X-rays, etc.), demographic information of the patients who underwent the previously performed surgical procedures, 3D anatomical models (e.g., indicating the angles, positions, dimensions, etc. of the previously performed surgical procedures), instrument availability information (e.g., hospital available inventory data indicating which surgical instruments are available for use), or combinations thereof. In some embodiments, the machine learning model 110 may be continuously improved based on the continuous feedback from the surgeon after the completion of the surgical procedure.

[0063] In some embodiments, the processor 104 may use the machine learning model 110 to compare the set of surgical parameters 108 with the available historical data. Based on the comparison using the machine learning model 110, the processor 104 may generate a list of the various closest matching surgical procedures (e.g., relative to the surgical procedure that provided the set of surgical parameters 108) to be displayed to the surgeon (e.g., or other healthcare provider). The closest matching surgical procedures may be compared with the surgical procedure that provided the set of surgical parameters 108 and compared with each other using a similarity index, which includes positional coordinate correlation (e.g., surgical anatomical location, implant location, etc.), deformation coefficient, demographic similarity (e.g., body mass index (BMI) and / or other demographic information for the associated patient), 3D model similarity, inventory stock match (e.g., whether the surgical instruments available for the surgical procedure are the same as the surgical instruments available and used in the closest matching surgical procedure), or combinations thereof. In some examples, different similarity index values of different components of the similarity index for each of the closest matching surgical procedures may be displayed to the surgeon to indicate how similar each individual component in the individual component is between the surgical procedure and the closest matching surgical procedure. Additionally or alternatively, an overall similarity index may be displayed, which indicates how similar the surgical procedure is relative to each of the most matching surgical procedures.

[0064] Subsequently, based on the similarity index, the surgeon can select the closest possible previously performed surgical procedure, which will assist in mapping and identifying the surgical workflow. As part of the identified surgical workflow, the processor 104 can provide surgical instrument recommendations 112 as part of the output 106 (e.g., recommend which surgical instruments to use based on which surgical instruments were used in the selected closest possible previously performed surgical procedure). For example, the processor 104 can display (e.g., via a user interface) recommendations for loading surgical instruments into a surgical tray for the surgeon to perform the surgical protocol. Based on the selected closest possible previously performed surgical procedure, the processor 104 can also recommend what the patient's position should be.

[0065] Based on these inputs (e.g., surgical workflow, surgical instrument recommendations 112, patient's position, etc.), the surgeon can edit or accept the plan corresponding to the closest possible previously performed surgical procedure. In some embodiments, after confirming and / or editing the plan, the processor 104 can pre-load the surgical instruments (e.g., from within a surgical instrument repository storing multiple surgical instruments), and place the surgical instruments in the surgical tray for the surgeon to use in the surgical protocol. Additionally or alternatively, the processor 104 can provide only an output (e.g., surgical instrument recommendations 112) indicating which surgical instruments to place or load into the surgical tray. The surgeon can then complete the surgical procedure and provide feedback back to the machine learning model 110 as part of a feedback loop, which will help further mature the machine learning model 110.

[0066] Thus, as described herein, the machine learning model 110 can be developed based on surgical procedure parameters 108 (e.g., patient input data such as radiological and physiological images and data) and previous surgical data of similar procedures (e.g., stored in a database), where for each similar procedure in the similar procedures, the previous surgical data can include 3D models, angles and positions, depths and sizes of implants, annotations, treatment plans, radiological diagnostic imaging, implants used relative to the 3D model, or combinations thereof, for each similar procedure. The processor 104 can then use the machine learning model 110 to recommend one or more surgical plans to the surgeon based on the similar surgical data, including the patient's disease state (e.g., angles, depths, and positions of the target area). In some examples, the recommended surgical plan can be recommended or displayed to the surgeon in a 3D model view.

[0067] Based on similar procedures and which surgical instruments are available at the hospital where the surgical procedure is to be performed (e.g., the hospital inventory of surgical instruments), the processor 104 can present to the surgeon one or more similarity indices that indicate the closest match of the available previous surgical procedures to the surgical procedure to be performed. For example, the similarity index can be the percentage of how close different aspects of each similar procedure are to the surgical procedure to be performed, such as the percentage similarity of degradation between surgical procedures, location similarity, percentage of implant depth, disease correlation, or other comparable aspects between the similar procedure and the surgical procedure to be performed.

[0068] Based on the similarity index, the surgeon can select one of the similar procedures to follow. In some embodiments, after making the selection, the surgeon may be able to make changes to the proposed surgical plan (e.g., based on the differences between the selected procedure and the surgical procedure to be performed). After confirming the surgical plan (e.g., any changes have been made), the processor 104 can provide a surgical instrument recommendation 112 to indicate which surgical instruments should be loaded onto the surgical tray to perform the surgical procedure (e.g., based on the availability of surgical instruments in the hospital inventory). In some embodiments, the processor can also automatically load the surgical tray with the selected surgical instruments in the order of the surgery (e.g., the order in which the surgical instruments will be used to perform the surgical procedure).

[0069] In some embodiments, the rendering of the patient's radiological images and the mapping to the machine learning model 110 (e.g., obtained from the set of surgical procedure parameters 108) can enable the processor 104 to display a first cutaway view for surgical planning. Then, the processor can also recommend the closest match of the previously performed surgical procedure based on a comparison of the cuts and / or incisions made for the previously performed surgical procedure and the first cutaway view, thereby allowing the surgeon to select between various options of the previously performed surgical procedure based on the similarity index and the visualization of the previously performed surgical procedure relative to the surgical procedure to be performed. Additionally, in some embodiments, the availability of the previous surgical procedure data and planning information can be used as training material for end users and employees.

[0070] Figure 2 is a diagram of a workflow 200 according to at least one embodiment of the present disclosure. In some examples, the workflow 200 can implement Figure 1 aspects of or can be implemented by these aspects. For example, the workflow 200 can be a more detailed view of the system 100, where the machine learning model uses inputs for the surgical procedure to determine a surgical plan and a surgical instrument recommendation based on historical data of previously performed surgical procedures. In some examples, the workflow 200 can be executed by a processor described herein (such as the processor 104 described with reference to Figure 1 ).

[0071] At operation 202 of workflow 200, one or more inputs for a given surgical procedure for a patient may be provided or received. For example, the one or more inputs may include demographic information for the patient, radiological and physiological images and data for the patient for the given surgical procedure, pathological data for the patient, or a combination thereof. At operation 204 of workflow 200, a predicted location and treatment for the patient and the given surgical procedure may be provided. At operation 206, a plan for the surgical procedure for the patient may be initiated. In some examples, the plan may be initiated or may be based on a machine learning model as described herein. For example, the machine learning model may include historical data 226 of previously performed surgical procedures (e.g., stored in a database or cloud database) or may be trained based on the historical data, including surgical procedure data 224.

[0072] Subsequently, workflow 200 may perform operation 208 to perform a comparison between the given surgical procedure and the historical data 226 of previously performed surgical procedures. At operation 210, the processor may display (e.g., via a user interface) and list the closest procedure match from the previously performed surgical procedures that is most similar to the given surgical procedure to be performed. In some embodiments, the processor may display a similarity index that indicates how similar each of the previously performed surgical procedures is to the given surgical procedure to be performed and / or how similar different aspects of the previously performed surgical procedures are to the corresponding aspects of the given surgical procedure to be performed.

[0073] At operation 212 of workflow 200, one of the closest procedure matches may be selected based on the similarity index (e.g., by a surgeon or other healthcare provider). Based on the selected closest procedure, at operation 214, the processor may preview and display to the surgeon (e.g., via a user interface) the surgical plan and surgical instrument recommendations. In some embodiments, the surgical plan may include the disease state of the patient, such as the angle, depth, and location of the target area to be entered into the patient's body as part of the surgical procedure. Additionally, at operation 216, the processor may recommend a position for the patient for performing the given surgical procedure (e.g., to relieve the load on the target area, fractured bone, etc.), such as on their side, on their stomach, etc.

[0074] At operation 218 of workflow 200, the surgeon may edit and / or accept a surgical plan based on the selected closest protocol. For example, after making a selection, the surgeon may be able to make changes to the proposed surgical plan (e.g., based on differences between the selected procedure and the surgery to be performed, differences between the patient for a given surgical protocol and the patient for the selected protocol, etc.). At operation 220, the processor may provide an output indicating surgical instruments to be loaded into a surgical tray for performing a given surgical protocol for a patient based on the selected closest protocol and / or changes made to the proposed surgical plan. In some embodiments, the processor may automatically load the instrument tray with surgical tools (e.g., for performing the surgical sequence of a surgical protocol). Additionally or alternatively, the processor may display the surgical instruments for the surgeon to load into the surgical tray.

[0075] At operation 222, the surgeon may use the surgical instruments that are proposed, displayed, and / or automatically loaded based on the selected closest protocol and the proposed surgical plan to perform and complete the surgical protocol. After completing the surgical protocol, the surgeon may provide feedback to the machine learning model (e.g., which surgical tools were used or not used, performance data of the proposed surgical plan, additional data, etc.) to further train and / or update the machine learning model. In some examples, the feedback may include surgical procedure data 224 for the completed surgical protocol, such as 3D models, angles and positions, dimensions, annotations, treatment plans, radiological diagnostic imaging, implants used relative to the 3D model, etc. of the surgical procedure to reach the target area of the patient for the surgical protocol. The surgical procedure data 224 may also include similar information from historical data 226 for previously performed surgical procedures.

[0076] The historical data 226 and the surgical procedure data 224 may be used to train the machine learning model in a continuous feedback loop at operation 228 to mature the machine learning model and continuously refine the machine learning model (e.g., including performing validation and testing of the machine learning model). Thus, after training based on the historical data 226 and the surgical procedure data 224, a machine learning model may be created and updated at operation 230 of workflow 200.

[0077] Go to Figure 3, which shows a block diagram of a system 300 according to at least one embodiment of the present disclosure. The system 300 can be used to recommend a surgical plan and / or surgical instrument selection for performing a surgical procedure. The system 300 includes a computing device 302, one or more imaging devices 312, a robot 314, a navigation system 318, a database 330, and / or a cloud or other network 334. Systems according to other embodiments of the present disclosure may include more or fewer components than the system 300. For example, the system 300 may not include one or more components of the imaging device 312, the robot 314, the navigation system 318, the computing device 302, the database 330, and / or the cloud 334.

[0078] The computing device 302 includes a processor 304, a memory 306, a communication interface 308, and a user interface 310. Computing devices according to other embodiments of the present disclosure may include more or fewer components than the computing device 302.

[0079] The processor 304 of the computing device 302 can be any processor described herein or any similar processor. The processor 304 can be configured to execute instructions stored in the memory 306, which can cause the processor 304 to perform one or more computational steps using or based on data received from the imaging device 312, the robot 314, the navigation system 318, the database 330, and / or the cloud 334.

[0080] The memory 306 can be or include RAM, DRAM, SDRAM, other solid-state memory, any memory described herein, or any other tangible non-transitory memory for storing computer-readable data and / or instructions. The memory 306 can store information or data for completing any steps of, for example, the methods 400, 500, and / or 600 described herein or any other method. The memory 306 can store instructions and / or machine learning models, for example, that support one or more functions of the robot 314. For example, the memory 306 can store content (e.g., instructions and / or machine learning models) that, when executed by the processor 304, enables surgical plan determination 320, surgical plan selection 322, surgical instrument determination 324, and / or surgical instrument output 328.

[0081] The surgical plan determination 320 enables the processor 304 to receive a set of inputs for a surgical procedure for a patient and to determine one or more potential plans for the surgical procedure at least in part based on the set of inputs and a machine learning model. For example, a set of inputs for a surgical procedure can include patient demographic data, one or more radiological images, pathological data, or a combination thereof. Additionally, one or more potential plans can be determined at least in part based on the disease state corresponding to the surgical procedure for the patient.

[0082] In some embodiments, surgical planning determination 320 enables the processor 304 to compare a set of inputs for a surgical procedure with historical data of previously performed surgical procedures to determine one or more potential plans based at least in part on a similarity index. For example, for a previously performed surgical procedure, the historical data of the previously performed surgical procedure may include procedure and instrument flow and anomalies, radiological images and annotations, demographic information, three-dimensional anatomical models, angles, positions, dimensions, implants used relative to the three-dimensional model, instrument availability information, treatment plans, or a combination thereof. Additionally, the similarity index may include position coordinate correlation, deformation coefficient, demographic information, three-dimensional model, inventory match of available surgical instruments, or a combination thereof.

[0083] Surgical plan selection 322 enables the processor 304 to receive a selection of a plan from one or more potential plans. Additionally, surgical plan selection 322 enables the processor 304 to display (e.g., via the user interface 310) one or more similarity index values for each of the one or more potential plans, wherein the selection of the plan is at least partially based on the similarity index values. In some embodiments, surgical plan selection 322 enables the processor 304 to provide position suggestions for the surgical procedure for the patient corresponding to the plan based on the selection.

[0084] In some embodiments, surgical plan selection 322 enables the processor 304 to provide a cutaway view of the surgical procedure for the patient based at least in part on a set of inputs and to provide one or more cutaway views for one or more potential plans based at least in part on historical surgical data corresponding to the one or more potential plans, wherein the selection of the plan from the one or more potential plans is received based at least in part on a comparison of the cutaway view of the surgical procedure with the one or more cutaway views of the one or more potential plans.

[0085] Surgical instrument determination 324 enables the processor 304 to determine a plurality of surgical instruments corresponding to the plan based on the selection. In some embodiments, surgical instrument determination 324 enables the processor 304 to receive one or more changes to the selected plan and may determine the plurality of surgical instruments based at least in part on the one or more changes.

[0086] The surgical instrument output 328 enables the processor 304 to provide an output indicative of a plurality of surgical instruments to be loaded into a surgical tray. For example, the output indicative of the plurality of surgical instruments to be loaded into the surgical tray may include an order (e.g., a surgical order) for loading the plurality of surgical instruments into the surgical tray. In some embodiments, the surgical instrument output 328 enables the processor 304 to display (via the user interface 310) a recommendation for loading the plurality of surgical instruments into the surgical tray. Additionally or alternatively, the surgical instrument output 328 enables the processor 304 to load the plurality of surgical instruments into the surgical tray. In some embodiments, the surgical instrument output 328 enables the processor 304 to receive feedback at least in part based on the output indicative of the plurality of surgical instruments to be loaded into the surgical tray after performing a surgical procedure on a patient. Thus, the feedback may be used in part to train a machine learning model.

[0087] In some embodiments, if provided as instructions, the content stored in the memory 306 may be organized into one or more application software, modules, packages, layers, or engines. Alternatively or additionally, the memory 306 may store other types of content or data (e.g., machine learning models, artificial neural networks, deep neural networks, etc.) that may be processed by the processor 304 to perform the various methods and features described herein. Thus, although the various content of the memory 306 may be described as instructions, it should be understood that the functions described herein may be implemented by using instructions, algorithms, and / or machine learning models. The data, algorithms, and / or instructions may cause the processor 304 to manipulate the data stored in the memory 306 and / or data received from or via the imaging device 312, the robot 314, the database 330, and / or the cloud 334.

[0088] The computing device 302 may also include a communication interface 308. The communication interface 308 can be used to receive image data or other information from external sources (such as an imaging device 312, a robot 314, a navigation system 318, a database 330, a cloud 334, and / or any other system or component that is not part of the system 300), and / or to send instructions, images, or other information to an external system or device (e.g., another computing device 302, an imaging device 312, a robot 314, a navigation system 318, a database 330, a cloud 334, and / or any other system or component that is not part of the system 300). The communication interface 308 may include one or more wired interfaces (e.g., USB ports, Ethernet ports, FireWire ports) and / or one or more wireless transceivers or interfaces (configured to send and / or receive information via one or more wireless communication protocols such as 802.11a / b / g / n, Bluetooth, NFC, ZigBee, etc.). In some embodiments, the communication interface 308 can be used to enable the device 302 to communicate with one or more other processors 304 or computing devices 302, whether to reduce the time required to complete computationally intensive tasks or for any other reason.

[0089] The computing device 302 may also include one or more user interfaces 310. The user interface 310 can be or include a keyboard, a mouse, a trackball, a monitor, a television, a screen, a touch screen, and / or any other device for receiving information from a user and / or for providing information to a user. The user interface 310 can be used, for example, to receive user selections or other user inputs regarding any step of any method described herein. Nevertheless, any required input for any step of any method described herein can be automatically generated by the system 300 (e.g., by the processor 304 or another component of the system 300) or received by the system 300 from a source external to the system 300. In some embodiments, the user interface 310 can be used to allow a surgeon or other user to modify the instructions to be executed by the processor 304 and / or to modify or adjust the settings of other information displayed on or corresponding to the user interface 310 in accordance with one or more embodiments of the present disclosure.

[0090] Although the user interface 310 is shown as part of the computing device 302, in some embodiments, the computing device 302 can utilize a user interface 310 that is separately housed from one or more of the remaining components of the computing device 302. In some embodiments, the user interface 310 can be located near one or more of the other components of the computing device 302, while in other embodiments, the user interface 310 can be located away from one or more of the other components of the computing device 302.

[0091] The imaging device 312 can be used to image anatomical features (e.g., bones, veins, tissues, etc.) and / or other aspects of the patient's anatomy to generate image data (e.g., image data depicting or corresponding to bones, veins, tissues, etc.). As used herein, "image data" refers to data generated or captured by the imaging device 312, including data in machine-readable form, graphical / visual form, and any other form. In different examples, the image data can include data corresponding to an anatomical feature portion of the patient or a part thereof. The image data can be or include preoperative images, intraoperative images, postoperative images, or images taken independent of any surgical procedure. In some embodiments, the first imaging device 312 can be used to obtain first image data (e.g., a first image) at a first time, and the second imaging device 312 can be used to obtain second image data (e.g., a second image) at a second time after the first time. The imaging device 312 may be capable of taking 2D images or 3D images to generate image data. The imaging device 312 can be or include, for example, an ultrasound scanner (which may include, for example, physically separate transducers and receivers, or a single ultrasound transceiver), an O-arm, a C-arm, a G-arm, or any other device utilizing X-ray-based imaging (e.g., a fluoroscope, a CT scanner, or other X-ray machine), a magnetic resonance imaging (MRI) scanner, an optical coherence tomography (OCT) scanner, an endoscope, a microscope, an optical camera, a thermal imaging camera (e.g., an infrared camera), a radar system (which may include, for example, a transmitter, a receiver, a processor, and one or more antennas), or any other imaging device 312 suitable for obtaining an image of an anatomical feature portion of the patient. The imaging device 312 can be fully contained within a single housing, or can include a transmitter / transmitter and a receiver / detector located in separate housings or otherwise physically separated.

[0092] In some embodiments, the imaging device 312 can include more than one imaging device 312. For example, the first imaging device can provide first image data and / or a first image, and the second imaging device can provide second image data and / or a second image. In still other embodiments, the same imaging device can be used to provide both first image data and second image data and / or any other image data described herein. The imaging device 312 can be used to generate an image data stream. For example, the imaging device 312 can be configured to operate using an open shutter, or using a shutter that continuously alternates between open and closed, in order to capture consecutive images. For the purposes of this disclosure, unless otherwise specified, if the image data represents two or more frames per second, the image data can be considered continuous and / or provided as an image data stream.

[0093] The robot 314 can be any surgical robot or surgical robot system. The robot 314 can be or include, for example, a Mazor XTM Stealth robotic guidance system. The robot 314 can be configured to position the imaging device 312 at one or more precise positions and orientations, and / or to return the imaging device 312 to the same position and orientation at a later time point. The robot 314 can additionally or alternatively be configured to manipulate surgical tools (whether or not guided by the navigation system 318) to complete or assist with surgical tasks. In some embodiments, the robot 314 can be configured to hold and / or manipulate anatomical elements during or in conjunction with a surgical procedure. The robot 314 can include one or more robotic arms 316. In some embodiments, the robotic arm 316 can include a first robotic arm and a second robotic arm, but the robot 314 can include more than two robotic arms. In some embodiments, one or more of the robotic arms 316 can be used to hold and / or manipulate the imaging device 312. In embodiments where the imaging device 312 includes two or more physically separate components (e.g., a transmitter and a receiver), one robotic arm 316 can hold one such component, and another robotic arm 316 can hold another such component. Each robotic arm 316 can be positioned independently of the other robotic arms. The robotic arms 316 can be controlled in a single shared coordinate space or in separate coordinate spaces.

[0094] The robot 314 together with the robotic arms 316 can have, for example, one, two, three, four, five, six, seven or more degrees of freedom. Additionally, the robotic arms 316 can be positioned or locatable in any pose, plane, and / or focus. This pose includes position and orientation. Thus, the imaging device 312, surgical tool, or other object held by the robot 314 (or more specifically, by the robotic arm 316) can be precisely positioned at one or more desired and specific positions and orientations.

[0095] The robotic arm 316 can include one or more sensors that enable the processor 304 (or the processor of the robot 314) to determine the precise pose of the robotic arm (and any object or element held or attached to the robotic arm) in space.

[0096] In some embodiments, reference markers (i.e., navigation markers) may be placed on the robot 314 (including, for example, on the robotic arm 316), on the imaging device 312, or on any other object in the surgical space. The reference markers may be tracked by the navigation system 318, and the results of the tracking may be used by the robot 314 and / or by an operator of the system 300 or any of its components. In some embodiments, the navigation system 318 may be used to track other components of the system (e.g., the imaging device 312), and the system may be operated without using the robot 314 (e.g., a surgeon may manually manipulate the imaging device 312 and / or one or more surgical tools based, for example, on information and / or instructions generated by the navigation system 318).

[0097] During operation, the navigation system 318 may provide navigation for the surgeon and / or the surgical robot. The navigation system 318 may be any known or future-developed navigation system, including, for example, the Medtronic StealthStation TM S8 surgical navigation system or any of its successors. The navigation system 318 may include one or more cameras or other sensors for tracking one or more reference markers, navigation trackers, or other objects in the operating room or other room in which part or all of the system 300 is located. The one or more cameras may be optical cameras, infrared cameras, or other cameras. In some embodiments, the navigation system 318 may include one or more electromagnetic sensors. In various embodiments, the navigation system 318 may be used to track the position and orientation (e.g., pose) of the imaging device 312, the robot 314, and / or the robotic arm 316 and / or one or more surgical tools (or more specifically, to track the pose of a navigation tracker directly or indirectly attached to one or more of the foregoing in a fixed relationship). The navigation system 318 may include a display for displaying one or more images from an external source (e.g., the computing device 302, the imaging device 312, or other source) or for displaying images and / or video streams from one or more cameras or other sensors of the navigation system 318. In some embodiments, the system 300 may be operated without using the navigation system 318. The navigation system 318 may be configured to provide guidance to the surgeon or other users of the system 300 or its components, to the robot 314, or to any other element of the system 300 regarding, for example, the pose of one or more anatomical elements, whether a tool is in the proper trajectory, and / or how to move the tool into the proper trajectory to perform a surgical task according to a preoperative or other surgical plan.

[0098] The database 330 may store information associating one coordinate system to another (e.g., associating one or more robotic coordinate systems to a patient coordinate system and / or a navigation coordinate system). The database 330 may additionally or alternatively store, for example, one or more surgical plans (including, for example, pose information about a target and / or image information about the anatomy of the patient at and / or near the surgical site, for use by the robot 314, the navigation system 318, and / or the user of the computing device 302 or the system 300); one or more images that may be used in connection with a surgical procedure performed by or with the assistance of one or more other components of the system 300; and / or any other useful information. The database 330 may be configured to provide any such information to the computing device 302 or to any other device external to the system 300 or any other device of the system 300, either directly or via the cloud 334. In some embodiments, the database 330 may be or include a part of a hospital image storage system, such as a Picture Archiving and Communication System (PACS), a Health Information System (HIS), and / or another system for collecting, storing, managing, and / or transmitting electronic medical records including image data.

[0099] The cloud 334 may be or represent the Internet or any other wide area network. The computing device 302 may be connected to the cloud 334 using a wired connection, a wireless connection, or both via the communication interface 308. In some embodiments, the computing device 302 may communicate with the database 330 and / or an external device (e.g., a computing device) via the cloud 334.

[0100] The system 300 or a similar system may be used, for example, to implement one or more aspects of any of the methods 400, 500, and / or 600 described herein. The system 300 or a similar system may also be used for other purposes.

[0101] Figure 4 A method 400 is depicted that may be used, for example, to implement a proposed surgical procedure plan and surgical instrument selection.

[0102] Method 400 (and / or one or more of its steps) may be implemented or otherwise performed, for example, by at least one processor. The at least one processor may be the same as or similar to processor 304 of computing device 302 described above. The at least one processor may be part of a robot (such as robot 314) or part of a navigation system (such as navigation system 318). Processors other than any of the processors described herein may also be used to perform method 400. The at least one processor may perform method 400 by executing elements stored in a memory (such as memory 306). The elements stored in the memory and executed by the processor may cause the processor to perform one or more of the steps of the functions shown in method 400. One or more portions of method 400 may be performed by a processor that executes any of the content in the memory (such as surgical plan determination 320, surgical plan selection 322, surgical instrument determination 324, and / or surgical instrument output 328).

[0103] Method 400 includes receiving a set of inputs for a surgical procedure for a patient (step 404). For example, the set of inputs for the surgical procedure may include patient demographics, one or more radiological images, pathology data, or a combination thereof.

[0104] Method 400 further includes determining one or more potential plans for the surgical procedure based at least in part on the set of inputs (step 408). In some embodiments, the one or more potential plans are determined based at least in part on a disease state corresponding to the surgical procedure for the patient.

[0105] Method 400 further includes receiving a selection of a plan from the one or more potential plans (step 412). In some embodiments, a location recommendation corresponding to the plan may be provided for the surgical procedure for the patient according to the selection. Additionally, a cutaway view of the surgical procedure for the patient may be provided based at least in part on the set of inputs, and one or more cutaway views for the one or more potential plans may also be provided based at least in part on historical surgical data corresponding to the one or more potential plans. Thus, in some embodiments, the selection of a plan from the one or more potential plans may be received based at least in part on a comparison of the cutaway view of the surgical procedure with the one or more cutaway views for the one or more potential plans.

[0106] Method 400 further includes determining a plurality of surgical instruments corresponding to the plan according to the selection (step 416). In some embodiments, one or more changes to the plan selected from the selection may be received, and the plurality of surgical instruments may be determined based at least in part on the one or more changes.

[0107] Method 400 also includes providing an output indicating a plurality of surgical instruments to be loaded into a surgical tray (step 420). In some examples, the output indicating the plurality of surgical instruments to be loaded into the surgical tray can include the order (e.g., surgical order) for loading the plurality of surgical instruments into the surgical tray. In some embodiments, providing the output can include displaying, via a user interface, a recommendation of the plurality of surgical instruments to be loaded into the surgical tray. Additionally or alternatively, providing the output can include loading the plurality of surgical instruments into the surgical tray.

[0108] The present disclosure encompasses embodiments of method 400 that include more or fewer steps and / or one or more steps different from those described above.

[0109] Figure 5 Method 500 is depicted that can be used to, for example, compare a given surgical procedure with a previously performed surgical procedure.

[0110] Method 500 (and / or one or more of its steps) can be implemented or otherwise executed, for example, by at least one processor. The at least one processor can be the same as or similar to processor 304 of computing device 302 described above. The at least one processor can be part of a robot (such as robot 314) or part of a navigation system (such as navigation system 318). Processors other than any of the processors described herein can also be used to execute method 500. The at least one processor can execute method 500 by executing elements stored in a memory (such as memory 306). The elements stored in the memory and executed by the processor can cause the processor to execute one or more of the functions shown in method 500. One or more portions of method 500 can be executed by a processor that executes any of the content in the memory (such as surgical plan determination 320, surgical plan selection 322, surgical instrument determination 324, and / or surgical instrument output 328).

[0111] Method 500 includes receiving a set of inputs for a surgical procedure for a patient (step 504). Method 500 also includes determining, at least in part based on the set of inputs, one or more potential plans for the surgical procedure (step 508). Steps 504 and 508 can respectively implement similar aspects of steps 404 and 408 as described with reference to Figure 4 those described above.

[0112] In some embodiments, as described herein, one or more potential plans may also be determined at least in part based on a machine learning model. For example, method 500 further includes comparing a set of inputs for a surgical procedure with historical data of previously performed surgical procedures (e.g., based on a machine learning model) to determine one or more potential plans at least in part based on a similarity index (step 512). In some embodiments, for a previously performed surgical procedure, the historical data of the previously performed surgical procedure may include procedure and instrument flow and anomalies, radiological images and annotations, demographic information, three-dimensional anatomical models, angles, positions, dimensions, implants used relative to the three-dimensional model, instrument availability information, treatment plans, or a combination thereof. Additionally, the similarity index may include position coordinate correlation, deformation coefficient, demographic information, three-dimensional model, inventory match of available surgical instruments, or a combination thereof.

[0113] Method 500 further includes receiving a selection of a plan from one or more potential plans (step 516). Step 516 may implement aspects of step 412 as described with reference to Figure 4 In addition, in some embodiments, one or more similarity index values for each of the one or more potential plans may be displayed (e.g., via a user interface), and the selection of a plan may be received at least in part based on the one or more similarity index values.

[0114] Method 500 further includes determining a plurality of surgical instruments corresponding to the plan based on the selection (step 520). Method 500 further includes providing an output indicating the plurality of surgical instruments to be loaded into a surgical tray (step 524). Steps 520 and 524 may represent similar aspects of steps 416 and 420 as described with reference to Figure 4 In addition, in some embodiments, one or more similarity index values for each of the one or more potential plans may be displayed (e.g., via a user interface), and the selection of a plan may be received at least in part based on the one or more similarity index values.

[0115] The present disclosure encompasses embodiments of method 500 that include more or fewer steps and / or one or more steps different from those described above.

[0116] Figure 6 Method 600 is depicted, which may be used, for example, to update a machine learning model to recommend a surgical plan and surgical instrument selection based on a feedback loop.

[0117] Method 600 (and / or one or more of its steps) may be implemented or otherwise performed, for example, by at least one processor. The at least one processor may be the same as or similar to processor 304 of computing device 302 described above. The at least one processor may be part of a robot (such as robot 314) or part of a navigation system (such as navigation system 318). Processors other than any of the processors described herein may also be used to perform method 600. The at least one processor may perform method 600 by executing elements stored in a memory (such as memory 306). The elements stored in the memory and executed by the processor may cause the processor to perform one or more steps of the functions shown in method 600. One or more portions of method 600 may be performed by a processor that executes any of the content in the memory (such as surgical plan determination 320, surgical plan selection 322, surgical instrument determination 324, and / or surgical instrument output 328).

[0118] Method 600 includes receiving a set of inputs for a surgical procedure for a patient (step 604). Method 600 further includes determining one or more potential plans for the surgical procedure based at least in part on the set of inputs (e.g., and as described herein with a machine learning model) (step 608). Method 600 further includes receiving a selection of a plan from the one or more potential plans (step 612). Method 600 further includes determining a plurality of surgical instruments corresponding to the plan based on the selection (step 616). Method 600 further includes providing an output indicating the plurality of surgical instruments to be loaded onto a surgical tray (step 620). Step 604, step 608, step 612, step 616, and step 620 may respectively represent steps 404, step 408, step 412, step 416, and step 420 as described with reference to Figure 4 and respectively represent steps 504, step 508, step 516, step 520, and step 524 as described with reference to Figure 5

[0119] Method 600 further includes, after performing the surgical procedure on the patient, receiving feedback based at least in part on the output indicating the plurality of surgical instruments to be loaded onto the surgical tray (step 624). In some embodiments, the feedback may be used in part to train the machine learning model.

[0120] The present disclosure encompasses embodiments of method 600 that include more or fewer steps and / or one or more steps different from those described above.

[0121] As described above, the present disclosure encompasses having more than Figure 4 、 Figure 5 and Figure 6 ​A method for identifying all steps with fewer steps (and corresponding descriptions of methods 400, 500, and 600), and a method including additional steps beyond the steps identified in Figure 4 , Figure 5 , and Figure 6 (and corresponding descriptions of methods 400, 500, and 600). The present disclosure also encompasses methods including one or more steps from one method described herein and one or more steps from another method described herein. Any correlation described herein may be or include registration or any other correlation. Figure 4 , Figure 5 and Figure 6 The foregoing is not intended to limit the present disclosure to one or more forms disclosed herein. In the foregoing detailed description, for example, for the purpose of simplifying the present disclosure, various features of the present disclosure are grouped together in one or more aspects, embodiments, and / or configurations. Features of aspects, embodiments, and / or configurations of the present disclosure may be combined in alternative aspects, embodiments, and / or configurations other than those discussed above. The methods of the present disclosure should not be construed as reflecting an intention that the claims require more features than those expressly recited in each claim. On the contrary, as reflected in the following claims, the inventive aspects lie in less than all of the features of a single foregoing disclosed aspect, embodiment, and / or configuration. Accordingly, the following claims are hereby incorporated into this detailed description, where each claim stands on its own as a separate preferred embodiment of the present disclosure.

[0122] In addition, although the foregoing has included a description of one or more aspects, embodiments, and / or configurations and certain variations and modifications, after understanding the present disclosure, other variations, combinations, and modifications are within the scope of the present disclosure, for example, within the skills and knowledge of those skilled in the art. It is intended to obtain rights to include alternative aspects, embodiments, and / or configurations within the scope permitted, including alternative, interchangeable, and / or equivalent structures, functions, scopes, or steps of those claimed, whether or not such alternative, interchangeable, and / or equivalent structures, functions, scopes, or steps are disclosed herein, and it is not intended to disclose any patentable subject matter.

[0123] ​

Claims

1. A system for suggesting a surgical plan and surgical instrument selection, the system comprising: a processor; and a memory that stores data for processing by the processor, the data when processed causing the processor to: receive a set of inputs for a surgical procedure for a patient; determine, at least in part based on the set of inputs and a machine learning model, one or more potential plans for the surgical procedure; receive a selection of a plan from the one or more potential plans; determine, based on the selection, a plurality of surgical instruments corresponding to the plan; and provide an output indicating the plurality of surgical instruments to be loaded onto a surgical tray.

2. The system according to claim 1, wherein the data stored in the memory that when processed causes the processor to provide the output indicating the plurality of surgical instruments to be loaded onto the surgical tray further causes the system to: display, via a user interface, a suggestion of the plurality of surgical instruments to be loaded onto the surgical tray.

3. The system according to claim 1, wherein the data stored in the memory that when processed causes the processor to provide the output indicating the plurality of surgical instruments to be loaded onto the surgical tray further causes the system to: load the plurality of surgical instruments onto the surgical tray.

4. The system according to claim 1, wherein the data stored in the memory that when processed causes the processor to determine the one or more potential plans for the surgical procedure further causes the system to: compare the set of inputs for the surgical procedure with historical data of previously performed surgical procedures to determine the one or more potential plans at least in part based on a similarity index.

5. The system according to claim 4, wherein the memory stores additional data for processing by the processor, the additional data when processed causing the processor to: display, via a user interface, one or more similarity indices for each of the one or more potential plans.

6. The system according to claim 4, wherein for the previously performed surgical procedure, the historical data of the previously performed surgical procedure includes: Procedures and instrument flows and anomalies, radiological images and annotations, demographic information, three-dimensional anatomical models, angles, positions, dimensions, implants used relative to the three-dimensional model, instrument availability information, treatment plans, or combinations thereof.

7. The system according to claim 4, wherein the similarity index includes: Position coordinate correlations, deformation coefficients, demographic information, three-dimensional models, inventory matching of available surgical instruments, or combinations thereof.

8. The system according to claim 1, wherein the memory stores additional data for processing by the processor, the additional data when processed causing the processor to: provide, based on the selection, position suggestions for the surgical procedure for the patient corresponding to the plan.

9. The system according to claim 1, wherein the memory stores additional data for processing by the processor, the additional data when processed causing the processor to: Receive one or more changes to the selected plan, wherein the plurality of surgical instruments to be loaded into the surgical tray are determined at least in part based on the one or more changes.

10. The system of claim 1, wherein the memory stores additional data for processing by the processor, the additional data when processed causing the processor to: Provide a cutting cross-section view of the surgical procedure for the patient at least in part based on the set of inputs; and Provide one or more cutting cross-section views for the one or more potential plans at least in part based on historical surgical data corresponding to the one or more potential plans, wherein the selection of the plan from the one or more potential plans is received at least in part based on a comparison of the cutting cross-section view of the surgical procedure with the one or more cutting cross-section views of the one or more potential plans.

11. The system of claim 1, wherein the memory stores additional data for processing by the processor, the additional data when processed causing the processor to: After performing the surgical procedure for the patient, receive feedback at least in part based on the output indicating the plurality of surgical instruments to be loaded into the surgical tray.

12. The system according to claim 11, wherein, The feedback is used in part to train the machine learning model.

13. The system of claim 1, wherein the one or more potential plans are determined at least in part based on a disease state corresponding to the surgical procedure for the patient.

14. The system according to claim 1, wherein the set of inputs for the surgical procedure comprises: Patient demographic data, one or more radiological images, pathological data, or a combination thereof.

15. The system of claim 1, wherein the output indicating the plurality of surgical instruments to be loaded into the surgical tray includes an order for loading the plurality of surgical instruments into the surgical tray.

16. A system for suggesting a surgical plan and surgical instrument selection, the system comprising: A processor; And A memory that stores data for processing by the processor, the data when processed causing the processor to: Receive a set of inputs for a surgical procedure for a patient; Determine one or more potential plans for the surgical procedure at least in part based on the set of inputs; Receive a selection of a plan from the one or more potential plans; Determine a plurality of surgical instruments corresponding to the plan based on the selection; And Provide an output indicating the plurality of surgical instruments to be loaded into a surgical tray.

17. The system of claim 16, wherein the data stored in the memory that when processed causes the processor to determine the one or more potential plans for the surgical procedure further causes the system to: Compare the set of inputs for the surgical procedure with historical data of previously performed surgical procedures to determine the one or more potential plans at least in part based on a similarity index.

18. The system according to claim 17, wherein the memory stores additional data for processing by the processor, and the additional data, when processed, causes the processor to: display, via a user interface, one or more similarity indices for each of the one or more potential plans.

19. A system for suggesting a surgical plan and a selection of surgical instruments, the system comprising: a processor; and a memory that stores data for processing by the processor, the data, when processed, causing the processor to: receive a set of inputs for a surgical procedure for a patient; determine, at least in part based on the set of inputs, one or more potential plans for the surgical procedure; receive a selection of a plan from the one or more potential plans; determine, based on the selection, a plurality of surgical instruments corresponding to the plan; and load the plurality of surgical instruments onto a surgical tray.

20. The system according to claim 19, wherein the plurality of surgical instruments are loaded onto the surgical tray according to an order for performing the surgical procedure for the patient, at least in part based on the plan from the selection.

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