COMPUTER-ASSISTED SURGICAL PLANNING

A method comprises obtaining, by a computing system, one or more surgeon preference parameters that specify values of one or more surgical parameters, wherein the surgical parameters include one or more positioning parameters for a glenoid implant to be attached to a glenoid fossa of a patient during a surgery; determining, by the computing system, based on one or more anatomic parameters of the patient and the surgeon preference parameters, one or more suggested surgical options, each of the surgical options corresponding to a. different combination of the positioning parameters for the glenoid implant and types of glenoid implant; and outputting, by the computing system, for display, the one or more suggested surgical options.

BACKGROUND

Shoulder replacement surgery is a complicated type of orthopedic surgery. However, shoulder replacement surgery is becoming increasingly common because of its ability to alleviate pain and restore range of motion in many patients. The complexity of shoulder replacement surgery has prevented many surgeons from performing shoulder replacement surgeries, especially those surgeons who do not frequently perform shoulder replacement surgeries. Accordingly, computerized surgical planning systems have been developed to help surgeons plan complicated surgeries, such as shoulder replacement surgeries.

SUMMARY

This disclosure describes a variety of techniques for improving computerized surgical planning systems. One challenge associated with implementing computerized surgical planning systems is how to ensure that a computerized surgical planning system generates surgical plans for shoulder replacement surgeries that are consistent with the preferences of individual surgeons. For example, a computerized surgical planning system may use a machine learning (ML) model to generate a plurality of predictions regarding various surgical options for a shoulder replacement surgery. In general, a large number of training datasets (e.g., data regarding individual surgeries) may need to be used to train the ML model. At least because of the number of cases required to train ML models, it may be impractical to train separate ML models for each individual surgeon to generate predictions based on the preferences of the individual surgeons. This may especially be the case with respect to surgeons who do not frequently perform shoulder replacement surgeries because such surgeons simply have not performed enough shoulder replacement surgeries to sufficiently train ML models. This disclosure describes techniques that may address this problem and allow a surgical planning system to generate surgical suggestions that accommodate preferences of individual surgeons, with the associated benefit of reduced storage requirements and reduced utilization of computing resources.

In one example, this disclosure describes a method comprising: obtaining, by a computing system, one or more surgeon preference parameters that specify values of one or more surgical parameters, wherein the surgical parameters include one or more positioning parameters for a glenoid implant to be attached to a glenoid fossa of a patient during a surgery; determining, by the computing system, based on one or more anatomic parameters of the patient and the surgeon preference parameters, one or more suggested surgical options, each of the surgical options corresponding to a different combination of the positioning parameters for the glenoid implant and types of the glenoid implant; and outputting, by the computing system, the one or more suggested surgical options.

In another example, this disclosure describes a computing system comprising: a memory configured to store one or more surgeon preference parameters that specify values of one or more surgical parameters, wherein the surgical parameters include one or more positioning parameters for a glenoid implant to be attached to a glenoid fossa of a patient during a surgery; one or more processors implemented in circuitry, the one or more processors configured to: determine, based on one or more anatomic parameters of the patient and the surgeon preference parameters, one or more suggested surgical options, each of the surgical options corresponding to a different combination of the positioning parameters for the glenoid implant and types of glenoid implant; and output, for display, the one or more suggested surgical options.

In other examples, this disclosure describes a computing system comprising means for performing the methods of this disclosure and a computer-readable storage medium having instructions stored thereon that when executed cause one or more processors of a computing system to perform the methods of this disclosure.

The details of various examples of the disclosure are set forth in the accompanying drawings and the description below. Various features, objects, and advantages will be apparent from the description, drawings, and claims.

DETAILED DESCRIPTION

Certain examples of this disclosure are described with reference to the accompanying drawings, wherein like reference numerals denote like elements. It should be understood, however, that the accompanying drawings illustrate only the various implementations described herein and are not meant to limit the scope of various technologies described herein.

The drawings show and describe various examples of this disclosure. In the following description, numerous details are set forth. However, it will be understood by those skilled in the art that the present invention may be practiced without these details and that numerous variations or modifications from the described examples may be possible.

This disclosure describes systems and methods associated with planning a surgery. In other words, this disclosure describes techniques for automated planning of surgeries. A surgical plan, e.g., a surgical plan generated by the BLUEPRINT T, system produced by Wright Medical Group NV or another surgical planning platform, may include a variety of information regarding a surgery. For example, a surgical plan may include information regarding steps to be performed on a patient by a user, such as a surgeon. Example steps may include, for example, bone or tissue preparation steps and/or steps for selection, modification and/or placement of implant components, such as prosthetics, and associated hardware or media. Furthermore, information in a surgical plan may include, in various examples, dimensions, shapes, angles, surface contours, and/or orientations of implant components to be selected or modified by users, dimensions, shapes, angles, surface contours and/or orientations to be defined in bone or tissue by the user in bone or tissue preparation steps, and/or positions, axes, planes, angle and/or entry points defining placement of implant components by the user relative to patient bone or tissue. Information such as dimensions, shapes, angles, surface contours, and/or orientations of anatomical features of the patient may be derived from analysis of imaging (e.g., x-ray. CT, MRI, ultrasound or other images), direct observation, or other techniques.

As described herein, a computing system may obtain one or more surgeon preference parameters that specify values of one or more surgical parameters. The surgical parameters may include one or more positioning parameters for a glenoid implant to be attached to a glenoid fossa of a patient during a surgery. Additionally, the computing system may determine, based on one or more anatomic parameters of the patient and the surgeon preference parameters, one or more suggested surgical options for attaching a glenoid implant to the glenoid fossa of the patient during the surgery. Each of the surgical options corresponds to a different combination of values of the surgical parameters. The computing system may output, for display, the one or more suggested surgical options.

FIG.1is a block diagram illustrating an example surgical assistance system100in accordance with one or more techniques of this disclosure. In the example ofFIG.1, surgical assistance system100includes a computing system102, which is an example of a computing system configured to perform one or more example techniques described in this disclosure. Computing system102may include various types of computing devices, such as server computers, personal computers, smartphones, tablet computers, laptop computers, and other types of computing devices. Computing system102includes processing circuitry104, memory106, a display108, and a communication interface110. Display108may be optional, such as in examples where computing system102comprises a server computer. Additionally, in the example ofFIG.1, surgical assistance system100includes a local device112and a communication network114.

Examples of processing circuitry104include one or more microprocessors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), hardware, or any combinations thereof. In general, processing circuitry104may be implemented as fixed-function circuits, programmable circuits, or a combination thereof. Fixed-function circuits refer to circuits that provide particular functionality and are preset on the operations that can be performed. Programmable circuits refer to circuits that can be programmed to perform various tasks and provide flexible functionality in the operations that can be performed. For instance, programmable circuits may execute software or firmware that cause the programmable circuits to operate in the manner defined by instructions of the software or firmware. Fixed-function circuits may execute software instructions (e.g., to receive parameters or output parameters), but the types of operations that the fixed-function circuits perform are generally immutable. In some examples, the one or more units may be distinct circuit blocks (fixed-function or programmable), and in some examples, the one or more units may be integrated circuits.

Processing circuitry104may include arithmetic logic units (ALUs), elementary function units (EFUs), digital circuits, analog circuits, and/or programmable cores, formed from programmable circuits. In examples where the operations of processing circuitry104are performed using software executed by the programmable circuits, memory106may store the object code of the software that processing circuitry104receives and executes, or another memory within processing circuitry104(not shown) may store such instructions. Examples of the software include software designed for surgical planning. Processing circuitry104may perform the actions ascribed in this disclosure to computing system102.

Memory106may store various types of data used by processing circuitry104. For example, memory106may store data regarding one or more surgical plans. Memory106may be formed by any of a variety of memory devices, such as dynamic random access memory (DRAM), including synchronous DRAM (SDRAM), magnetoresistive RAM (MRAM), resistive RAM (RRAM), hard disk drives, optical discs, or other types of non-transitory computer-readable media. Examples of display108may include a liquid crystal display (LCD), a plasma display, an organic light emitting diode (OLED) display, or another type of display device.

Furthermore, in the example ofFIG.1, memory106may include computer-readable instructions that, when executed by processing circuitry104, cause computing system102to provide a surgical planning system116. In some examples, some or all of the instructions of surgical planning system116are stored on local device112and/or executed by processing circuitry of local device112. In other examples, some or all of the instructions of surgical planning system116are stored on computing system102and/or executed by processing circuitry of computing system102. In some examples, local device112may be or may include a mixed reality (MR) visualization device. For ease of explanation, this disclosure may simply describe actions performed by computing system102and/or local device112when processing circuitry104and/or processing circuitry of local device112executes instructions of surgical planning system116as being performed by surgical planning system116, with the understanding that processing operations may be performed by processing circuitry of computing system102, local device112, or a combination both, or by or in combination with other processing circuitry, including processing circuitry associated with one or more cloud servers and/or one or more other remote computing devices. In the example ofFIG.1, memory106may also include surgical plan data117, medical imaging data119, and surgeon preference parameters121.

Communication interface110allows computing system102to output data and instructions to and receive data and instructions from local device112and/or other devices via a network114. Communication interface110may comprise hardware circuitry that enables computing system102to communicate (e.g., wirelessly or using wires) to other computing systems and devices, such as MR visualization device112. Network114may include various types of communication networks including one or more wide-area networks, such as the Internet, local area networks, and so on. In some examples, network114may include wired and/or wireless communication links.

Local device112may be a computing device used by a user118. In other examples, user118may directly use a computing device of computing system102. In such examples, user118may view content displayed on display108. In some examples, local device112is a personal computer, smartphone, tablet computer, laptop computer, or another type of computing device. In some examples, local device112is a mixed reality (MR) visualization device. An MR visualization device may use various visualization techniques to display image content to user118, who may be a surgeon. For instance, an MR visualization device may include a holographic projector or other type of device for presenting MR scenes. In some examples where local device112is an MR visualization device, local device112may be a Microsoft HOLOLENS™ headset, available from Microsoft Corporation, of Redmond, Washington, USA, or a similar device, such as, for example, a similar MR visualization device that includes waveguides. The HOLOLENS™ device can be used to present 3D virtual objects via holographic lenses, or waveguides, while permitting user118to view actual objects in a real-world scene, i.e., in a real-world environment, through the holographic lenses.

As mentioned above, memory106may include computer-readable instructions that, when executed by processing circuitry104, cause computing system102to provide a surgical planning system116. Surgical planning system116is configured to help a surgeon plan a surgery, such as an anatomic shoulder replacement surgery or a reverse shoulder replacement surgery. In an anatomic shoulder replacement surgery, a surgeon implants a cup-shaped glenoid implant on a glenoid fossa of a patient's scapula and a ball-shaped humeral implant on the proximal end of the patient's humerus. In a reverse shoulder replacement surgery, a surgeon implants a ball-shaped glenoid implant on the glenoid fossa of the patient's scapula and a cup-shaped humeral implant on the proximal end of the patient's humerus.

In either an anatomic shoulder replacement surgery or a reverse shoulder replacement surgery, the surgeon can select from among various different types of glenoid implants and humeral implants. For example, the surgeon can select among glenoid implants with keels, glenoid implants with pegs, or other types of glenoid implants. Moreover, the surgeon can select among various sizes within each type of glenoid implant. Furthermore, the surgeon can select among stemmed humeral implants, stemless humeral implants, or other types of humeral implants. Similarly, the surgeon can select among various sizes within each type of glenoid implant. For any one glenoid or humeral implant, the surgeon can select among various placement parameters for the glenoid or humeral implants. For example, the surgeon can select among various angles at which to seat the glenoid or humeral implants. In some examples, the surgeon can select among various bone preparation angles, depths, and positions.

Given that there are numerous types of implants and various available placement parameters, it may be difficult for surgeons to choose which types of implants and which placement parameters to use for a specific patient. Accordingly, surgical planning system116may generate suggested surgical options regarding types of implants and placement parameters to use for specific patients when the surgeon is planning shoulder replacement surgeries for the patients. When planning a shoulder replacement surgery, the surgeon may select from among the suggested surgical options generated by surgical planning system116or select other types of implants and/or surgical parameters. In other words, the surgeon is not limited to the suggested surgical options generated by surgical planning system116.

Individual surgeons may have specific preferences with respect to types of implants and placement parameters. For example, a surgeon may prefer to always use pegged glenoid implants instead of keeled glenoid implants because they feel that patients have lower revision rates with pegged glenoid implants than keeled glenoid implants. In another example, a surgeon may prefer to have implant retroversion angles for glenoid implants be no greater than 10°.

Ignoring the preferences of surgeons may result in surgical planning system116generating suggested surgical options that surgeons will not use. This presents a significant limit on the utility of surgical planning system116generating suggestions at all. One approach to addressing this problem is to train machine learning (ML) models based on surgeries performed according to the preferences of individual surgeons. However, the surgeon may not have completed enough surgeries for there to be sufficient training data to train an ML model for the surgeon. Without sufficient training data, the ML model for a surgeon may generate poor suggestions. Moreover, implementing different ML models for different surgeons may consume considerable processing power and storage space.

The techniques of this disclosure may address this issue. As described herein, surgical planning system116may obtain one or more surgeon preference parameters121that specify ranges of surgical parameters, such as glenoid implant types and positioning parameters for a glenoid implant to be attached to a glenoid of a patient during a surgery. Furthermore, surgical planning system116may determine, based on one or more anatomic parameters of the patient and the surgeon preference parameters, one or more suggested surgical options. The suggested surgical options may correspond to different combinations of the positioning parameters for the implant and types of glenoid implant.

Examples of how surgical planning system116may perform automated planning to determine the suggested surgical options based on the one or more anatomic parameters and the surgeon preference parameters are described in greater detail below. Surgical planning system116may output the one or more suggested surgical options for display. In some examples, surgical planning system116may receive an indication of user input from the surgeon to select one of the suggestions or select alternative implant types or placement parameters. Surgical planning system116may store the selected implant types and/or placement parameters in surgical plan data117.

FIG.2is a block diagram illustrating example details of surgical planning system116, in accordance with one or more techniques of this disclosure. In the example ofFIG.2, surgical planning system116includes a surgery prediction unit200, a preference acquisition unit202, an anatomic parameter unit204, a parameter prediction unit206, a range of motion (RoM) unit208, and a plan presentation unit210. In other examples, surgical planning system116may include more, fewer, or different units. Surgery prediction unit200, preference acquisition unit202, anatomic parameter unit204, parameter prediction unit206, RoM unit208, and plan presentation unit210may be implemented in software executed by programmable processing circuitry. In some examples, one or more of surgery prediction unit200, preference acquisition unit202, anatomic parameter unit204, parameter prediction unit206, RoM unit208, and plan presentation unit210may be at least partially implemented using special-purpose hardware. Surgery prediction unit200, preference acquisition unit202, anatomic parameter unit204, parameter prediction unit206, RoM unit208may work together to generate computer-assisted predictions.

Surgery prediction unit200may generate a prediction regarding whether to perform an anatomic shoulder replacement surgery or a reverse shoulder replacement surgery. For example, surgery prediction unit200may generate a first confidence value indicating a level of confidence (e.g., an estimated probability) that a set of reference surgeons would select an anatomic shoulder replacement surgery for a patient and a second confidence value indicating a level of confidence that the set of reference surgeons would select a reverse shoulder replacement surgery for the patient. In this example, surgery prediction unit200may output an indication of whichever of the anatomic shoulder replacement surgery and the reverse shoulder replacement surgery has a greater confidence score.

Surgery prediction unit200may be implemented in one of a variety of ways. For example, surgery prediction unit200may be implemented using one or more artificial intelligence systems, such as a combination of one or more artificial neural networks, support vector machines (SVMs), decision tree networks, random forests, naïve Bayesian networks, and so on. Surgery prediction unit200may generate the prediction regarding whether to perform an anatomic shoulder replacement surgery or a reverse shoulder replacement surgery based on a set of input data. Example types of input data for surgery prediction unit200may include patient data such as the age of the patient, a diagnosis of a condition of the patient (e.g., massive rotator cuff tear, osteoarthritis, etc.), a gender of the patient, a glenoid orientation of the patient, a glenoid sphere radius of the patient, a glenoid version of the patient, a glenoid inclination of the patient, a humerus subluxation of the patient, a glenoid direction of the patient, a glenoid area of the patient, and/or other types of data regarding the patient.

Preference acquisition unit202is configured to acquire surgeon preference parameters for a surgeon. Preference acquisition unit202may store acquired surgeon preference parameters as surgeon preference parameters121. In some examples, the surgeon preference parameters may be specific to a surgery for a specific patient. In some examples, the surgeon preference parameters may be common across all patients treated by the surgeon. Preference acquisition unit202may output a user interface for selecting surgeon preference parameters.FIG.4A, which is described in greater detail below, illustrates an example user interface for selecting surgeon preference parameters for an anatomic shoulder replacement surgery.FIG.4B, which is described in greater detail below, illustrates an example user interface for selecting surgeon preference parameters for a reverse shoulder replacement surgery.

In some examples, to determine one or more of the anatomic parameters of the patient based on medical imaging data119, anatomic parameter unit204may generate 3-dimensional (3D) models of the bones (e.g., scapula, humerus, etc.) of the patient based on medical imaging data119. Additionally, anatomic parameter unit204may perform processes to identify specific landmarks in the 3D models of the bones. The landmarks are positions in 3D space that are on or within the 3D models of the bones. Anatomic parameter unit204may then use the positions of the landmarks in 3D space to calculate one or more of the anatomic parameters. For example, to calculate the critical shoulder angle, anatomic parameter unit204may determine an angle between (i) a line from a most-superior point (i.e., a first landmark) on a border of a glenoid fossa of the patient to a most-inferior point (i.e., a second landmark) on the border of the glenoid fossa of the patient, and (ii) a line from the most-inferior point on the border of the glenoid fossa of the patient to a most-lateral point (i.e., a third landmark) on an acromion of the scapula of the patient. Anatomic parameter unit204may use one or more types of algorithms to identify the landmarks. For example, anatomic parameter unit204may use a hill-climbing algorithm to identify specific landmarks, such as points on the border of the glenoid fossa of the patient.

Parameter prediction unit206may determine one or more suggested surgical options based on the one or more anatomic parameters of the patient and the surgeon preference parameters. The suggested surgical options may correspond to different combinations of glenoid implants, positioning parameters, and/or bone preparation parameters for glenoid implants. Parameter prediction unit206may perform a process to determine the suggested surgical options that includes several stages. In a first stage, parameter prediction unit206may filter glenoid implant types based on surgeon preference parameters. In a second stage, parameter prediction unit206may determine a size of the glenoid implant. In a third stage, parameter prediction unit206may use a cost function to determine cost values for trial vectors. Each trial vector is a set of surgical parameter values, such as positioning parameters and/or glenoid implant type.FIG.8, which is described in greater detail below, describes an example process to determine suggested surgical options for a glenoid implant.

RoM unit208may determine a range of motion (RoM) of the patient's shoulder for one or more of the suggested surgical options. RoM unit208may determine a range of motion for a suggested surgical option using a combined 3D model of the patient's scapula, humerus (with humeral implant), and the glenoid implant attached to the patient's scapula with the surgical parameters corresponding to the suggested surgical option. RoM unit208may then move the 3D model of the humerus relative to the 3D models of the scapula and glenoid implant along one or more axes of motion. For each axis of motion, RoM unit208may then detect the angles at which the model of the humerus collides with the model of the scapula. These collisions represent the outermost ends of the ranges of motion for the axes of motion.

FIG.3is a conceptual diagram illustrating an example surgical planning user interface300, in accordance with one or more techniques of this disclosure. Plan presentation unit210(FIG.2) may generate user interface300for display (e.g., on display108or local device112(FIG.1)). User118may use interface300as part of a process to plan a shoulder replacement surgery. In the example ofFIG.3, user interface300includes a superior view302, a frontal view304, and a bone model306. In this example, superior view302is an x-ray image of a shoulder of a patient from a superior perspective (i.e., looking in the inferior direction from a superior position). In this example, frontal view304is an x-ray image of the shoulder of the patient from an anterior perspective (i.e., looking in the posterior direction from an anterior position). In this example, model306is a 3-dimensional model of the bone of the patient's shoulder. Superior view302, frontal view304, and model306may help user118visualize the patient's shoulder for purposes of planning a shoulder replacement surgery on the patient's shoulder.

Additionally, surgical planning interface300includes a patient information field308, a patient anatomy field310, a surgery prediction field312, a “plan anatomic” button314, and a “plan reverse” button316. Patient information field308includes name information, age information, and information about whether the surgery is being planned on the patient's left or right shoulder. Patient anatomy field310includes information about the patient's diagnosis, glenoid type, prior surgeries, and F1 sub-scapularis footprint (F1 sub-scap). Surgery prediction field312may include an indication of a predicted type of shoulder replacement surgery for the patient. In the example ofFIG.3, surgery prediction field312indicates that the predicted type of shoulder replacement surgery for the patient is a reverse shoulder replacement surgery with a probability of 66%. In other words, given the information about the patient, a majority of the surgeons associated with the training data would perform the predicted type of shoulder replacement surgery and a confidence level in the prediction is 66%. Surgery prediction unit200(FIG.2) may determine the predicted type of shoulder replacement surgery, e.g., as described elsewhere in this disclosure.

User118may initiate a process to plan an anatomic shoulder replacement surgery by selecting “plan anatomic” button314. User118may initiate a process to plan a reverse shoulder replacement surgery by selecting “plan reverse” button316. Note that, in some examples, user118does not need to, i.e., is not required to, select the type of shoulder replacement surgery indicated in surgery prediction field312, and may instead select another type of shoulder replacement surgery that is not indicated.

FIG.4Ais a conceptual diagram illustrating an example surgical planning user interface400for selecting surgeon preference parameters for an anatomic shoulder replacement surgery, in accordance with one or more techniques of this disclosure. In some examples, preference acquisition unit202(FIG.2) may present user interface400in response to receiving an indication of user input to select “plan anatomic” button314(FIG.3).

In the example ofFIG.4A, user interface400includes anchorage checkboxes402A-402C (collectively. “anchorage checkboxes402”). Anchorage checkbox402A corresponds to glenoid implants having keeled anchorages. Anchorage checkbox402B corresponds to glenoid implants having pegged anchorages. Anchorage checkbox402C corresponds to glenoid implants having pegged anchorages that include one or more finned pegs. In other examples, anchorage checkboxes402may correspond to other types of anchorage arrangements for glenoid implants. User118(e.g., surgeon) may use anchorage checkboxes402to indicate which types of anchorage arrangements for glenoid implants may be used by parameter prediction unit206to determine suggested surgical options.

Furthermore, in the example ofFIG.4A, user interface400includes range selection features404A-404F (collectively, “range selection features404”). Range selection feature404A corresponds to a maximum retroversion angle of the glenoid implant. Range selection feature404B corresponds to a maximum anteversion angle of the glenoid implant. Range selection feature404C corresponds to a maximum inferior inclination angle of the glenoid implant. Range selection feature404D corresponds to a maximum superior inclination angle of the glenoid implant. Range selection feature404E corresponds to a minimum seating percentage of the glenoid implant. A seating percentage is a percentage of an area of a seating surface of the implant that is in contact with (i.e., seats on) the bone. Range selection feature404F corresponds to a maximum seating percentage of the glenoid implant.

FIG.4Bis a conceptual diagram illustrating an example surgical planning user interface450for selecting surgeon preference parameters for an anatomic shoulder replacement surgery, in accordance with one or more techniques of this disclosure. In some examples, preference acquisition unit202(FIG.2) may present user interface450in response to receiving an indication of a user input to select “plan reverse” button316(FIG.3).

In the example ofFIG.4B, user interface450includes checkboxes452A-452D (collectively, “checkboxes452”). Checkboxes452correspond to types of implants that the surgeon is willing to use in a reverse shoulder replacement surgery. Checkbox452A corresponds to eccentric glenospheres. Checkbox452B corresponds to glenoid implants with 135° neck shaft angles. Checkbox452C corresponds to a first type of glenoid implants. Checkbox452D corresponds to a second type of glenoid implants. In other examples, checkboxes452may correspond to other types of implants used in reverse shoulder replacement surgeries. User118(e.g., surgeon) may use checkboxes452to indicate which types of implants may be used by parameter prediction unit206to determine suggested surgical options.

Furthermore, in the example ofFIG.4B, user interface450includes range selection features454A-454E (collectively, “range selection features454”). Range selection feature454A corresponds to a maximum retroversion angle of the glenoid implant. Range selection feature454B corresponds to a maximum anteversion angle of the glenoid implant. Range selection feature454C corresponds to a maximum inferior inclination angle of the glenoid implant. Range selection feature454D corresponds to a maximum superior inclination angle of the glenoid implant. Range selection feature454E corresponds to a minimum seating percentage of the glenoid implant.

FIG.5is a conceptual diagram illustrating an example surgical planning user interface500showing surgical suggestions for an anatomic shoulder replacement surgery, in accordance with one or more techniques of this disclosure. Plan presentation unit210(FIG.2) may generate user interface500for display (e.g., on display108or local device112(FIG.1)). In some examples, plan presentation unit210may generate user interface500after receiving indications of user input indicating surgeon preference parameters (e.g., via user interface300(FIG.3)).

In the example ofFIG.5, user interface500shows surgical suggestions502A,502B (collectively, “surgical suggestions502”) for an anatomic shoulder replacement surgery. Each of surgical suggestions502indicates a type of a glenoid implant, an anchorage type of the glenoid implant, a size of the glenoid implant, a radius of a sphere of the glenoid implant, an augment of the glenoid implant, a version of the glenoid implant, an inclination of the glenoid implant, and a seating percentage of the glenoid implant. An augment of a glenoid implant is a device that compensates for high glenoid versions. In other examples, surgical suggestions502may indicate more, fewer, or different types of data. For instance, in some examples, surgical suggestions502may indicate an amount of reaming (e.g., in cubic millimeters or in millimeters). In some examples, surgical suggestions502may include two radii of curvature for augmented glenoid implants and a single radius of curvature for non-augmented glenoid implants. User118may select one of surgical suggestions502. In the example ofFIG.5, the black background is used to indicate that surgical suggestion502A is the selected surgical suggestion.

Furthermore, user interface500includes superior view506, frontal view508, and model510. Superior view506shows an x-ray image of the shoulder of a patient from a superior perspective (i.e., looking in the inferior direction from a superior position). Superior view506shows an outline512of a glenoid implant of the type indicated by the selected surgical suggestion at the positions indicated by the selected surgical suggestion. Frontal view508shows an x-ray image of the shoulder of the patient from an anterior perspective (i.e., looking in the posterior direction from an anterior position). Frontal view508shows an outline514of the glenoid implant of the type indicated by the surgical suggestion at the positions indicated by the selected surgical suggestion. Model510shows a 3D model of the patient's scapula with the glenoid fossa516highlighted.

User interface500also includes controls504A,504B for switching between display of surgical suggestions for an anatomic shoulder replacement surgery and a reverse shoulder replacement surgery.

FIG.6is a conceptual diagram illustrating an example surgical planning user interface600showing surgical suggestions for a reverse shoulder replacement surgery, in accordance with one or more techniques of this disclosure. Plan presentation unit210(FIG.2) may generate user interface600for display (e.g., on display108or local device112(FIG.1)). In some examples, plan presentation unit210may generate user interface600after receiving indications of user input indicating surgeon preference parameters (e.g., via user interface300(FIG.3)).

In the example ofFIG.6, user interface600shows surgical suggestions602A,602B (collectively, “surgical suggestions602”) for a reverse shoulder replacement surgery. Each of surgical suggestions602indicates a type of a glenoid implant, a diameter of the glenoid implant, a glenosphere diameter and type (e.g., centered, eccentric, tilted, etc.) of the glenoid implant, a neck shaft angle of a corresponding humerus implant, a version of the glenoid implant, a seating percentage of the glenoid implant, and a peg depth of the glenoid implant. User118may select one of surgical suggestions602. In the example ofFIG.6, the black background is used to indicate that surgical suggestion602A is the selected surgical suggestion.

Furthermore, user interface600includes superior view606, frontal view608, and model610. Superior view606shows an x-ray image of the shoulder of a patient from a superior perspective (i.e., looking in the inferior direction from a superior position). Superior view606shows an outline612of a glenoid implant of the type indicated by the selected surgical suggestion at the positions indicated by the selected surgical suggestion. Frontal view608shows an x-ray image of the shoulder of the patient from an anterior perspective (i.e., looking in the posterior direction from an anterior position). Frontal view608shows an outline614of the glenoid implant of the type indicated by the surgical suggestion at the positions indicated by the selected surgical suggestion. Model610shows a 3D model of the patient's scapula with a phantom image of a glenoid implant.

User interface600also includes controls604A,604B for switching between display of surgical suggestions for an anatomic shoulder replacement surgery and a reverse shoulder replacement surgery.

Although not shown in the example ofFIG.6, each of surgical suggestions602may include data indicating expected ranges of motion for surgical suggestions602. For example, each of surgical suggestions602may indicate an expected angle of extension, an expected angle of flexion, an expected angle of abduction, and an expected angle of internal rotation. RoM unit208may determine these expected ranges of motion, e.g., in the manner described elsewhere in this disclosure.

FIG.7is a flowchart illustrating an example operation of surgical planning system116, in accordance with one or more techniques of this disclosure. In the example ofFIG.7, surgical planning system116(e.g., preference acquisition unit202(FIG.2) may obtain one or more surgeon preference parameters that specify values of one or more surgical parameters (700). The surgical parameters may indicate ranges of positioning parameters for a glenoid implant to be attached to a glenoid of a patient during a surgery. For example, surgical planning system116may obtain the surgeon preference parameters via a user interface, such as user interface400(FIG.4A) or user interface450(FIG.4B).

Furthermore, in the example ofFIG.7, surgical planning system116(e.g., parameter prediction unit206) may determine, based on one or more anatomic parameters of the patient and the surgeon preference parameters, one or more suggested surgical options (702). Each of the surgical options corresponds to a different combination of the positioning parameters for the glenoid implant and types of glenoid implant.FIG.8, described in detail below, is a flowchart illustrating an example operation of a parameter prediction unit206to determine the one or more suggested surgical options for the glenoid implant. In some examples, as part of determining the one or more suggested surgical options, parameter prediction unit206may filter suggested surgical options (e.g., suggested surgical options determined using the operation ofFIG.8) to remove invalid suggested surgical options. For example, parameter prediction unit206may filter out suggested surgical options that include glenoid implants having anchorages that perforate a boundary of the scapula opposite the glenoid.

In some examples, surgical planning system116may obtain medical imaging data for the glenoid of the patient. For example, surgical planning system116may obtain the medical imaging data from a memory, such as memory106(FIG.1), from a medical imaging machine (e.g., an x-ray machine, CT machine, etc.). The medical imaging data may include medical images and/or models of the shoulder of the patient. Surgical planning system116(e.g., anatomic parameter unit204) may determine the one or more anatomic parameters of the patient based on the medical imaging data.

In the example ofFIG.7, surgical planning system116(e.g., plan presentation unit210(FIG.2) may output the one or more suggested surgical options (704). For example, surgical planning system116may output the one or more suggested surgical options in a user interface, such as user interface500(FIG.5) or user interface600(FIG.6). In some examples, surgical planning system116may output the one or more suggested surgical options for display in an MR visualization. In some examples, surgical planning system116may output the one or more suggested surgical options for display on a conventional monitor or screen. In some examples, surgical planning system116may output the suggested surgical options audibly.

FIG.8is a flowchart illustrating an example operation of parameter prediction unit206to determine one or more suggested surgical options for a glenoid implant, in accordance with one or more techniques of this disclosure. In the example ofFIG.8, parameter prediction unit206may filter glenoid implant types based on surgeon preference parameters (800). In other words, parameter prediction unit206may filter out glenoid implant types based on the surgeon preference parameters to determine a set of one or more remaining glenoid implant types. When filtering glenoid implant types, parameter prediction unit206may start from a set of glenoid implants that includes all available glenoid implant types. For example, the glenoid implant types may include glenoid implants with keeled anchorages, glenoid implants with pegged anchorages, and glenoid implants with pegged anchorages that include one or more finned pegs. Furthermore, in this example, if the surgeon preference parameters indicate that the surgeon does not want to use glenoid implants with keeled anchorages, parameter prediction unit206may filter out (e.g., remove) all glenoid implants with keeled anchorages from a list of available glenoid implants.

Additionally, in the example ofFIG.8, parameter prediction unit206may determine a size of a glenoid implant based on the anatomical parameters of the patient (802). For instance, parameter prediction unit206may determine a glenoid area size (i.e., an anatomical parameter) of the glenoid fossa of the patient. In this example, the glenoid area size of the glenoid fossa is the 2-dimensional area contained within a border of the glenoid fossa. Parameter prediction unit206may then compare the glenoid area size of the glenoid to a set of one or more thresholds. The thresholds may correspond to sizes of glenoid implants in the list of available glenoid implants. In some examples, parameter prediction unit206may determine the glenoid area size, a length of a glenoid major axis, and a length of a glenoid minor axis. The glenoid major axis and the glenoid minor axis are defined by an ellipse corresponding to a boundary of the glenoid. Parameter prediction unit206may determine the size of the glenoid implant based on the glenoid area size, the length of the glenoid major axis, and the length of the glenoid minor axis. For instance, parameter prediction unit206may look up the size of the glenoid implant in a table that maps combinations of glenoid area size, length of the glenoid major axis, and the length of the glenoid minor axis to sizes of glenoid implants.

Furthermore, parameter prediction unit206may generate a current trial vector (803). The trial vector is a set of surgical parameter values. The surgical parameter values are values of surgical parameters. The surgical parameters may include placement parameters and types of glenoid implants. Example placement parameters may include a version of the glenoid implant, an inclination of the glenoid implant, an anterior position of the glenoid implant, a lateral position of the glenoid implant, a superior position of the glenoid implant, and so on. The types of glenoid implants that may be included in a trial vector may be limited to the types of glenoid implant that are in the set of filtered glenoid implant types (i.e., the remaining glenoid implant types) determined in step800. In other words, parameter prediction unit206may generate the trial vectors such that the trial vectors include only glenoid implant types in the set of remaining glenoid implant types. In some examples, the surgical parameter values may include a glenoid implant size parameter that is limited to the determined size of the glenoid implant. In other words, parameter prediction unit206may generate the trial vectors such that the trial vectors include only glenoid implants having the determined size.

Furthermore, in the example ofFIG.8, parameter prediction unit206may determine a first preliminary cost value for the current trial vector based on the input values (806). Parameter prediction unit206may determine the first preliminary cost value for the trial vector based on a linear combination of the input values as shown in Equation (1), below:

In Equation (1), above, C1indicates the first preliminary cost value for the trial vector, i is an index of the input values, m is the number of input values, aiis a scaling factor for input value i, biis input value i in combination of inputs k, and offset is an offset value.

In one example where parameter prediction unit206determines the first preliminary cost value for the trial vector based on a linear combination of the input values, parameter prediction unit206may determine the first preliminary cost value for the trial vector as:

In Equation (2), above, C1denotes the first preliminary cost value for the trial vector, a1through a7denote weight values. VReamedindicates a volume reamed when the surgical parameters of the trial vector are applied. ANoSeatingindicates an area of no contact between the implant and the bone when the surgical parameters of the trial vector are applied. AStrongSeatingindicates an area of contact between the implant and a strong portion of the bone when the surgical parameters of the trial vector are applied. AWeakSeatingindicates an area of contact between the implant and a weak portion of the bone when the surgical parameters of the trial vector are applied. Rimplantindicates a radius of the glenoid implant indicated by the surgical parameter of the trial vector. VAnchoragePerforationindicates whether an anchorage (e.g., peg) of a glenoid implant perforates a boundary of the scapula opposite the glenoid or would come too close to the boundary of the scapula. In some examples, to determine whether the anchorage would come too close to the boundary of the scapula, parameter prediction unit206may compare a position of the anchorage to a scaled-down model of the scapula and determine whether any portion of the anchorage passes through a boundary of the scaled-down model of the scapula opposite the glenoid. αVersionand αInclinationare fixed values. αVersionand αInclinationmay be determined based on an analysis of cases.

In Equation (2), P indicates a penalty value applied if any of the surgical parameters of the trial vector (or input values) are not consistent with the surgeon preferred values. For example, the surgical parameters of the trial vector include a version parameter that may range from −15° (retroversion) to 15° (anteversion). In this example, the surgeon preference parameters may specify a maximum retroversion of 10° (i.e., a version of −10°). Hence, in this example, if the surgical parameters of the trial vector include a version parameter of −15°, parameter prediction unit206may set P equal to the penalty value (e.g., 100). Otherwise, if the version parameter is not outside of the range specified by the surgeon preference parameters, parameter prediction unit206may set P equal to a non-penalty value (e.g., 0).

In Equation (2), VReamed, ANoSeating, AStrongSeating, and AWeakSeatingare based on anatomic parameters of the patient. A portion of a bone may be considered weak if a density of the bone is below a specified threshold. In some examples, a portion of a bone may be considered strong if a density of the bone is above the specified threshold.

In addition to determining the first preliminary cost value for a set of input values, parameter prediction unit206may determine a second preliminary cost value for the current trial vector (808). The second preliminary cost value may serve as part of a coherence verification that may ensure that values of the surgical parameters in the trial vector are within reasonable ranges. In some examples, parameter prediction unit206may determine the second preliminary cost value for the trial vector based on differences between the surgical parameters of the trial vector and typical values of the surgical parameters. For instance, parameter prediction unit206may determine the second preliminary cost value for the set of input values as follows:

In Equation (3) above, C2denotes the second preliminary cost value, k is an index of a surgical parameter, n denotes the number of surgical parameters in the trial vector, akdenotes a weight for surgical parameter k, xkdenotes a value of surgical parameter k in the trial vector, meankindicates a mean of values of surgical parameter k in a body of cases in which the surgery was previously performed, stdDevkis a standard deviation of surgical parameter k in the body of cases in which the surgery was previously performed. Equation (3), above, is calculated in a logarithmic scale. In Equation (3), above, each of the surgical parameters in the trial vector is assumed to follow a naïve Bayesian Gaussian distribution. Thus, equation (3) may be equivalent to calculating, for each surgical parameter Vkin the trial vector, given the values of other parameters x1. . . xnin the trial vector accordingly to the following chain rule:

In other examples, it may be assumed that the surgical parameters in the trial vector follow other distributions. In some examples, different distributions may be assumed for different surgical parameters. For instance, seating percentage is an example of a surgical parameter. Many surgeons prefer the seating percentage to be 100%, but 100% seating is often not attainable in some patients. As a result, the mean seating percentage across patients (and hence a mean of the distribution for the seating percentage surgical parameter) may be less than 100% (e.g., 95%). Therefore, in this example, a different distribution from the distribution of derived directly from seating percentages in the trial vector may be used. For instance, a bias term may be used to modify the distribution derived from the seating percentages such that a mean of the distribution is equal to 100%.

Furthermore, in the example ofFIG.8, parameter prediction unit206may determine a cost value for the current trial vector based on the first preliminary cost value and the second preliminary cost value (810). For example, parameter prediction unit206may determine the cost value for the trial vector as:

In Equation (4), above, C denotes the cost value for the trial vector. C1is the first preliminary cost value, C2is the second preliminary cost value, and M is a constant that ensures that (C1+M) and C2have the same sign.

Parameter prediction unit206may determine whether the cost value for the trial vector is less than a cost value for a previous trial vector (812). If the cost value for the trial vector is not less than the cost value for a previous trial vector (“NO” branch of812), parameter prediction unit206may revert the trial vector to the previous trial vector (814). If the cost value for the current trial vector is less than the cost value for the previous trial vector (“YES” branch of812) or after reverting the current trial vector to the previous trial vector, parameter prediction unit206may determine whether a stopping condition is met (816). In various examples, the stopping condition may be one or more of a specific number of times that step (816) has been reached, a particular number of times that a lower cost value has not been found, after evaluating all glenoid implant types in the remaining set of glenoid implant types, etc.

In response to determining that the stopping condition has not been met (“NO” branch of816), parameter prediction unit206may generate a new trial vector (818) and repeat steps (804)-(816). Parameter prediction unit206may generate the new trial vector by updating one (or more) of the surgical parameter values of the current (or reverted) trial vector. For example, parameter prediction unit206may generate the new trial vector by incrementing or decrementing a surgical parameter value, such as the inclination angle, version angle, etc. In some examples, parameter prediction unit206may increment or decrement different surgical parameters by different amounts when generating new trial vectors. In another example, parameter prediction unit206may generate a new trial vector by changing a glenoid implant type to another glenoid implant type in the filtered set of glenoid implant types.

In this way, parameter prediction unit206may determine, based on a comparison of the cost value for a current trial vector in the set of trial vectors and a cost value of a previous trial vector in the set of trial vectors, whether the current trial vector represents an improvement over the previous trial vector. Additionally, based on the current trial vector not representing an improvement over the previous trial vector (e.g., the cost score for the current trial vector is less than the cost score for the previous trial vector), parameter prediction unit206may revert the current trial vector to the previous trial vector and update one or more surgical parameters of the current trial vector to determine a new current trial vector in the set of trial vectors. Alternatively, based on the current trial vector representing an improvement over the previous trial vector, parameter prediction unit206does not revert the current trial vector to the previous trial vector. In either case, parameter prediction unit206may generate a new trial vector based on the current trial vector.

After generating the new trial vector, parameter prediction unit206may repeat steps (804) through (816) with the new trial vector serving as the current trial vector. In this way, parameter prediction unit206may act as an amoeba optimizer that loops through combinations of implants and other surgical parameters. Thus, parameter prediction unit206may learn potentially optimal suggested surgical options for a specific patient based on the surgeon preference parameters.

In this way, parameter prediction unit206may generate a set of one or more trial vectors, where each trial vector in the set of trial vectors includes one or more of the surgical parameters. For each trial vector in the set of trial vectors, parameter prediction unit206may determine input values based on the surgical parameters of the trial vector and the anatomic parameters, and determine a cost value for the trial vector based on the input values.

On the other hand, if the stopping condition is met (“YES” branch of816), parameter prediction unit206may determine a best trial vector in the set of trial vectors (820). The best trial vector is the trial vector having a lowest cost value when the stopping condition is met.

Each of the evaluated trial vectors inFIG.8may correspond to a different suggested surgical option. In some examples, parameter prediction unit206may rank the trial vectors based on their cost values and set a specific number of the trial vectors with the lowest cost values as the suggested surgical options. In some examples, parameter prediction unit206may determine, based on the cost value for a trial vector, whether to include the trial vector as one of the suggested surgical options. For instance, parameter prediction unit206may determine whether to include a trial vector as one of the suggested surgical options based on determining that the cost value for the trial vector exceeds a threshold.

Certain techniques of this disclosure are described with respect to a shoulder arthroplasty surgery and particularly with respect to a human scapula. Examples of shoulder arthroplasties include, but are not limited to, reversed arthroplasty, augmented reverse arthroplasty, standard total shoulder arthroplasty, augmented total shoulder arthroplasty, and hemiarthroplasty. However, the techniques are not so limited, and the visualization system may be used to provide virtual guidance information, including virtual guides in any type of surgery. Other example procedures in which surgical assistance system100may be used to provide virtual guidance include, but are not limited to, other types of orthopedic surgeries; any type of procedure with the suffix “plasty,” “stomy,” “ectomy,” “clasia,” or “centesis,”; orthopedic surgeries for other joints, such as elbow, wrist, finger, hip, knee, ankle or toe, or any other orthopedic surgery in which precision guidance is desirable. For instance, surgical assistance system100may be used to provide computer-assisted planning for an ankle arthroplasty surgery.

While the techniques been disclosed with respect to a limited number of examples, those skilled in the art, having the benefit of this disclosure, will appreciate numerous modifications and variations there from. For instance, it is contemplated that any reasonable combination of the described examples may be performed. It is intended that the appended claims cover such modifications and variations as fall within the true spirit and scope of the invention. Moreover, techniques of this disclosure have generally been described with respect to human anatomy. However, the techniques of this disclosure may also be applied to animal anatomy in veterinary cases.

Operations described in this disclosure may be performed by one or more processors, which may be implemented as fixed-function processing circuits, programmable circuits, or combinations thereof, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Fixed-function circuits refer to circuits that provide particular functionality and are preset on the operations that can be performed. Programmable circuits refer to circuits that can programmed to perform various tasks and provide flexible functionality in the operations that can be performed. For instance, programmable circuits may execute instructions specified by software or firmware that cause the programmable circuits to operate in the manner defined by instructions of the software or firmware.U Fixed-function circuits may execute software instructions (e.g., to receive parameters or output parameters), but the types of operations that the fixed-function circuits perform are generally immutable. Accordingly, the terms “processor” and “processing circuitry,” as used herein may refer to any of the foregoing structures or any other structure suitable for implementation of the techniques described herein.