Apparatus, system, and method for determining alignment of an artificial knee joint in a patient's bone

Abstract

An apparatus, system, and method for formulating a surgical plan for each patient are disclosed. One exemplary method (1202) includes identifying anatomical parameters of a knee joint (402), determining target kinematics of the knee joint based on the pre-implant anatomical structure (404), determining, for each of a first set of implant alignment options, one or more predicted gaps between a femoral prosthesis (102) and a tibial prosthesis (104) when implanted in the knee joint (1204), determining a second set of implant alignment options from the first set based at least in part on the predicted gaps (1206), determining the predicted kinematics for each implant alignment of the second set (406), identifying at least one recommended implant alignment of the femoral prosthesis (102) and the tibial prosthesis (104) based on the target kinematics and the predicted kinematics (408), and incorporating the at least one recommended implant alignment into a patient-specific surgical plan (800) (410).

Description

Technical Field

[0001] (Cross - Reference to Related Applications) This application claims the benefit of U.S. Provisional Patent Application No. 63 / 359,200, filed Jul. 7, 2022, and U.S. Provisional Patent Application No. 63 / 422,508, filed Nov. 4, 2022, both entitled “APPARATUS, SYSTEM, AND METHOD FOR DETERMINING AN ALIGNMENT OF A KNEE PROSTHESIS IN A BONE OF A PATIENT”. The foregoing applications are hereby incorporated by reference in their entireties.

[0002] (Field of the Invention) The present disclosure relates generally to computer - assisted orthopedic systems for use in the planning and performance of orthopedic surgery, and more particularly to techniques for determining the alignment of a knee prosthesis for a patient undergoing knee arthroplasty.

Background Art

[0003] Arthroplasty is a well - known surgical procedure in which a diseased and / or damaged native joint is replaced with an artificial joint. For example, a typical knee prosthesis includes a tibial tray, a femoral prosthesis, and a polymeric insert or bearing positioned between the tibial tray and the femoral prosthesis. Depending on the severity of the patient's joint injury, various degrees of mobility orthopedic prostheses can be used. For example, a knee prosthesis may include a “fixed” tibial insert when it is desirable to limit the movement of the knee prosthesis, such as when there is significant soft tissue injury or loss. Alternatively, a knee prosthesis may include a “mobile” tibial insert when a greater degree of freedom of movement is desired. Additionally, a knee prosthesis can be a knee prosthesis designed to replace the femoral - tibial joint surfaces of both condyles of the patient's femur, or a unicompartmental (or single - condyle) knee prosthesis designed to replace the femoral - tibial joint surface of a single condyle of the patient's femur.

[0004] The type of orthopedic artificial knee joint used to replace a patient's natural knee joint can also depend on whether the patient's posterior cruciate ligament is retained or sacrificed (i.e., removed) during surgery. For example, if the patient's posterior cruciate ligament is damaged, diseased, and / or otherwise removed during surgery, a posterior stabilized artificial knee joint can be used to provide additional support and / or control in the lateral flexion degree. Alternatively, if the posterior cruciate ligament is intact, a cruciate-retaining artificial knee joint may be used.

[0005] Orthopedic surgeons may perform some surgical planning, for example, to select the type, size, position, and orientation of the artificial knee joint with respect to the patient's bone anatomy. Such planning can be manually performed by the orthopedic surgeon based on the patient's examination, preoperative medical images of the patient's bone anatomy, or intraoperative data collected in the operating room before and / or during the surgical procedure. SUMMARY OF THE INVENTION MEANS FOR SOLVING THE PROBLEM

[0006] According to one aspect of the present disclosure, an orthopedic surgical planning method includes, all by a computer system, identifying the anatomical parameters of a patient's knee joint, determining the target kinematics of the knee joint based on the patient's pre-implantation anatomical structure, determining the predicted kinematics of several implant alignment options of the artificial knee joint when implanted in the knee joint, identifying the recommended implant alignment of the artificial knee joint based on the target kinematics and the predicted kinematics of the artificial knee joint, and formulating a patient-specific surgical plan incorporating the recommended implant alignment. Determining the predicted kinematics may include the computer system inputting the type and size of the artificial knee joint and the identified anatomical parameters into a mathematical model representing the kinematics of the artificial knee joint when implanted, and operating the mathematical model to determine the predicted kinematics of the artificial knee joint for each alignment of several implant alignment options.

[0007] In some embodiments, determining the target kinematics of the knee joint may include determining the target ligament elongations of the ligaments of the knee joint over the flexion range. Operating a mathematical model to determine the predicted kinematics of a patient's knee joint may include determining the predicted ligament elongations of the ligaments over the flexion range. Identifying a recommended implant alignment may include, for each alignment of several implant alignment options, calculating the difference between the target ligament elongation and the predicted ligament elongation over the flexion range, and recommending the alignment corresponding to the minimum difference between the target ligament elongation and the predicted ligament elongation over the flexion range.

[0008] In some embodiments, calculating the difference between the target ligament elongation and the predicted ligament elongation over the flexion range may include calculating the sum of the absolute differences between the target ligament elongation and the predicted ligament elongation at several positions over the entire flexion range. Recommending the alignment corresponding to the minimum difference may include recommending the alignment corresponding to the minimum sum of the absolute differences.

[0009] In some embodiments, the method may further include, by a computer system, displaying a graph of ligament elongations for a recommended alignment over the flexion range. The graph may include an indicator of the target ligament elongation over the flexion range and an indicator of the predicted ligament elongation over the flexion range. The surgical plan for each patient may include the graph of ligament elongations.

[0010] In some embodiments, the ligament may be at least one of the medial collateral ligament, the lateral collateral ligament, the posterior cruciate ligament, the anterolateral ligament, and the posterior capsule of the patient's knee joint.

[0011] In some embodiments, determining the target ligament elongation of the ligaments of the knee joint over a flexion range may include determining the respective target ligament elongations of the medial collateral ligament, the lateral collateral ligament, and the posterior cruciate ligament of the knee joint. Determining the predicted ligament elongation of the ligament over the flexion range may include determining the respective predicted ligament elongations of the medial collateral ligament, the lateral collateral ligament, and the posterior cruciate ligament. Calculating the difference between the target ligament elongation and the predicted ligament elongation may include calculating the absolute difference between the respective target ligament elongation and the predicted ligament elongation of the medial collateral ligament, the lateral collateral ligament, and the posterior cruciate ligament, and calculating the sum of the absolute differences of the medial collateral ligament, the lateral collateral ligament, and the posterior cruciate ligament. The method may include recommending an alignment having the minimum sum of absolute differences.

[0012] In some embodiments, determining the target ligament elongation of the ligaments of the knee joint over a flexion range may further include determining the respective target ligament elongations of the anterolateral ligament and the posterior capsule of the knee joint. Determining the predicted ligament elongation of the ligament over the flexion range may further include determining the respective predicted ligament elongations of the anterolateral ligament and the posterior capsule of the knee joint. Calculating the difference between the target ligament elongation and the predicted ligament elongation may further include calculating the absolute difference between the respective target ligament elongation and the predicted ligament elongation of the anterolateral ligament and the posterior capsule of the knee joint. Calculating the sum of the absolute differences may include calculating the sum of the absolute differences for the medial collateral ligament, the lateral collateral ligament, the posterior cruciate ligament, the anterolateral ligament, and the posterior capsule of the knee joint.

[0013] In some embodiments, the surgical plan for each patient may include several planned resections of the patient's femur and at least one planned resection of the patient's tibia to prepare the patient's bone to receive an artificial knee joint.

[0014] In some embodiments, identifying the anatomical parameters of the knee joint may include identifying the anatomical parameters of the patient's knee joint based on a set of medical images of the patient's preoperative anatomical structure. The method may include obtaining the set of medical images through at least one of a CT scan, an X-ray imaging, and an ultrasound imaging of the patient's preoperative anatomical structure.

[0015] In some embodiments, the method may include performing an intraoperative bone alignment procedure on the patient's knee joint using a computer system to identify some landmarks on the patient's pre-implant anatomical structure, and the anatomical parameters may include some landmarks. The method may further include using a computer system to track the movement of the knee joint over a range of flexion based on the identified landmarks, and determining the target kinematics of the knee joint includes determining the target kinematics based on the movement of the knee joint. This method may further include inserting an articular extension device into the knee joint and operating the articular extension device to adjust the distance between the patient's femur and tibia over a range of flexion.

[0016] In some embodiments, determining the target kinematics of the knee joint based on the patient's pre-implant anatomical structure may include determining a first target kinematics of the knee joint based on a set of medical images of the patient's preoperative anatomical structure. Determining the target kinematics of the knee joint based on the patient's pre-implant anatomical structure may further include determining a second target kinematics of the knee joint based on an intraoperative bone alignment procedure for the patient's knee joint. Determining the predicted kinematics for several implant alignment options for the artificial knee joint when implanted in the knee joint may include operating a mathematical model after inputting the type and size of the artificial knee joint and the anatomical parameters identified from the set of medical images to determine the first predicted kinematics of the artificial knee joint for each alignment of several implant alignment options. Determining the predicted kinematics for several implant alignment options for the artificial knee joint when implanted in the knee joint may further include operating a mathematical model after inputting the type and size of the artificial knee joint and the anatomical parameters identified during the operation to determine the second predicted kinematics of the artificial knee joint for each alignment of several implant alignment options. Identifying the recommended implant alignment for the artificial knee joint may include identifying a first possible alignment based on the first target kinematics and the first predicted kinematics of the artificial knee joint. Identifying the recommended implant alignment for the artificial knee joint may further include a second possible alignment based on the second target kinematics and the second predicted kinematics of the artificial knee joint. The method may further include displaying an indicator indicating at least one of the first possible alignment and the second possible alignment, resected the patient's bone, preparing the bone to receive the artificial knee joint in one of the first possible alignment and the second possible alignment, and implanting the artificial knee joint.

[0017] In some embodiments, the method may further include determining the implanted movement of the artificial knee joint over a flexion range and displaying an indicator indicative of the implanted kinematics and at least one of a first target kinematic, a second target kinematic, a first predicted kinematic, and a second predicted kinematic.

[0018] In some embodiments, determining the predicted kinematics of the artificial knee joint includes inputting into a mathematical model several sizes of artificial knee joints, each of which has the same type of size of the artificial knee joint, and specific anatomical parameters, and operating the mathematical model to determine the predicted kinematics for each size of the artificial knee joint at each alignment of several implant alignment options.

[0019] In some embodiments, determining the target kinematics may include inputting anatomical parameters into a statistical shape function model and operating the statistical shape function model to generate the target kinematics of the knee joint.

[0020] In some embodiments, the mathematical model may be a linear response model configured to receive anatomical parameters, artificial knee joint size, and artificial knee joint type as inputs and generate the predicted kinematics of the artificial knee joint as an output.

[0021] In some embodiments, the linear response model may be further configured to generate as an output the predicted mechanical properties of the artificial knee joint. The mechanical properties may include at least one of the predicted contact forces between the components of the artificial knee joint and the predicted contact forces between the components of the artificial knee joint and the patient's bone.

[0022] According to another aspect, an orthopedic surgical planning system comprises a computer system configured to automatically identify anatomical parameters of a knee joint, determine a target kinematics of the knee joint based on the pre-implant anatomical structure of a patient, determine the predicted kinematics of several implant alignment options of an artificial knee joint when implanted in the knee joint, identify a recommended alignment of the artificial knee joint based on the target kinematics and the predicted kinematics of the artificial knee joint, and formulate a patient-specific surgical plan including the recommended implant alignment. To determine the predicted kinematics, the computer system may be configured to automatically input the type and size of the artificial knee joint and the identified anatomical parameters into a mathematical model representing the kinematics of the artificial knee joint when implanted, and operate the mathematical model to determine the predicted kinematics of the artificial knee joint for each alignment of several implant alignment options.

[0023] In some embodiments, the orthopedic surgical planning system further comprises a robotic system including a cutting tool configured to excise a patient's bone according to the patient-specific surgical plan.

[0024] In some embodiments, the orthopedic surgical planning system further comprises a bone alignment tool for identifying several landmarks on the pre-implant anatomical structure of a patient using the computer system. The anatomical parameters may include several landmarks. The computer system may be configured to determine the target kinematics of the knee joint by tracking the movement of the knee joint based on the identified landmarks to determine the target kinematics of the knee joint over a range of flexion.

[0025] In some embodiments, the computer system may include a display operable to display a graph of ligament elongation for the recommended alignment over a range of flexion. The graph may include an indicator showing the target ligament elongation over the range of flexion and an indicator showing the predicted ligament elongation over the range of flexion.

[0026] In some embodiments, the mathematical model may be a linear response model configured to receive anatomical parameters, artificial knee joint size, and artificial knee joint type as inputs and generate the predicted dynamics of the artificial knee joint as an output. In some embodiments, the linear response model may be further configured to generate, as an output, the predicted mechanical properties of the artificial knee joint. The mechanical properties may include at least one of the predicted contact forces between the components of the artificial knee joint and the predicted contact forces between the components of the artificial knee joint and the patient's bone.

[0027] According to yet another aspect, an orthopedic surgical planning method includes, all by a computer system, identifying the anatomical parameters of a patient's knee joint, determining the target performance of the knee joint based on the pre-implant anatomical structure of the patient, determining the predicted performance of several implant alignment options of the artificial knee joint when implanted in the knee joint, identifying the recommended implant alignment of the artificial knee joint based on the target performance and the predicted performance of the artificial knee joint, and creating a surgical plan for each patient incorporating the recommended implant alignment. Determining the predicted performance may include the computer system inputting the type and size of the artificial knee joint and the identified anatomical parameters into a mathematical model representing the dynamics and mechanical properties of the artificial knee joint when implanted, and operating the mathematical model to determine the predicted performance of the artificial knee joint for each alignment of several implant alignment options.

[0028] In some embodiments, the predicted performance may include the predicted dynamics for several implant alignment options for the artificial knee joint when implanted in the knee joint.

[0029] In some embodiments, determining the target performance of the knee joint may include determining the target ligament elongation of the ligaments of the knee joint over a range of flexion. Operating a mathematical model to determine the predicted performance of a patient's knee joint may include determining the predicted ligament elongation of the ligaments over a range of flexion. Identifying a recommended implant alignment may include, for each alignment of several implant alignment options, calculating the difference between the target ligament elongation and the predicted ligament elongation over a range of flexion, and recommending the alignment corresponding to the minimum difference between the target ligament elongation and the predicted ligament elongation over a range of flexion.

[0030] In some embodiments, calculating the difference between the target ligament elongation and the predicted ligament elongation over a range of flexion may include calculating the sum of the absolute differences between the target ligament elongation and the predicted ligament elongation at several positions over the entire range of flexion. Recommending the alignment corresponding to the minimum difference may include recommending the alignment corresponding to the minimum sum of the absolute differences.

[0031] In some embodiments, the predicted performance may include the predicted mechanical properties for several implant alignment options for the artificial knee joint when implanted in the knee joint.

[0032] In some embodiments, identifying the recommended implant alignment of the artificial knee joint based on the target performance and the predicted performance of the artificial knee joint may include selecting the implant alignment based on one of the predicted contact forces between the components of the artificial knee joint and the predicted contact forces between the components of the artificial knee joint and the patient's bone.

[0033] According to yet another aspect, an orthopedic surgical planning method includes, all by a computer system, identifying anatomical parameters of a patient's knee joint, determining a subset of implant alignment options for an artificial knee joint when implanted in the knee joint, each implant alignment from a larger set of implant alignment options that meet one or more flexion-extension gap criteria, determining a target ligament elongation of at least one ligament of the knee joint over a flexion range based on the patient's pre-implant anatomical structure, determining a predicted ligament elongation of at least one ligament over the flexion range for each implant alignment in the subset of implant alignment options, identifying at least one recommended implant alignment for the artificial knee joint based on the target ligament elongation and the predicted ligament elongation, and incorporating the at least one recommended implant alignment into a surgical plan for each patient.

[0034] In some embodiments, the computer system does not determine a predicted ligament elongation for each implant alignment in a larger set of implant alignment options other than the subset of implant alignment options.

[0035] In some embodiments, determining a subset of implant alignment options that meet one or more flexion-extension gap criteria may include, for each implant alignment in a larger set of implant alignment options, virtually positioning the femoral prosthesis of the artificial knee joint relative to the femur of the knee joint according to the implant alignment, determining a predicted gap between the virtually positioned femoral prosthesis and a planned tibial resection plane according to the implant alignment, and comparing the predicted gap to a reference value.

[0036] In some embodiments, determining the predicted gap for each implant alignment may include finding the minimum distance between a virtually positioned femoral prosthesis and a planned tibial resection plane. Determining the predicted gap for each implant alignment may include calculating the distance between a predetermined point along the sagittal surface of the virtually positioned femoral prosthesis and the planned tibial resection plane.

[0037] Determining a subset of implant alignment options that meet one or more flexion-extension gap criteria may further include calculating the predicted gap between a virtually positioned femoral prosthesis and a planned tibial resection plane at each of a plurality of flexion-extension angles, determining the absolute difference between the predicted gap at each of the plurality of flexion-extension angles and a corresponding reference value, summing the absolute differences, and generating a performance metric for each implant alignment in a larger set of implant alignment options. Generating a performance metric may further include combining an inner performance metric and an outer performance metric.

[0038] In some embodiments, identifying at least one recommended implant alignment may include calculating the difference between a target ligament elongation and a predicted ligament elongation for each implant alignment in a subset of implant alignment options, and recommending the implant alignment corresponding to the minimum difference between the target ligament elongation and the predicted ligament elongation over a range of flexion. Calculating the difference between the target ligament elongation and the predicted ligament elongation over a range of flexion may include calculating the sum of the absolute differences between the target ligament elongation and the predicted ligament elongation at several postures over the entire range of flexion. Recommending the implant alignment corresponding to the minimum difference may include recommending the implant alignment corresponding to the minimum sum of the absolute differences.

[0039] In some embodiments, at least one ligament may be at least one of the medial collateral ligament, lateral collateral ligament, posterior cruciate ligament, anterolateral ligament, and posterior capsule of the patient's knee joint. Determining the target ligament elongation of at least one ligament of the knee joint over a range of flexion may include determining the respective target ligament elongations of the medial collateral ligament, lateral collateral ligament, and posterior cruciate ligament of the knee joint. Determining the predicted ligament elongation of at least one ligament over a range of flexion may include determining the respective predicted ligament elongations of the medial collateral ligament, lateral collateral ligament, and posterior cruciate ligament. Calculating the difference between the target ligament elongation and the predicted ligament elongation may include calculating the absolute difference between the respective target ligament elongation and the predicted ligament elongation of the medial collateral ligament, lateral collateral ligament, and posterior cruciate ligament, and calculating the sum of the absolute differences of the medial collateral ligament, lateral collateral ligament, and posterior cruciate ligament. Recommending an implant alignment may include recommending an implant alignment that minimizes the sum of the absolute differences.

[0040] In some embodiments, identifying the anatomical parameters of the knee joint may include identifying the anatomical parameters of the patient's knee joint based on a set of medical images of the patient's preoperative anatomical structure. The method may include obtaining a set of medical images through at least one of a CT scan, radiography, and ultrasound imaging of the patient's preoperative anatomical structure.

[0041] In some embodiments, the method can further include performing an intraoperative bone alignment procedure at the patient's knee joint using a computer system to identify some landmarks on the patient's pre-implant anatomical structure, and the anatomical parameters include some landmarks. The method may further include tracking the dynamics of the knee joint over a range of flexion using a computer system, and determining the target dynamics of the knee joint includes determining the target dynamics based on the dynamics of the knee joint. In some embodiments, the method may further include inserting an articular extension device into the knee joint and operating the articular extension device to adjust the distance between the patient's femur and tibia over a range of flexion.

[0042] In some embodiments, for each implant alignment of a subset of implant alignment options, determining the predicted ligament elongation of at least one ligament over a range of flexion may include inputting the anatomical parameters, artificial knee joint size, artificial knee joint type, and implant alignment into a linear response model and operating the linear response model to generate the predicted ligament elongation.

[0043] According to yet another aspect, an orthopedic surgical planning system automatically identifies anatomical parameters of a patient's knee joint and, for each implant alignment from a larger set of implant alignment options that meet one or more flexion-extension gap criteria, determines a target ligament elongation of at least one ligament of the knee joint over a flexion range based on the patient's pre-implant anatomical structure when implanted in the knee joint, determines a predicted ligament elongation of at least one ligament over the flexion range for each implant alignment in the subset of implant alignment options, identifies at least one recommended implant alignment for the artificial knee joint based on the target ligament elongation and the predicted ligament elongation, and may comprise a computer system configured to incorporate the at least one recommended implant alignment into a patient-specific surgical plan.

[0044] In some embodiments, the orthopedic surgical planning system may further comprise a bone registration tool for identifying some landmarks on the patient's pre-implant anatomical structure using a computer system. The anatomical parameters may include some landmarks. In some embodiments, the orthopedic surgical planning system may further comprise a robotic system including a cutting tool configured to excise one or more of the patient's bones according to a patient-specific surgical plan.

[0045] According to yet another aspect, an orthopedic surgical planning method may include, all by a computer system, identifying anatomical parameters of a patient's knee joint, determining target kinematics of the knee joint based on the patient's pre-implant anatomical structure, determining one or more predicted gaps between a femur prosthesis and a tibia prosthesis when implanted in the knee joint for each of a first set of implant alignment options, determining a second set of implant alignment options from among the first set of implant alignment options based at least in part on the one or more predicted gaps, determining predicted kinematics for each implant alignment of the second set of implant alignment options, identifying at least one recommended implant alignment of the femur prosthesis and the tibia prosthesis based on the target kinematics and the predicted kinematics, and incorporating the at least one recommended implant alignment into a patient-specific surgical plan. Determining the predicted kinematics may include the computer system inputting the identified anatomical parameters into a mathematical model representing the kinematics of the femur prosthesis and the tibia prosthesis when implanted, and operating the mathematical model to determine the predicted kinematics of the femur prosthesis and the tibia prosthesis for each implant alignment of the second set of implant alignment options.

[0046] The first set of implant alignment options may reflect a first set of degrees of freedom for positioning the femoral prosthesis and the tibial prosthesis relative to the bones of the knee joint, the second set of implant alignment options may reflect a second set of degrees of freedom for positioning the femoral prosthesis and the tibial prosthesis relative to the bones of the knee joint, and at least one degree of freedom of the second set of degrees of freedom may be restricted relative to the first set of degrees of freedom. In some embodiments, a plurality of degrees of freedom of the second set of degrees of freedom may be restricted relative to the first set of degrees of freedom. In some embodiments, at least one degree of freedom of the first set of degrees of freedom may be fixed relative to the second set of implant alignment options.

[0047] In some embodiments, the computer system may use the target kinematics and the known geometric shapes of the femoral prosthesis and the tibial prosthesis to determine one or more predicted gaps. Determining one or more predicted gaps may include determining, for each implant alignment of the first set of implant alignment options, the predicted gap between the femoral prosthesis and the tibial prosthesis at each of a plurality of flexion-extension angles.

[0048] In some embodiments, determining one or more predicted gaps may include determining, for each implant alignment of the first set of implant alignment options, the predicted medial gap between the femoral prosthesis and the tibial prosthesis and the predicted lateral gap between the femoral prosthesis and the tibial prosthesis. Determining one or more predicted gaps may include determining, for each implant alignment of the first set of implant alignment options, the predicted medial gap and the predicted lateral gap at each of a plurality of flexion-extension angles.

[0049] In some embodiments, determining a second set of implant alignment options from among a first set of implant alignment options may include selecting an implant alignment in which one or more predicted gaps are within a target range.

[0050] In some embodiments, determining the target kinematics of a knee joint may include determining the target ligament elongation of at least one ligament of the knee joint over a flexion range. Operating a mathematical model to determine the predicted kinematics of a femoral prosthesis and a tibial prosthesis may include determining the predicted ligament elongation of at least one ligament over a flexion range. Identifying at least one recommended implant alignment may include, for each implant alignment of a second set of implant alignment options, calculating a difference between the target ligament elongation and the predicted ligament elongation over a flexion range, and recommending an implant alignment corresponding to a minimum difference between the target ligament elongation and the predicted ligament elongation over a flexion range.

[0051] In some embodiments, calculating a difference between the target ligament elongation and the predicted ligament elongation over a flexion range may include calculating a sum of absolute differences between the target ligament elongation and the predicted ligament elongation at several postures over the entire flexion range. Recommending an implant alignment corresponding to a minimum difference may include recommending an implant alignment corresponding to a minimum sum of absolute differences.

[0052] In some embodiments, the at least one ligament may be at least one of the medial collateral ligament, the lateral collateral ligament, the posterior cruciate ligament, the anterolateral ligament, and the posterior capsule of the patient's knee joint.

[0053] In some embodiments, the mathematical model may be a linear response model configured to receive anatomical parameters, prosthesis sizes, prosthesis types, and implant alignments as inputs and generate, as outputs, the predicted dynamics of the femoral prosthesis and the tibial prosthesis. The linear response model may be further configured to generate, as outputs, the predicted mechanical properties of the femoral prosthesis and the tibial prosthesis, the mechanical properties including at least one of (i) the predicted contact force between the femoral prosthesis and the tibial prosthesis and (ii) the predicted contact force between the femoral prosthesis and the tibial prosthesis and the bone of the knee joint.

[0054] In some embodiments, the method may include adding, to the surgical plan for each patient, several planned resections of the patient's femur and at least one planned resection of the patient's own tibia to prepare the patient's bone to receive the femoral prosthesis and the tibial prosthesis with the recommended implant alignment.

[0055] According to yet another aspect, the orthopedic surgical planning system may include a computer system configured to execute any of the methods described in this disclosure.

[0056] According to yet another aspect, one or more computer-readable media can store multiple instructions that, when executed by one or more processors of a computer system, cause the one or more processors to execute any of the methods described in this disclosure.

Brief Description of the Drawings

[0057] The detailed description specifically refers to the following drawings.

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[0058] The concepts of the present disclosure are capable of various modifications and alternative forms, but specific exemplary embodiments thereof are shown in the drawings and described in detail herein. However, it is not intended to limit the concepts of the present disclosure to the specific forms disclosed, but on the contrary, it is intended to cover all modifications, equivalents, and alternatives included within the spirit and scope of the invention as defined by the appended "claims".

[0059] Terms such as anterior, posterior, medial, lateral, superior, inferior, etc., representing anatomical references, may be used throughout this specification with respect to the orthopedic implants and surgical instruments described herein, as well as the patient's native anatomical form. Such terms have meanings well understood in both the study of anatomy and the field of orthopedic surgery. The use of such anatomical reference terms in the description and "claims" provided is intended to be consistent with their well-understood meanings unless otherwise specified.

[0060] References to "one embodiment", "an embodiment", "exemplary embodiments", etc. in this specification indicate that the embodiment being described may include a particular element, structure, or feature, but not all embodiments necessarily include that particular element, structure, or feature. Further, such phrases do not necessarily refer to the same embodiment. Further, when a particular element, structure, or feature is described in connection with an embodiment, it is considered within the knowledge of one skilled in the art to implement such element, structure, or feature in connection with other embodiments, whether or not explicitly stated. In addition, it should be understood that items included in a list in the form of "at least one of A, B, and C" may mean (A), (B), (C), (A and B), (A and C), (B and C), or (A, B, and C). Similarly, items listed in the form of "at least one of A, B, or C" may mean (A), (B), (C), (A and B), (A and C), (B and C), or (A, B, and C).

[0061] In the drawings, some structural or method elements may be shown in a particular arrangement and / or order. However, it should be recognized that such a particular arrangement and / or order may not be necessary. Rather, in some embodiments, such elements may be arranged in a different form and / or order than that shown in the explanatory drawings. In addition, when a structural or method element is included in a particular figure, it does not imply that such an element is necessary in all embodiments, and in some embodiments it may not be included, or it may be combined with other elements.

[0062] Referring now to FIG. 1, an orthopedic artificial knee joint 100 includes a femur prosthesis 102, a tibial tray 104, and a tibial insert 106. The femur prosthesis 102 is configured to be fixed to the surgically prepared distal end of the patient's femur 110. Similarly, the tibial tray 104 is configured to be fixed to the surgically prepared proximal end of the patient's tibia 112.

[0063] The tibial insert 106 is coupled to the tibial tray 104 and configured to provide an articulating surface for the femoral prosthesis 102 during flexion of the patient's knee joint. For example, the femoral prosthesis 102 includes a medial condyle and a lateral condyle, which are shaped and configured to engage corresponding medial and lateral articulating surfaces of the tibial insert 106. (A portion of the femoral prosthesis 102 also engages the patient's patella 114 during flexion.) The articulating surfaces of the tibial insert 106 may be symmetric or asymmetric. In some embodiments, an asymmetric tibial insert may be configured to provide more constraint by the medial condyle of the femoral prosthesis 102 and less constraint by the lateral condyle, thereby allowing further rotation of the lateral condyle about the more constrained medial condyle.

[0064] When the femoral prosthesis 102 is positioned on the surgically prepared distal end of the patient's femur 110, the femoral prosthesis 102 is spaced from the surgically prepared proximal end of the patient's tibia 112 by a gap 116 (within which the tibial tray 104 and the tibial insert 106 are positioned). The gap 116 may be defined with reference to an inner gap 116a at the minimum distance between the surgically prepared tibia 112 and the medial condyle of the femoral prosthesis 102, and an outer gap 116b at the minimum distance between the surgically prepared tibia 112 and the lateral condyle of the femoral prosthesis 102. Alternatively, the gap 116 may be defined in different ways, such as with reference to one or more predetermined points on the femoral prosthesis 102 (e.g., one or more predetermined points along the sagittal curve of the femoral prosthesis 102, although other points on the femoral prosthesis 102 may be closer to the tibia 112), and / or with reference to the tibial tray 104 or the tibial insert 106 (rather than the surgically prepared tibia 112 itself). In many cases, the size of the gap 116 (including the size(s) of the inner gap 116a and the outer gap 116b) will vary as the patient's femur 110 and tibia 112 are moved through their full range of motion, including flexion and extension. The sizes of the gaps 116, 116a, 116b are often measured at various flexion-extension angles, and thus the gaps 116, 116a, 116b are often referred to as "flexion-extension gaps."

[0065] The femoral prosthesis 102 and the tibial tray 104 are formed from a metallic material such as cobalt chrome or titanium, although in other embodiments they may be formed from other materials such as ceramic materials, polymeric materials, biologic materials, etc. The tibial insert 106 is typically formed from a polymeric material such as ultra-high molecular weight polyethylene (UHMWPE), although in other embodiments it may be formed from other materials such as ceramic materials, metallic materials, biologic materials, etc. It should also be understood that the tibial tray and the tibial insert may be provided as a single, integral tibial prosthesis.

[0066] Different types of orthopedic artificial knee joints can be used for different patients based on various factors including the health condition of the patient's knee joint. For example, as shown in Figure 2A, an exemplary orthopedic artificial knee joint system 200 includes two different types of femoral prostheses 202, 204, four different types of tibial inserts 212, 214, 216, 218, and two different types of tibial trays 222, 224. The femoral prosthesis 202 is embodied as a posterior-stabilized (PS) femoral prosthesis and includes a posterior cam. The femoral prosthesis 202 is configured to articulate with either a fixed-bearing tibial insert 212 or a rotating-platform tibial insert 214, each of which includes a post configured to contact the posterior cam of the femoral prosthesis 202. The tibial insert 212 is configured to connect to a tibial tray 222 that is a fixed or non-rotating tibial tray. That is, the tibial insert 212 is restricted from rotating relative to the tibial tray 222 via corresponding locking mechanisms incorporated into the tibial insert 212 and the tibial tray 222. The rotating-platform tibial insert 214 is configured to connect to a tibial tray 224 that is a movable or rotating tibial tray. That is, the tibial insert 212 is free to rotate around its stem relative to the tibial tray 224.

[0067] The femoral prosthesis 204 is embodied as a cruciate-retaining (CR) femoral prosthesis and, unlike the femoral prosthesis 202, does not include a posterior cam. The femoral prosthesis 204 is configured to articulate with either the tibial insert 216 or the tibial insert 218, each of which lacks a post and can be embodied as either a symmetric tibial insert or an asymmetric tibial insert as described above. The tibial insert 216 is a fixed tibial insert or a non-rotating tibial insert and is configured to connect to the tibial tray 222. In contrast, the tibial insert 218 is a movable insert or a rotating insert and is configured to connect to the tibial tray 224.

[0068] In use, an orthopedic surgeon may select a desired combination of orthopedic artificial knee joint components and sizes based on the patient's bone anatomy (e.g., the extent of damage or deterioration of the patient's knee joint) and / or the surgeon's preference. Although only a single size of each of the above-described components of the orthopedic artificial knee joint system 200 is shown in FIG. 2A, it should be understood that the orthopedic artificial knee joint system 200 may include additional sizes of each of these components to accommodate the bone anatomies of various patients. The orthopedic artificial knee joint system 200 may also include respective versions of each of these components that are specifically designed for use in only the left or right knee joint.

[0069] Referring now to FIG. 2B, the condyles of exemplary femur prostheses 102, 202, 204 each include a condylar surface 230 that is convexly curved in the sagittal plane. Exemplarily, the condylar surface 230 is formed from a number of curved surface sections 232, 234, 236, 238, 240, and 242, each of which is in contact with an adjacent curved surface section. Each curved surface section 232, 234, 236, 238, 240, and 242 contacts a tibial insert 106 (or one of tibial inserts 212, 214, 216, 218) through a different range of flexion. For example, curved surface sections 232, 234 of the condylar surface 230 contact the tibial insert 106 during initial flexion (including extension). Curved surface sections 236, 238 of the condylar surface 230 contact the tibial insert 106 during mid-flexion. Also, curved surface sections 240, 242 of the condylar surface 230 contact the tibial insert 106 during end-flexion. Each of the curved surface sections 232, 236, 238, 240, and 242 is defined by a respective constant radius of curvature R1, R3, R4, R5, and R6. However, the curved surface section 234 is defined by a plurality of radial lines 244 (rather than a constant radius of curvature), and the curved surface section 234 has a continuously decreasing radius of curvature and gradually transitions from the radius of curvature R1 of the curved surface section 232 to the radius of curvature R2 that contacts the curved surface section 236. Further details regarding the sagittal curvature of exemplary femur prostheses 102, 202, 204 are described in U.S. Patent Application Publication No. 2022 / 0008207, the entire disclosure of which is incorporated herein by reference. It is contemplated that other embodiments may use femur prostheses having different sagittal curvatures, including sagittal curvatures defined by a single constant radius.

[0070] In preparing for and performing knee replacement surgery, an orthopedic surgeon determines the type, size, and implant alignment of each prosthetic component to be implanted in the patient's knee joint. The surgeon may adjust or select the alignment of each prosthetic component along six degrees of freedom (medial-lateral, anterior-posterior, superior-inferior, flexion-extension, adduction-abduction, and internal-external) to achieve the desired mechanical properties of the knee joint. Once the type, size, and implant alignment are determined, the surgeon may excise the patient's femur and tibia (and in some cases, the patella) and prepare the bone to receive the prosthetic components. In the case of total knee arthroplasty, the surgeon excises the patient's femur to form at least five planes and excises the patient's tibia to form a single plane. As will be described in more detail below, the surgeon may prepare the patient's bone using manual instruments such as, for example, the ATTUNE® INTUITION™ Instruments commercially available from DePuy Synthes Products, Inc., and / or robotic devices such as, for example, the VELYS® Robotic-Assisted Solution also commercially available from DePuy Synthes Products, Inc.

[0071] Referring now to FIG. 3, an exemplary computer system 300 for determining the placement of a knee prosthesis, such as knee prosthesis 100, includes a knee surgery planning and analysis device 302 and an imaging device 304 communicatively coupled to the analysis device 302 via a network 306. In use, an orthopedic surgeon can operate the analysis device 302 to select implant sizes and types, determine the recommended implant alignment of the femur prosthesis 102 and tibia prosthesis 104 of the knee prosthesis 100, develop a surgical plan incorporating the recommended implant alignment, and guide the preparation of the patient's bone to receive the knee prosthesis 100. As will be described in more detail below, the surgeon can perform the surgical plan using manual instruments and / or a robotic surgical device 602 programmed and configured according to the surgical plan.

[0072] As will be described in more detail below with reference to FIG. 6, the analysis device 302 is configured to identify anatomical parameters that can be used to determine a patient-specific target motion. The anatomical parameters may be identified automatically and / or in a surgeon-assisted manner based on medical images, a preoperative model of the patient's knee joint, and / or landmarks positioned during the surgical procedure. The analysis device 302 also creates predictions of the performance of the artificial knee joint in various possible implant alignments based on the type and size of the artificial knee joint and the patient's anatomical parameters. The analysis device 302 uses mathematical models and rule-based decision criteria to identify a recommended implant alignment based on the patient-specific target motion and the predicted performance of the artificial knee joint, and is configured to incorporate the recommended implant alignment into the patient-specific surgical plan.

[0073] Referring now to FIG. 4, the analysis device 302 may be embodied as any type of computer device or system capable of performing the functions described herein. For example, the analysis device 302 may be embodied as a desktop computer, a surgical navigation computer, a laptop computer, a tablet computer, a smartphone, a mobile computer, a smart device, a wearable computer system, or other computer device or system. The knee surgery planning and analysis device 302 may be separated from the operating room (e.g., in an orthopedic surgeon's office) or located within the operating room.

[0074] As shown in FIG. 4, an exemplary analysis device 302 includes a controller 310, an input / output (I / O) subsystem 312, a data storage 314, a display 316, and a communication system 318, and in some embodiments, also includes one or more peripheral devices 320. Of course, it should be understood that the analysis device 302 may include additional components or other components in other embodiments, such as those commonly found in typical computer devices. In addition, in some embodiments, one or more of the exemplary components may be incorporated into or form part of another component.

[0075] The controller 310 can be embodied as any type of controller, functional block, digital logic, or other component, device, circuit, or collection thereof capable of performing the functions described herein. In the illustrated embodiment, the controller 310 includes a processor 322 and a memory 324. The processor 322 can be embodied as any type of processor capable of performing the functions described herein. For example, the processor 322 can be embodied as a single or multi-core processor(s), a digital signal processor, a microcontroller, or other processor or processing / control circuit. Similarly, the memory 324 can be embodied as any type of volatile and / or non-volatile memory or data storage capable of performing the functions described herein. During operation, the memory 324 can store various data, such as an operating system, applications, executable software, programs, libraries, drivers, and various data, etc., and the software used during the operation of the analysis device 302, which can be executed by the processor 322 or used in other ways.

[0076] The controller 310 is communicatively coupled to other components of the analysis device 302 via an I / O subsystem 312, which may be embodied as circuitry and / or components for facilitating input / output operations between the controller 310 (e.g., processor 322 and / or memory 324) and other components of the analysis device 302. For example, the I / O subsystem 312 may be embodied as, or otherwise include, a memory controller hub, an input / output control hub, a firmware device, a communication link (i.e., a point-to-point link, a bus link, a wire, a cable, a light guide, a trace on a printed circuit board, etc.), and / or other components and subsystems for facilitating input / output operations. In some embodiments, the I / O subsystem 312 may form part of a system-on-chip (SoC) and may be incorporated onto a single integrated circuit chip along with the controller 310 (e.g., processor 322 and main memory 324) and other components of the controller 310. Further, in some embodiments, the memory 324, or a portion of the memory 324, may be incorporated into the processor 322.

[0077] The data storage 314 may be embodied as any type of device(s) configured for short-term or long-term storage of data, such as, for example, a solid state drive, a hard disk drive, memory devices and circuits, memory cards, non-volatile flash memory, or other data storage devices. In an exemplary embodiment, the data storage 314 stores various data used by the analysis device 302 to perform the functions described herein. For example, the data storage 314 may store one or more medical images 330 of a patient. The medical images 330 may be generated by the imaging device 304, but may also be transmitted to the analysis device 302 via the network 306 for local storage in the data storage 314. As will be described in more detail below, the medical images may be embodied as X-ray images of a patient's knee joint, computed tomography (CT) images, magnetic resonance imaging (MRI) images, three-dimensional models, and / or other medical images, models, or related data.

[0078] The data storage 314 may also store one or more image processing modules 332 that may be embodied as one or more mathematical models or algorithms (e.g., machine learning algorithms) capable of analyzing medical images and determining anatomical parameters, including related anatomical landmarks. As will be described in more detail below, the analysis device 302 may use the landmark model 332 to determine anatomical parameters in an automated manner and / or may determine anatomical parameters in a manual manner based on annotations of medical images received from an orthopedic surgeon.

[0079] Furthermore, data storage device 314 can store at least one statistical shape function model 334. As will be described in more detail below, the statistical shape function model 334 is a mathematical model that matches or correlates the shape of a patient's knee bones (femur, tibia, patella) to a library of "healthy" knee bones and their associated kinematics. The statistical shape function model 334 is configured to determine the target kinematics of a patient's knee joint based on the kinematics associated with "healthy" knee bones of similar shape.

[0080] Data storage 314 may further store a performance mathematical model 336. As will be described in more detail below, the performance mathematical model 336 uses anatomical parameters as inputs to generate, across several implant alignments (i.e., combinations of the position and orientation of the tibial tray 104 and the femoral prosthesis 102 when implanted), the joint kinematics and mechanical properties generated by each orthopedic artificial knee joint (i.e., the type and size of the orthopedic artificial knee joint). In this way, the mathematical model 336 can quickly generate or create the joint kinematics and mechanical properties of a specific orthopedic artificial knee joint in a specific alignment, because such calculations have been performed beforehand. In an exemplary embodiment, the performance mathematical model 336 is embodied as a linear response model, but in other embodiments it may be embodied as other types of models. By way of example, in some embodiments, the performance mathematical model 336 is embodied as a machine learning model.

[0081] The display 316 can be embodied as any type of display capable of displaying information to a user (e.g., an orthopedic surgeon) of the analysis device 302. For example, the display 316 can be embodied as a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED), a cathode ray tube (CRT) display, a plasma display, and / or other display devices. In some embodiments, the display 316 may include a touch screen configured to receive input from the orthopedic surgeon based on tactile interaction. Additionally, in some embodiments, the display 316 or a duplicate display 316 can be separated from but communicatively coupled to the analysis device 302.

[0082] The communication subsystem 318 can be embodied as any type of communication circuitry, device, or collection thereof that enables communication between the analysis device 302 and the imaging device 304 and / or other devices of the computer system 300. To do so, the communication subsystem 318 may be configured to perform such communication using any one or more communication technologies (e.g., wired or wireless communication) and associated protocols (e.g., Ethernet, Bluetooth®, Wi-Fi®, WiMAX, LTE, 5G, etc.).

[0083] The one or more peripheral device(s) 320 may include any number of additional peripheral or interface devices, such as, for example, other input / output devices, storage devices, etc. The particular devices included in the peripheral device 320 may depend, for example, on the type and / or intended usage of the analysis device(s) 302.

[0084] Referring again to FIG. 3, the imaging device 304 can be embodied as any type of device or set of devices capable of generating medical images of a patient's bone anatomical structure preoperatively and / or intraoperatively. In an exemplary embodiment, the imaging device 304 is embodied as a CT scanner capable of generating CT medical images. However, in other embodiments, the imaging device 304 can be embodied as an imaging device capable of generating X-ray medical images and / or three-dimensional models. It is contemplated that the imaging device 304 need not be used in all embodiments.

[0085] The network 306 can be embodied as any type of communication network capable of facilitating communication between the analysis device 302 and the imaging device 304 (and other components of the computer system 300). Thus, the network 306 may include one or more networks, routers, switches, gateways, computers, and / or other intervening devices. For example, the network 306 can be embodied as, or otherwise include, one or more local area networks or wide area networks, cellular networks, publicly available global networks (e.g., the Internet), ad hoc networks, short-range communication networks or links, or any combination thereof. Also, for example, it should be understood that various modules of the analysis device 302, such as the models 334, 336, etc., may be network-based or cloud-based and may be remotely accessed through a suitably configured local computing system. It should also be understood that the modules of the analysis device 302 can also be accessed by or incorporated into the robotic surgical device 602.

[0086] Referring now to FIG. 5, an exemplary robotic surgical device 602 includes some of the same components as the analysis device 302. The same reference numbers are used to refer to those components, and the description of those components is not repeated with respect to the robotic surgical device 602. The robotic surgical device 602 includes a controller 310, an input / output (“I / O”) subsystem 312, data storage 314, a display 316, a communication system 318, and in some embodiments, one or more peripheral devices 320. The robotic surgical device 602 also includes a camera 618, an excision guide 620, one or more patient arrays 622, a bone alignment tool 626, and a bone extension device 628. It should be understood, of course, that the surgical device 602 may include additional or different components such as those commonly found in a typical computer device. Additionally, in some embodiments, one or more of the exemplary components may be incorporated into or form a part of another component. It should also be understood that in some embodiments, some of these components may be omitted. For example, some embodiments of the robotic surgical device 602 may not include the bone extension device 628 or the excision guide 620. In such embodiments, the use of the omitted component may be omitted or modified to use other components.

[0087] The camera 618 may be embodied as any type of camera or imaging device capable of capturing an image of the patient's bone anatomy, particularly visual markers attached to the patient's bone anatomy. For example, in some embodiments, the camera 618 may be embodied as an infrared camera. Additionally, in some embodiments, the camera 618 may include an array of cameras to assist in identifying the location of the patient's bone anatomy in three-dimensional space.

[0088] The resection guide 620 may be embodied as any type of guide controllable by the surgical device 602 to control the resection of the patient's bone anatomy. For example, the resection guide 620 may be embodied as a robotic arm attached to or attachable to an orthopedic saw to position and control the saw blade cutting plane. In other embodiments, the resection guide 620 may include an automated cutting tool operated by the controller 310. In yet other embodiments, the resection guide 620 may be embodied as or otherwise include one or more cutting blocks or guides. In yet other embodiments, the resection guide 620 may be virtually implemented using augmented reality or another method to show the planned cutting plane to the surgeon.

[0089] The patient array 622 is embodied as various brackets and / or other devices configured to attach to the patient's bone anatomy. Once the patient array 622 is attached, it is detectable by the camera 618, enabling the surgical device 602 to monitor the movement of the patient's leg in three-dimensional space and allowing the analysis device 302 to determine the kinematics of the patient's knee joint based on such movement, as will be described in more detail below.

[0090] The bone alignment tool 626 is embodied as a stylus or pointer that can be used by the surgeon to contact various locations on the patient's tibia and femur to obtain anatomical parameters (e.g., landmarks and bone axes). The alignment tool 626 is detectable by the camera 618 and records the position and orientation of the desired anatomical parameters.

[0091] The robotic surgical device 602 also includes a bone extension tool 628 configured to adjust the distance between the distal end of the femur and the proximal end of the tibia. The extension tool 628 may be a manual tool, such as the instrument shown and described in U.S. Patent No. 10,945,777, for example. In other embodiments, the extension tool 628 may be an electromechanical tool controlled by the analysis device 302 to adjust the distance between the patient's bones.

[0092] Referring now to FIG. 6, the analysis device 302 includes a module 350 configured to execute a method 400 for formulating a patient-specific surgical plan for a patient. For example, the method 400, or a portion thereof, may be stored on the analysis device 302 and embodied as a set of executable instructions executable by the analysis device 302. It should also be understood that the operation of the module 350 and the method 400 may be performed by one or more components of the analysis device 302 and / or a device communicatively coupled to the analysis device 302 (including, for example, the surgical device 602). In some embodiments of the method 400, creating a patient-specific surgical plan for a patient may be embodied as modifying a portion of an existing patient-specific surgical plan previously created for that patient.

[0093] In block 402 of method 400, the analysis device 302 determines various anatomical features or parameters of the patient's knee joint. The determined or specified anatomical parameters can be embodied as any anatomical landmark or other parameter (e.g., relative dimensions of the anatomical structure of the patient's bone) that facilitates or improves the determination of the target kinematics and / or predicted performance of the artificial knee joint. The particular landmarks used may vary depending on various factors such as the anatomical structure of the patient's bone, the size and type of the artificial knee joint 100, and / or other factors. For example, in an exemplary embodiment, the anatomical parameters used include the center and radius of the medial femoral condyle, the center and radius of the lateral femoral condyle, the posterior tilt(s) of the tibial plateau (medial and lateral), the varus-valgus angle of the tibial articular surface, the rotational angle relative to the tibial tuberosity, and the rotational angle relative to the fibular head. In other embodiments, the anatomical parameters may additionally or alternatively include any anatomical landmarks of the tibial center, femoral center, femoral condyles (medial and lateral), and malleolar prominences, e.g., the attachment site of the posterior cruciate ligament (PCL), and the most distal and posterior point(s) of the medial and / or lateral femoral condyle(s). The anatomical parameters may also include axes such as the tibial sagittal axis, the Whiteside line, and the mechanical axes of the femur and tibia. Additional anatomical parameters can facilitate the determination or calculation of various coordinate systems (e.g., the anatomical femoral coordinate system, the anatomical tibial coordinate system, and / or the femoral condyle coordinate system).

[0094] The anatomical parameters can be derived from intraoperative bone alignment (described in more detail below), medical images of the patient's preoperative anatomical structure, or a combination of bone alignment and medical images. The anatomical parameters are determined with the patient's knee joint placed in various functional positions. The analysis device 302 can receive any type and number of appropriate medical images that facilitate the determination of the patient's target kinematics, as described in more detail below.

[0095] Analysis device 302 also determines, at block 404, the target kinematics for each patient. For example, in an exemplary embodiment, analysis device 302 uses the identified anatomical parameters (or a subset thereof) as input to determine the target kinematics for each patient based on a patient-specific statistical shape function model. In other embodiments, the target kinematics for each patient may be measured directly from the patient (e.g., during surgery) by moving the patient's leg over its range of motion while camera 618 observes patient array 622 attached to the patient's femur 110 and tibia 112, enabling surgical device 602 to monitor the movement of the patient's leg in three-dimensional space and allowing analysis device 302 to determine the kinematics of the patient's knee joint based on such movement. As a result of block 404, analysis device 302 now has knowledge of the positions of femur 110 and tibia 112 relative to each other at various flexion angles over the range of motion (the positions can also be converted to the global reference frame of camera 618, if desired).

[0096] The statistical shape function model illustratively used at block 404 is embodied as a mathematical model that uses anatomical parameters to match or correlate the shape of the patient's knee bones defined by the anatomical parameters to a library of "healthy" knee bones. The library of knee bones can be established based on a pool of individuals with healthy knee joints. Illustratively, the target kinematics determined by the statistical shape function model include the varus-valgus angle, internal-external rotation, medial-lateral translation, anterior-posterior translation, and superior-inferior translation of the patient's bones over the flexion range. In other embodiments, the target kinematics may include additional or other kinematic parameters of the patient's knee joint. By matching or correlating the anatomical features or parameters of the patient's knee bones to a library of "healthy" joints and associated kinematics, the "healthy" or uninjured / undeteriorated kinematics of the patient's knee joint are determined and the target kinematics for each patient achieved through implantation of an artificial knee joint are provided.

[0097] The target dynamics include characteristics related to the location / position, orientation, or movement of the patient's bone, regardless of the force or load on the patient's bone. In an exemplary embodiment, the target dynamics include patient-specific target ligament elongations over a flexion range of one or more ligaments of the patient's knee joint. For example, in an exemplary embodiment, the analysis device 302 may determine target ligament elongations for the medial collateral ligament (MCL), lateral collateral ligament (LCL), posterior cruciate ligament (PCL), anterolateral ligament, and posterior capsule of the patient's target knee joint. The femoral and tibial attachment sites of each of these ligaments may be specified in block 402 (e.g., using imaging or intraoperative measurements), or determined from other specified anatomical parameters in block 404 (e.g., using a statistical shape model). Next, the analysis device 302 applies the target dynamics using a transformation matrix to determine the relative positions of the patient's femur and tibia at several positions over the flexion range, and calculates the distance between the femoral attachment site and the tibial attachment site for each ligament at each of the several positions. These calculations provide an indication of the target length of each ligament over the flexion range.

[0098] As shown in FIG. 6, next, the analysis device 302 proceeds to determine, in block 406, the predicted performance of the artificial knee joint over a number of implant alignment options. As used herein, the term "implant alignment" refers to the position and orientation of the femoral prosthesis on the distal end of the patient's femur, as well as the position and orientation of the tibial tray on the proximal end of the patient's tibia. In some embodiments, implant alignment may also refer to the position and orientation of the patellar prosthesis. As noted above, the position and orientation of each prosthesis component can be varied with up to six degrees of freedom (medial-lateral, anterior-posterior, superior-inferior, flexion-extension, adduction-abduction, and internal-external). The analysis device 302 predicts the performance of the artificial knee joint over the flexion range when the artificial knee joint is implanted with each implant alignment in a set of implant alignment options. It will be appreciated that the number of implant alignments in a set of implant alignment options depends on the allowed values for each of the degrees of freedom for each of the prosthesis components of the artificial knee joint. For example, constraining the allowed values for one or more degrees of freedom of one or more prosthesis components of the artificial knee joint (e.g., reducing the range thereof) (e.g., restricting or further fixing the superior-inferior position of the femoral prosthesis) will reduce the number of implant alignments in a set of implant alignment options. As will be described in more detail below, the allowed values of the degrees of freedom can be constrained in several different ways, but by way of example and not limitation, include predetermined system constraints, surgeon preferences, and / or an initial evaluation algorithm that determines a narrowed set of implant alignment options that meet one or more specified criteria.

[0099] As will be described in more detail below, the predicted performance in the exemplary embodiments includes the predicted kinematics of the artificial knee joint when implanted in the knee joint. The predicted performance may also include the predicted mechanical properties of the artificial knee joint when implanted. The predicted mechanical properties may include the predicted forces or loads on the artificial knee joint or the patient's bone for each implant alignment. The predicted mechanical properties may also include interfacial and contact stresses that can be derived from the predicted forces. Such predictions may also include indications of implant alignments that present a greater or lesser risk of difficulties associated with the artificial knee joint, including, for example, detachment of the connection between the components of the prosthesis and the patient's bone, and wear performance of the tibial insert.

[0100] Referring now to FIG. 7, the analysis device 302 can predict the performance of the artificial knee joint in block 406 via process 500. In process 500, the anatomical parameters identified in block 402 are provided for use in determining the predicted performance. The analysis device 302 also selects, in process step 502, the type of artificial knee joint (at least the type for the tibial tray, tibial insert, and femoral prosthesis), and in step 504, selects the size of the artificial knee joint (at least the size for the tibial tray, tibial insert, and femoral prosthesis). It should be understood that the orthopedic surgeon may select the type and size from a menu of available types and sizes, or otherwise provide these selections to the analysis device 302. The analysis device 302 may also be configured to determine predictions for each possible combination of the type and size of each component of the artificial knee joint available within the implant system 200.

[0101] In process step 506, the analysis device 302 selects one implant alignment option from the set of implant alignment options under consideration. In an exemplary embodiment, the implant alignment of the artificial knee joint 100 is defined by nine degrees of freedom regarding the position and orientation of the femur prosthesis and the tibia prosthesis of the artificial knee joint. These nine degrees of freedom are the flexion-extension angle of the femur, the varus-valgus rotation of the femur, the internal-external rotation of the femur, the superior-inferior resection distance of the femur, the anterior-posterior position of the femur, the inclination of the tibia, the varus-valgus rotation of the tibia, the internal-external rotation of the tibia, and the insert thickness of the tibia. However, in other embodiments, additional degrees of freedom, fewer degrees of freedom, and / or different degrees of freedom may be used for the implant alignment options.

[0102] After selecting one implant alignment from the set of implant alignment options, the analysis device 302 proceeds to block 508 and, in the performance mathematical model 336, "positions" the selected prosthesis components for the selected implant alignment. To do so, the analysis device 302 accesses the configuration data of each prosthesis component, including the component size, shape, and geometric shape of the selected femur prosthesis as well as the selected tibial tray and tibial insert. The analysis device 302 inputs that data, along with the specified anatomical parameters, into the performance mathematical model 336 to configure the mathematical model to make a patient-specific prediction regarding the performance of the artificial knee joint when positioned in the selected implant alignment in the patient's knee joint. In this way, the analysis device 302 virtually implants the selected prosthesis components in the patient's knee joint with the selected implant alignment.

[0103] Next, in block 510, the analysis device 302 operates the performance mathematical model 336 to predict the performance of the selected prosthesis component. The performance mathematical model 336 in the exemplary embodiment is a linear response model, which is derived from pre-computing a mathematical model for each combination of the type and size of the orthopedic artificial knee joint, the combination of alignment parameters, and the combination of anatomical parameters. In this way, the linear response model defines the pre-computed performance for each combination of input parameters, which significantly improves the calculation speed of the linear response model compared to performing such calculations during "runtime". Thus, the model is configured to predict the performance of the selected orthopedic artificial knee joint 100.

[0104] As described above, the linear response model 336 includes a model of joint dynamics (and, in some embodiments, mechanical properties), and the model 336 can determine the predicted dynamics (and, in some embodiments, mechanical properties) of the selected prosthesis component in the selected implant alignment based on the size and type of the prosthesis component and the specified anatomical parameters over the flexion range. In the exemplary embodiment, the predicted dynamics determined in block 510 includes the predicted ligament elongation of one or more ligaments of the patient's knee joint over the flexion range. By doing so, as described above, the analysis device 302 can predict the ligament elongation of the medial collateral ligament (MCL), lateral collateral ligament (LCL), posterior cruciate ligament (PCL), anterolateral ligament, and posterior capsule of the relevant knee joint of the patient.

[0105] In block 510, after the analysis device 302 determines the predicted performance for the selected prosthesis component in the selected implant alignment, process 500 repeats steps 506, 508, and 510 to predict the performance of the selected prosthesis component in each of the other implant alignments in the set of implant alignment options under consideration. To do so, before repeating step 510, the analysis device 302 may change the implant alignment along one or more degrees of freedom (e.g., the tibial anteroposterior tilt for repositioning the tibial prosthesis at the proximal end of the patient's tibia) to obtain another prediction of the performance for the new implant alignment. The analysis device 302 continues this process, repeating steps 506, 508, and 510 until performance predictions are generated for each implant alignment in the set of implant alignment options. The performance prediction for each implant alignment (e.g., the predicted kinematics and / or the predicted ligament elongation) is recorded or stored in process step 512.

[0106] The analysis device 302 may also repeat steps 504, 506, 508, and 510 to predict the performance for different sizes of the prosthesis component. The performance predictions for each implant alignment for each size of the prosthesis component (including the predicted kinematics and the predicted ligament elongation) are recorded or stored in process step 514. Optionally, the analysis device 302 may also repeat steps 502, 504, 506, 508, and 512 to predict the performance for all possible combinations of different types and sizes of prosthesis components of the implant system 200. The performance predictions for each possible implant alignment for each possible total knee replacement (including the predicted kinematics and / or the predicted ligament elongation) may be recorded or stored in process step 516.

[0107] After determining the predicted performance of the knee prosthesis for each desired type, size, and implant alignment option at block 406, the method 400 proceeds to block 408 (see FIG. 6 ) where the analysis device 302 identifies one or more recommended implant alignments for the selected orthopaedic knee prosthesis 100. To do so, the analysis device 302 is configured to evaluate and compare the target kinematics determined at block 404 to the predicted performance of the knee prosthesis. In an exemplary embodiment, the analysis device 302 may calculate a cost function for each implant alignment of the orthopaedic knee prosthesis 100 across a flexion range (illustratively, 0 degrees to 120 degrees of flexion) and select a recommended implant alignment for the orthopaedic knee prosthesis 100 based on the result of the cost function. For example, the analysis device 302 may determine the sum of absolute differences between the target lengths and implanted lengths of the medial collateral ligament (MCL), lateral collateral ligament (LCL), and posterior cruciate ligament (PCL) of the patient's knee joint over the range of flexion according to a cost function.

[0108]

number

[0109] In an exemplary embodiment, the analysis device 302 selects, as the recommended implant alignment of the orthopedic artificial knee joint 100, the implant alignment with the lowest calculated cost function (i.e., the implant alignment in which the length of the associated ligament is most closely aligned with the length of the corresponding target ligament). In other embodiments, the analysis device 302 may select, as the recommended implant alignment(s), all implant alignments for which the calculated cost function is less than a threshold value. In yet other embodiments, the analysis device 302 can select or determine the recommended implant alignment(s) based on a set of decision rules. The decision rules may define specific criteria (e.g., requirements for a specific ligament length at a specific flexion degree) on which the recommendation should be based. The decision rules may require the analysis device 302 to evaluate and weight implant alignment options based on other aspects of the predicted dynamics, such as, for example, the degree of internal rotation - external rotation, and aspects of the mechanical properties, such as, for example, the predicted forces and stresses on the artificial knee joint. In some embodiments, block 408 may also consider preferences specified by the surgeon, such as, for example, constraints on one or more degrees of freedom for the implant alignment, preference between implant sizes when two adjacent sizes have the same or similar performance metrics.

[0110] After the analysis device 302 selects the recommended implant alignment of the orthopedic artificial knee joint 100, method 400 proceeds to block 410, where one or more of the recommended implant alignment(s) are incorporated into the patient - specific surgical plan 800. In some embodiments, block 410 may involve preparing a new surgical plan 800 that includes the recommended implant alignment(s). In other embodiments, block 410 may involve partially modifying an existing surgical plan 800 previously developed for the patient. The surgical plan 800 may be displayed on the display 316 for the surgeon to review pre - operatively on the analysis device 302, or may be displayed on the surgical device 602 intra - operatively. The surgical plan 800 may be embodied in hard - copy form.

[0111] As shown in FIG. 8, the surgical plan 800 for each patient can include a graphic representation of the resection plane 802 on the patient's bone, the graphic representation indicating the angle of each plane and the amount of bone to be removed from each part of the patient's bone in order to position the prosthesis components in a selected implant alignment (e.g., the recommended implant alignment). The surgical plan 800 may also include a visualization 804 of the prosthesis components positioned on the patient's bone in the selected implant alignment. The surgical plan may also include the recommended size and type of the prosthesis components, as well as other information for guiding the surgical procedure.

[0112] In some embodiments, the surgical plan 800 may include a graphical representation of the expected performance of a selected artificial knee joint in some alignment options, including one or more recommended implant alignments (plural). For example, as shown in FIG. 9, the surgical plan 800 may include a heat map 810 showing the output of the cost function of the ligament length described above for various combinations of femoral internal rotation-external rotation and tibial varus-valgus inclination, along with an indication 812 showing the recommended implant alignment. As another example, FIG. 10 shows a heat map 814 showing the output of the cost function of the ligament length for various combinations of femoral varus-valgus rotation and tibial varus-valgus inclination, and an indication 812 showing the recommended implant alignment may be additionally or alternatively included in the surgical plan 800. The heat maps 810, 814 may include a scale for the output of the cost function of the ligament length shown on the right side of each of FIGS. 9-10. The heat maps 810, 814 may also include an indication 816 showing other typical alignment techniques, such as mechanical alignment or anatomical alignment, for example, to provide the surgeon with a visual comparison of the patient-specific alignment with these other alignment techniques. The analysis device 302 may, in addition or as an alternative, display other heat maps illustrating the output of the ligament length cost function for other input variables, such as the planned flexion and / or extension gaps of the patient, as well as different alignment combinations. In this way, the surgical plan 800 can provide the surgeon with visual guidance on the dynamics of the orthopedic artificial knee joint in various alignments.

[0113] The orthopedic surgeon can adjust the implant alignment as needed, including during preoperative activities or during the surgery, using devices 302, 602. For example, the device may be configured to display a user interface including surgical plan 800. The user interface may be configured to receive adjustments from the surgeon for any of the alignment parameters, including, for example, the size and type of prosthesis components, tibial slope, varus-valgus slope of the tibia or femur, cut depth, internal-external rotation, and tibial insert thickness. The device may also be configured to display a user interface 900 as shown in FIG. 11. Interface 900 includes MCL extension 904 and LCL extension 906. For example, exemplary MCL extension 904 and LCL extension 906 include a control 910 that the orthopedic surgeon can manipulate to adjust the relaxation of the corresponding ligament at a defined flexion degree. Dashed lines 924, 926 of MCL extension 904 and LCL extension 906 indicate target ligament extension, and solid lines 934, 936 indicate predicted ligament extension. When the orthopedic surgeon adjusts the alignment parameters and / or ligament extensions 904, 906, the analysis device 302 performs steps 402, 404, 406, 410 to update the predicted performance of the artificial knee joint and the patient-specific plan based on the adjustment.

[0114] Referring now to FIG. 12, an alternative module 1200 is shown that may be included in (instead of, or in addition to) the analysis device 302 in place of module 350. Module 1200 is configured to execute a method 1202 for creating a patient-specific surgical plan 800 for that patient. For example, method 1202 or a portion thereof may be embodied as a set of executable instructions stored on and executable by the analysis device 302. It should also be understood that the operation of module 1200 and method 1202 may be performed by one or more components of the analysis device 302 and / or a device communicatively coupled to the analysis device 302 (including, for example, the surgical device 602). In some embodiments of method 1202, creating a patient-specific surgical plan for a patient may be embodied as modifying a portion of an existing patient-specific surgical plan previously created for that patient.

[0115] In block 402 of method 1202, the analysis device 302 identifies various anatomical features or parameters of the patient's knee joint. Block 402 of method 1202 may include any or all of the features described above for block 402 of method 400 (FIG. 6). For example, the anatomical parameters can be derived from intraoperative bone alignment (described in more detail below), a medical image of the patient's preoperative anatomical structure, or a combination of bone alignment and medical image.

[0116] After the anatomical parameters are identified, method 1202 proceeds to block 404 where the target kinematics for each patient are determined. Block 404 of method 1202 may include any or all of the features described above for block 404 of method 400 (FIG. 6). For example, analysis device 302 may use the identified anatomical parameters (or a subset thereof) as an input to determine the target kinematics for each patient based on a patient-specific statistical shape function model. In some embodiments, the target kinematics include the respective target lengths of one or more ligaments of the patient's knee joint, as described above. In other embodiments, the target kinematics for each patient are measured directly from the patient (e.g., during surgery) by moving the patient's leg through the range of motion while the surgical device 602 monitors the kinematics of the patient's knee joint such that the analysis device 302 can determine the kinematics of the patient's knee joint based on such motion.

[0117] When implementing module 1200, analysis device 302 is also configured to perform block 1204 of method 1202. During block 1204, analysis device 302 determines one or more predicted gaps 116 for the selected knee implant 100 when implanted in the knee joint for each initial set of implant alignment options. As described above, the position and orientation of each prosthesis component (i.e., implant alignment) can vary with up to six degrees of freedom. The set of implant alignment options includes implant alignments that reflect each combination of all possible values for each degree of freedom of the prosthesis component(s), whatever the constraints applicable to the set. Thus, the initial set of implant alignment options used in block 1204 reflects an initial set of the degrees of freedom for the positioning of the femoral prosthesis 102 relative to the femur 110 and the degrees of freedom for the positioning of the tibial tray 104 relative to the tibia 112.

[0118] In one exemplary embodiment of block 1204, the analysis device 302 virtually "plants" or positions a selected prosthetic component at each implant alignment within an initial set of implant alignment options and calculates a predicted gap 116 corresponding to that implant alignment. For example, in a particular implant alignment option, the femoral prosthesis 102 is virtually fixed relative to the femur 110 and the tibial prostheses 104, 106 are virtually fixed relative to the tibia 112. The analysis device 302 can then determine the position of the femoral prosthesis 102 relative to the tibial prostheses 104, 106 (or a portion of the tibia 112 itself such as a planned tibial resection plane) at different points within the range of motion of the knee joint by applying one or more transformation matrices to the target kinematics of the knee joint. Then, for each flexion angle of the subject (e.g., 0 degrees, 90 degrees), the analysis device 302 measures the minimum distance between the medial condyle of the femoral prosthesis 102 and the tibia 112 (or one of the tibial prostheses 104, 106) to determine a predicted medial gap 116a and measures the minimum distance between the lateral condyle of the femoral prosthesis 102 and the tibia 112 (or one of the tibial prostheses 104, 106) to determine a predicted lateral gap 116b. The analysis device 302 can repeat this process to determine at least one predicted gap 116 for each implant alignment within the initial set of implant alignment options.

[0119] As described above, the gap(s) 116 may be defined in a number of manners, and any of those gap(s) 116 may be predicted at block 1204. For example, in some embodiments, rather than searching for the point on the virtually implanted femur prosthesis 102 that is closest to the tibia 112 at each flexion angle of the subject for each implant alignment option, the analysis device 302 may instead utilize a predetermined point on the sagittal surface of the femur prosthesis 102 corresponding to each flexion angle of the subject. As an example, referring to FIG. 2B, point 250 may be used to determine the predicted gap 116 at 0 degrees of flexion, while point 252 may be used to determine the predicted gap 116 at 90 degrees of flexion. When the femur prosthesis 102 is virtually implanted by the analysis device, the location of each such predetermined point relative to the patient's bone can be readily identified based on the known geometric shape of the femur prosthesis 102, enabling a more efficient determination of the predicted gap(s) 116. In some embodiments, this information can be used as input to train a mathematical model (e.g., a linear response model) to output the predicted gap(s) 116 for an initial set of implant alignment options using prosthesis size as well as patient-specific anatomical parameters and / or kinematics (from blocks 402 and / or 404). It is also contemplated that block 1204 may include predicting the gap(s) 116 for any number of positions of the knee joint over the range of motion. As an example, in some embodiments, the analysis device 302 may predict the gap(s) 116 for each implant alignment when the knee joint is in extension, mid-flexion, and late flexion.

[0120] After block 1204, method 1202 proceeds to block 1206, where analysis device 302 selects a set of implant alignment options from an initial set of implant alignment options analyzed at block 1204 (e.g., the selected set may be a subset of the initial set). In an exemplary embodiment, block 1206 includes determining which implant alignment from the initial set of implant alignment options results in a predicted gap 116 that meets one or more flexion-extension gap criteria. It is contemplated that additional and / or different criteria may be used to narrow a larger initial set of implant alignment options to a smaller selected set of implant alignment options. For example, in other embodiments, the analysis device may narrow the set of implant alignment options at block 1206 using other performance predictions and / or surgeon preferences. As an example, the surgeon may restrict one or more degrees of freedom (e.g., through preference settings) such that only implant alignments where the tibial tray is perpendicular to the mechanical axis are included in the set of implant alignment options selected at block 1206.

[0121] In an exemplary embodiment of method 1202, the analysis device 302 executes block 1206 by calculating performance metrics for each implant alignment option of an initial set of implant alignment options based on the predicted gap(s) 116 determined in block 1204. By way of example, block 1206 may include comparing the predicted gap(s) 116 determined for each implant alignment option to one or more reference values. The reference value(s) may be constant or may vary with flexion. The reference value(s) may be defined by the user to reflect the desired gap(s) 116 of the implanted knee prosthesis 100. The analysis device 302 calculates the difference between the predicted gap(s) 116 and the reference value(s) at each flexion angle of the subject, and then sums those absolute differences to arrive at a performance metric for each implant alignment option, thereby implementing block 1206. In some embodiments, the analysis device may calculate separate performance metrics for the medial and lateral sides for each implant alignment option. In some embodiments, the medial and lateral performance metrics can be summed to create a single performance metric for that implant alignment option. The foregoing performance metric calculation may be implemented as a cost function for finding the optimal implant alignment (or alignments) across a set or subset of implant alignment options. For example, for each subset of implant alignment options that share the same tibial cut surface and the same femoral prosthesis flexion-extension angle, the analysis device 302 can use the performance metric to identify and select an implant alignment option having a predicted gap(s) 116 that matches the reference value(s). In other embodiments, the analysis device 302 can apply a threshold filter to select a set of implant alignment options for which the performance metric is less than a threshold.

[0122] By the selection process of block 1206, at least one degree of freedom of the selected set of implant alignment options is constrained relative to the initial set of degrees of freedom reflected in the initial set of implant alignment options. Often, block 1206 results in a plurality of degrees of freedom that are constrained (e.g., have fewer possible values) relative to the initial set of degrees of freedom. In some cases, one or more degrees of freedom present in the initial set of implant alignment options may even be fixed (i.e., all implants within the selected set may share the same position with respect to translation and / or rotation of that type) in the selected set of implant alignment options. It will be understood that these constraints on one or more degrees of freedom of the prosthesis components result in a selected set of implant alignment options consisting of fewer possible implant alignments than the initial set of implant alignment options.

[0123] After block 1206, method 1202 proceeds to block 406 where the analysis device 302 determines the predicted performance of the knee implant for each of the selected set of implant alignment options identified in block 1206. Block 406 of method 1202 may include any or all of the features described above for block 406 of method 400 (FIG. 6), which may include any or all of the features of process 500 (FIG. 7). For example, the analysis device 302 may use the performance mathematical model 336 to determine the performance of the knee implant and may determine the predicted ligament elongation for each of the implant alignments within the selected set of implant alignment options. However, in contrast to method 400, in an exemplary embodiment of method 1202, block 406 is only performed for a smaller selected set of implant alignment options and not for the larger initial set of implant alignment options utilized to predict the flexion-extension gap in block 1204. This can reduce the computational load on the analysis device 302 because generating each performance prediction in block 406 is often more computationally intensive than generating each predicted flexion-extension gap in block 1204.

[0124] In block 406, after determining the predicted performance of the knee implant for each of the implant alignments of the selected set of implant alignment options, method 1202 proceeds to block 408 where the analysis device 302 identifies one or more recommended implant alignments for the selected orthopedic knee implant 100. Block 408 of method 1202 may include any or all of the features described above for block 408 of method 400 (FIG. 6). For example, the analysis device 302 can evaluate the target kinematics determined in block 404 of method 1202 and compare it to the performance predictions determined in block 406 of method 1202 (e.g., using a cost function).

[0125] After the analysis device 302 selects the recommended implant alignment(s) for the orthopedic artificial knee joint 100, method 1202 proceeds to block 410 where one or more of the recommended implant alignments are incorporated into the patient-specific surgical plan 800. Block 410 of method 1202 may include any or all of the features described above for block 410 of method 400 (FIG. 6). For example, block 410 may involve either preparing a new surgical plan 800 that includes the recommended implant alignment(s) or modifying a portion of an existing surgical plan 800 previously developed for the patient. The surgical plan 800 may be presented to the surgeon electronically and / or as a hard copy, either preoperatively, intraoperatively, or both.

[0126] As described above, the analysis device 302 is configured to prepare a patient-specific surgical plan based on medical images, anatomically identified features during surgery, or a combination of such images and features. Referring now to FIG. 13, method 1300 shows one embodiment for formulating a patient-specific surgical plan based on medical images and implementing that plan. Method 1300 includes block 1302 of using an imaging device 304 to obtain the necessary medical images of the patient's anatomical structure. For example, the medical images in this embodiment are taken via a CT scan, although in other embodiments they may be embodied as other types of two-dimensional medical images and / or three-dimensional medical images.

[0127] Next, method 1300 implements either module 350 (see FIG. 6) or module 1200 (see FIG. 12). In block 402 of the implemented module 350 / 1200, the analysis device 302 can identify relevant anatomical parameters based on the manually annotated medical images received from the orthopedic surgeon. In an exemplary embodiment, the anatomical parameters include the center and radius of the medial femoral condyle, the center and radius of the lateral femoral condyle, the posterior slope(s) of the tibial plateau (medial and lateral), the varus-valgus angle of the tibial articular surface, the rotational angle with respect to the tibial tuberosity, and the rotational angle with respect to the fibular head. In other embodiments, the anatomical parameters may additionally or alternatively include any anatomical landmarks of the tibial center, femoral center, femoral condyles (medial and lateral), and malleoli, for example, the attachment site of the posterior cruciate ligament (PCL), and the most distal and posterior points(s) of the medial and / or lateral femoral condyle(s). Various axes such as the tibial sagittal axis, the Whiteside line, and the mechanical axes of the femur and tibia can also be determined from the medical images.

[0128] In some embodiments, the analysis device 302 can be configured to automatically and / or autonomously identify anatomical parameters regarding the anatomical structure of a patient's bone in a medical image. This autonomous identification may be in addition to or instead of manual annotations provided by an orthopedic surgeon. For example, in some embodiments, the analysis device 302 can utilize a machine learning algorithm (e.g., the image processing module 332) to identify anatomical landmarks and other anatomical parameters in the medical image(s). In such embodiments, the machine learning algorithm can go through a training phase, in which a training set of manually annotated medical images of sample patients is supplied to the machine learning algorithm. In this way, the machine learning algorithm is trained to identify the corresponding anatomical parameters in new medical images, such as the medical image of the current patient. After the anatomical parameters are determined, the analysis device 302 can perform other activities of the implemented module 350 / 1200, which are described in more detail above with reference to FIGS. 6, 7, and 12.

[0129] After formulating a patient-specific surgical plan using module 350 or module 1200, method 1300 proceeds to block 1304, where the surgeon executes the patient-specific surgical plan 800 to implant the prosthesis components into the patient. To do so, the surgeon may use manual instruments to identify the location of the resection plane 802 outlined in plan 800, position the cutting guide and other surgical instruments in the desired locations, and prepare the patient's bone to receive the prosthesis components. It should be understood that the surgeon may use customized patient-specific instruments configured with features to align the instruments with the planned resection plane. The surgeon may also use an augmented reality device to display the surgical plan and guide the placement of the manual instruments.

[0130] Referring now to FIG. 14, in another embodiment, a surgeon can utilize a robotic surgical device 602 to develop and execute a patient-specific plan according to method 1400. Method 1400 may begin with the surgeon performing an initial surgical procedure on the patient's knee joint. For example, an orthopedic surgeon may open the patient's knee joint envelope to gain access to the anatomical structure of the patient's bone. Further, in this embodiment, the orthopedic surgeon can attach optical tracking arrays to each of the patient's femur and tibia of the corresponding knee joint to facilitate tracking of the anatomical structure of the patient's bone in three-dimensional space by the surgical device 602.

[0131] In block 1402, the surgeon performs a bone alignment procedure. To do so, the orthopedic surgeon can utilize an optical stylus 626 to identify specific anatomical parameters recorded by the surgical device 602. In this embodiment, the anatomical parameters illustratively include the center and radius of the medial femoral condyle, the center and radius of the lateral femoral condyle, the posterior slope(s) of the tibial plateau (medial and lateral), the varus-valgus angle of the tibial articular surface, the rotational angle relative to the tibial tuberosity, and the rotational angle relative to the fibular head. In other embodiments, the anatomical parameters may additionally or alternatively include any anatomical landmarks of the tibial center, femoral center, femoral condyles (medial and lateral), and malleolar processes, for example, the attachment site of the posterior cruciate ligament (PCL), and the most distal and posterior point(s) of the medial and / or lateral femoral condyle(s). Various axes such as the tibial sagittal axis, the Whiteside line, and the mechanical axes of the femur and tibia can also be determined from the bone alignment procedure of block 1402.

[0132] At block 1404, the surgeon may move the patient's knee joint through the range of flexion and / or perform other movements of the patient's knee joint. The surgical device 602 tracks the anatomical structure of the patient's bones (i.e., the patient's femur and tibia) based on the attached optical tracking array and records the movement of the anatomical structure of the patient's bones. In this exemplary embodiment, the surgeon may use the bone extension tool 626 to adjust the distance or gap between the patient's femur and tibia over the range of flexion. The tool 626 may be configured to generate distance or force data that can be recorded for use in module 350.

[0133] Method 1400 proceeds to either module 350 or module 1200, where an analysis device 302 (which may be incorporated into the surgical device 602 or separate therefrom) formulates a surgical plan for each patient based on the bone alignment information and tracking data recorded at blocks 1302, 1304. For example, when using either module 350 or module 1200, the analysis device 302 may determine the target kinematics of the patient's knee joint based on the tracked movements and anatomical landmarks (block 404). As described above, the target kinematics may include the varus-valgus angle, internal-external rotation, medial-lateral translation, anterior-posterior translation, and superior-inferior translation through the range of flexion. Using these target kinematics, the analysis device 302 formulates a surgical plan for each patient that incorporates the recommended implant alignment, as described in more detail above with reference to FIGS. 6, 7, and 12.

[0134] At block 1406, the orthopedic surgeon may perform an orthopedic surgery to implant the selected orthopedic artificial knee joint 100 based on the recommended alignment of the artificial knee joint. During this, the orthopedic surgeon may further adjust the implant alignment and / or the ligament elongation, and accordingly, the surgical device 602 updates the displayed alignment and ligament elongation based on such adjustments.

[0135] Referring now to FIG. 15, in some embodiments, computer system 300 may be configured to execute a method 1500 for determining the positioning of the artificial knee joint 100 based on medical images and information generated during surgery. As shown in FIG. 15, method 1500 includes block 1302, module 350 or 1200, block 1402, and block 1404, each of which functions as described above with reference to FIGS. 6, 7, 12, 13, and 14. Method 1500 also includes block 1502, where the analysis device 302 updates the patient-specific surgical plan developed in module 350 or module 1200 (depending on the embodiment) based on data obtained during surgery from blocks 1402 and 1404. The surgical device 602 can display both the pre-operative recommended implant alignment identified using the medical images and the intra-operative recommended implant alignment generated in block 1502.

[0136] In block 1502, the analysis device 302 may reconcile the pre-operative recommended implant alignment and the intra-operative recommended implant alignment using any suitable algorithm. For example, in some embodiments, the analysis device 302 may select one of the implant alignments recommended based on certain criteria, average the two recommended implant alignments, and / or apply another mathematical operation to the recommended implant alignments to determine the final recommended implant alignment for the orthopedic artificial knee joint 100.

[0137] Subsequently, the surgical device 602 is configured to visualize and display on the display 616 the final recommended alignment of the artificial knee joint and the associated ligament elongation(s). Additionally, in some embodiments, the orthopedic surgeon may adjust the implant alignment and / or ligament elongation, and the surgical device 602 updates the displayed alignment and ligament elongation based on the surgeon's adjustments.

[0138] In block 1504, the orthopedic surgeon can perform orthopedic surgery to implant the selected orthopedic artificial knee joint 100 based on the determined recommended artificial knee joint alignment parameters. While doing so, the orthopedic surgeon may further adjust the implant alignment and / or ligament elongation, and accordingly, the surgical device 602 updates the displayed alignment and ligament elongation based on such adjustments.

[0139] It should be understood that each of the methods described with reference to FIGS. 13 - 15 is simplified in its level of detail, and each of methods 1300, 1400, and 1500 may include additional or alternative process steps in other embodiments. For example, each of methods 1300, 1400, and 1500 may include additional initialization procedures, user interface interactions, coordinate system construction steps, and / or other process steps and / or sub - flows.

[0140] As described in detail in the drawings and the above description, such drawings and descriptions should be regarded as exemplary in nature and not limiting, and merely illustrate and describe exemplary embodiments. It is desirable to understand that all changes and modifications within the scope of the spirit of the present disclosure are protected.

[0141] The present disclosure has multiple advantages based on various features of the methods, devices, and systems described herein. Alternative embodiments of the methods, devices, and systems of the present disclosure do not include all of the described elements, but still benefit from at least some of the advantages of such elements. Those skilled in the art can readily implement alone the methods, devices, and systems that incorporate one or more of the elements of the present invention and are included within the spirit and scope of the present disclosure defined in the appended "claims".

[0142] 〔Embodiments〕 (1) An orthopedic surgical planning method (1202) for a patient's knee joint, comprising: identifying, by a computer system (302), anatomical parameters of the knee joint (402); determining, by the computer system (302), a target kinematics of the knee joint based on the pre-implant anatomical structure of the patient (404); determining, by the computer system (302), one or more predicted gaps (116) between a femoral prosthesis (102) and a tibial prosthesis (104) when implanted in the knee joint in each of a first set of implant alignment options, wherein the first set of implant alignment options reflects a first set of degrees of freedom for positioning the femoral prosthesis (102) and the tibial prosthesis (104) relative to the bones (110, 112) of the knee joint (1204); determining, by the computer system (302), a second set of implant alignment options from among the first set of implant alignment options, based at least in part on the one or more predicted gaps (116), wherein the second set of implant alignment options reflects a second set of degrees of freedom for positioning the femoral prosthesis (102) and the tibial prosthesis (104) relative to the bones (110, 112) of the knee joint, and at least one degree of freedom of the second set of degrees of freedom is constrained relative to the first set of degrees of freedom (1206); The computer system (302) determines (406) the predicted kinematics for each implant alignment of the second set of implant alignment options, wherein determining the predicted kinematics (406) includes the computer system (302) inputting the identified anatomical parameters into a mathematical model (336) representing the kinematics of the femur prosthesis (102) and the tibia prosthesis (104) when implanted, and operating the mathematical model (336) to determine the predicted kinematics of the femur prosthesis (102) and the tibia prosthesis (104) for each implant alignment of the second set of implant alignment options (510). Based on the target kinematics and the predicted kinematics, the computer system (302) identifies (408) at least one recommended implant alignment for the femur prosthesis (102) and the tibia prosthesis (104). The computer system (302) incorporates (410) the at least one recommended implant alignment into a patient-specific surgical plan (800). A method (1202) including the above. (2) The method according to Embodiment 1, wherein a plurality of degrees of freedom of the second set are constrained with respect to the degrees of freedom of the first set. (3) The method according to Embodiment 1 or 2, wherein at least one degree of freedom of the first set is fixed with respect to the implant alignment options of the second set. (4) The method according to any one of Embodiments 1 to 3, wherein the computer system (302) determines (1204) the one or more predicted gaps (116) using the target kinematics and the known geometric shapes of the femur prosthesis (102) and the tibia prosthesis (104). (5) Determining the one or more predicted gaps (116) (1204) includes, for each implant alignment of the first set of implant alignment options, determining the predicted gap (116) between the femur prosthesis (102) and the tibia prosthesis (104) at each of a plurality of flexion-extension angles, the method according to any of embodiments 1 to 4.

[0143] (6) Determining the one or more predicted gaps (116) (1204) includes, for each implant alignment of the first set of implant alignment options, determining a predicted medial gap (116a) between the femur prosthesis (102) and the tibia prosthesis (104) and a predicted lateral gap (116b) between the femur prosthesis (102) and the tibia prosthesis (104), the method according to any of embodiments 1 to 5. (7) Determining the second set of implant alignment options from among the first set of implant alignment options (1206) includes selecting an implant alignment in which the one or more predicted gaps (116) are within a target range, the method according to any of embodiments 1 to 6. (8) Determining the target kinematics of the knee joint (404) includes determining a target ligament elongation of at least one ligament of the knee joint over a flexion range, operating the mathematical model to determine the predicted kinematics of the femur prosthesis (102) and the tibia prosthesis (104) includes determining predicted ligament elongations (934, 936) of the at least one ligament over the flexion range, Identifying the at least one recommended implant alignment (408) includes, for each implant alignment of the second set of implant alignment options, calculating a difference between the target ligament elongation and the predicted ligament elongation (934, 936) over the range of flexion, and recommending the implant alignment corresponding to the minimum difference between the target ligament elongation and the predicted ligament elongation (934, 936) over the range of flexion, the method according to any of embodiments 1-7. (9) Calculating the difference between the target ligament elongation and the predicted ligament elongation (934, 936) over the range of flexion includes calculating the sum of the absolute differences between the target ligament elongation and the predicted ligament elongation (934, 936) at several postures over the range of flexion, Recommending the implant alignment corresponding to the minimum difference includes recommending the implant alignment corresponding to the minimum sum of the absolute differences, the method according to embodiment 8. (10) The at least one ligament is at least one of the medial collateral ligament, lateral collateral ligament, posterior cruciate ligament, anterolateral ligament, and posterior capsule of the knee joint of the patient, the method according to embodiment 8 or 9.

[0144] (11) The at least one ligament includes at least the medial collateral ligament, the lateral collateral ligament, and the posterior cruciate ligament, the method according to embodiment 10. (12) Identifying the anatomical parameters of the knee joint of the patient (402) includes identifying the anatomical parameters of the knee joint based on a set of medical images (330) of the preoperative anatomical structure of the patient, the method according to any of embodiments 1-11. (13) Further including obtaining the set of medical images (1302) through at least one of a CT scan, X-ray imaging, and ultrasonic imaging of the preoperative anatomical structure of the patient, the method according to embodiment 12. (14) Further comprising performing an intraoperative bone alignment procedure (1402) on the knee joint of the patient using the computer system (302) to identify some landmarks on the anatomical structure of the patient before implantation, wherein the anatomical parameters include the some landmarks, the method according to any one of Embodiments 1 to 13. (15) Further comprising tracking the movement of the knee joint (1404) over a range of flexion using the computer system (302) based on the identified landmarks, wherein determining the target kinematics (404) of the knee joint includes determining the target kinematics based on the movement of the knee joint, the method according to Embodiment 14.

[0145] (16) Inserting an articular extension device (628) into the knee joint; Operating the articular extension device (628) to adjust the distance (116) between the femur (110) and the tibia (112) of the patient over the range of flexion; Further comprising, the method according to Embodiment 15. (17) The mathematical model (336) is a linear response model configured to receive anatomical parameters, prosthesis size, prosthesis type, and implant alignment as inputs and generate the predicted kinematics of the femur prosthesis (102) and the tibia prosthesis (104) as outputs, the method according to any one of Embodiments 1 to 16. (18) The linear response model is further configured to generate the predicted mechanical properties of the femur prosthesis (102) and the tibia prosthesis (104) as outputs, wherein the mechanical properties include at least one of the predicted contact force between the femur prosthesis (102) and the tibia prosthesis (104), and the predicted contact force between the femur prosthesis (102) and the tibia prosthesis (104) and the bones (110, 112) of the knee joint, the method according to Embodiment 17. (19) To prepare the bone of the patient to receive the femoral prosthesis and the neck prosthesis with the recommended implant alignment, further including adding to the surgical plan for each patient several planned resections of the femur of the patient and at least one planned resection of the neck bone of the patient, the method according to any one of Embodiments 1-16. (20) An orthopedic surgical planning system (300) including a computer system (302) configured to automatically execute the method (1202) according to any one of Embodiments 1-19.

[0146] (21) A robotic system (602) including a cutting tool configured to resect one or more of the bones (110, 112) of the patient according to the surgical plan (800) for each patient, or A bone alignment tool (626) for identifying some landmarks on the pre-implant anatomical structure of the patient using the computer system (302), wherein the anatomical parameters include the some landmarks, the bone alignment tool (626), The system (300) according to Embodiment 20, further including at least one of.

Claims

**Claim 1** An orthopedic surgical planning method (1202) for a patient's knee joint, comprising: identifying (402), by a computer system (302), anatomical parameters of the knee joint; determining (404), by the computer system (302), a target kinematics of the knee joint based on the pre-implant anatomical structure of the patient; determining (1204), by the computer system (302), one or more predicted gaps (116) between a femur prosthesis (102) and a tibia prosthesis (104) when implanted in the knee joint in each of a first set of implant alignment options, wherein the first set of implant alignment options reflects a first set of degrees of freedom for positioning the femur prosthesis (102) and the tibia prosthesis (104) relative to the bones (110, 112) of the knee joint; determining (1206), by the computer system (302), a second set of implant alignment options from among the first set of implant alignment options, based at least in part on the one or more predicted gaps (116), wherein the second set of implant alignment options reflects a second set of degrees of freedom for positioning the femur prosthesis (102) and the tibia prosthesis (104) relative to the bones (110, 112) of the knee joint, and at least one degree of freedom of the second set of degrees of freedom is constrained relative to the first set of degrees of freedom. Determining, by the computer system (302), the predicted kinematics for each implant alignment of the second set of implant alignments (406), wherein determining the predicted kinematics (406) comprises the computer system (302) inputting the identified anatomical parameters into a mathematical model (336) representing the kinematics of the femur prosthesis (102) and the tibia prosthesis (104) when implanted, and operating the mathematical model (336) to determine the predicted kinematics of the femur prosthesis (102) and the tibia prosthesis (104) for each implant alignment of the second set of implant alignments (510), and (406); Identifying, by the computer system (302), at least one recommended implant alignment of the femur prosthesis (102) and the tibia prosthesis (104) based on the target kinematics and the predicted kinematics (408); Incorporating, by the computer system (302), the at least one recommended implant alignment into a patient-specific surgical plan (800) (410); A method (1202) comprising the steps of. Claim 2 The method according to claim 1, wherein a plurality of degrees of freedom of the second set are constrained with respect to the degrees of freedom of the first set. Claim 3 The method according to claim 1 or 2, wherein at least one degree of freedom of the first set is fixed with respect to the implant alignment options of the second set. Claim 4 The method according to claim 1, wherein the computer system (302) determines the one or more predicted gaps (116) using the target kinematics and the known geometric shapes of the femur prosthesis (102) and the tibia prosthesis (104) (1204). Claim 5 Determining the one or more predicted gaps (116) (1204) includes, for each implant alignment of the first set of implant alignment options, determining the predicted gap (116) between the femur prosthesis (102) and the tibia prosthesis (104) at each of a plurality of flexion-extension angles. The method according to claim 1.

6. Determining the one or more predicted gaps (116) (1204) includes, for each implant alignment of the first set of implant alignment options, determining a predicted medial gap (116a) between the femur prosthesis (102) and the tibia prosthesis (104) and a predicted lateral gap (116b) between the femur prosthesis (102) and the tibia prosthesis (104). The method according to claim 1.

7. Determining the second set of implant alignment options (1206) from among the first set of implant alignment options includes selecting an implant alignment for which the one or more predicted gaps (116) are within a target range. The method according to claim 1.

8. Determining the target kinematics of the knee joint (404) includes determining a target ligament elongation of at least one ligament of the knee joint over a flexion range. Operating the mathematical model (510) to determine the predicted kinematics of the femur prosthesis (102) and the tibia prosthesis (104) includes determining a predicted ligament elongation (934, 936) of the at least one ligament over the flexion range. Identifying the at least one recommended implant alignment (408) includes, for each implant alignment of the second set of implant alignment options, calculating a difference between the target ligament elongation and the predicted ligament elongation (934, 936) over the flexion range, and recommending the implant alignment corresponding to the minimum difference between the target ligament elongation and the predicted ligament elongation (934, 936) over the flexion range. The method according to claim 1.

9. Calculating the difference between the target ligament elongation and the predicted ligament elongations (934, 936) over the flexion range includes calculating the sum of the absolute differences between the target ligament elongation and the predicted ligament elongations (934, 936) at several postures over the flexion range, Recommending the implant alignment corresponding to the minimum difference includes recommending the implant alignment corresponding to the minimum sum of the absolute differences, the method of claim 8. [

10. ] The method of claim 8 or 9, wherein the at least one ligament is at least one of the medial collateral ligament, lateral collateral ligament, posterior cruciate ligament, anterolateral ligament, and posterior capsule of the patient's knee joint.

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