System and method for determining personalized placement and selection of shoulder implants

Abstract

Disclosed herein is a system and method for performing pre-operative planning of total joint arthroplasty. The planning tool builds a model and analyzes and visualizes movement of the joint for various selections of implant models and placement of the components on the implants on the patient's anatomy. The tool also analyzes and visualizes motions of the joint during common activities of daily living and analyzes and visualizes changes in muscles.

Description

SYSTEM AND METHOD FOR DETERMINING PERSONALIZED PLACEMENT AND SELECTION OF SHOULDER IMPLANTSRelated Applications

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 545,667, filed October 25, 2023, entitled “System and Method for Determining Personalized Placement and Selection of Shoulder Implants”, the contents of which are incorporated herein in their entirety.Field of the Disclosure

[0002] The present disclosure relates generally to orthopedic procedures, for example, total joint replacement procedures, and, in particular, to total shoulder arthroplasty (TSA) procedures.Background

[0003] Orthopedic procedures to replace joints, for example, shoulders, are well known and commonplace in today’s society. Total shoulder replacement, also known as total shoulder arthroplasty (TSA), is the removal of portions of the shoulder joint, which are replaced with artificial implants to reduce pain and restore range of rotation and mobility. In an anatomic TSA procedure, which may be used, for example, when osteoarthritis has compromised portions of the shoulder joint, the ball portion of the shoulder joint is attached to the top of the humerus and the cup portion of the shoulder joint is attached to the glenoid. In a reverse TSA procedure, which may be used, for example, whenthe rotator cuff has been compromised, the ball portion of the shoulder joint is placed on the glenoid while the cup portion of the shoulder joint is attached to the top of the humerus.

[0004] Current state-of-the-art planning for TSA consists of using a computed tomography (CT) scan from the patient to correctly size and position implant components on the patient’s specific anatomy. One of the objectives of TSA is to restore normal shoulder function, which includes both mobility and stability. Pre-operative prediction of the implanted joint range-of-motion (RoM) can facilitate optimization of post-operative mobility. Collision between the humeral liner and the scapula due to limited RoM in adduction has been identified as a cause of scapular notching, which has been reported to occur in more than half of reverse TSA patients and has been related to glenoid loosening and implant failure. In addition, implant-on-bone and bone- on-bone impingement during daily activities can increase the risk of implant instability and dislocation.

[0005] In addition, post-operative mobility and stability of the joint are significantly impacted by muscle function. Although this is true for every joint replacement, this is especially relevant to TSA as joint stability is primarily provided by muscle compressive forces and implant design (e.g., anatomic vs reverse) has a significant impact on muscle moment arms and muscle force generation capacity. Therefore, pre-operative knowledge of patient-specific post-operative muscle function can further improve the surgeon’s ability to optimally select implant design, size, and placement.

[0006] Muscle forces are the main determinant of contact mechanics. Large rotational moments at the implant-bone interface due to the joint contact force line of action can lead to glenoid component loosening (e.g., “rocking-horse” failure mechanism for anatomic TSA). Therefore, prediction of contact mechanics during activities of daily living can provide valuable insight into the risk of failure of the implant.

[0007] A number of planning applications currently available on the market provide the ability to reconstruct the pre-operative bony-anatomy of the shoulder (humerus and scapula) and calculate shoulder impingement-free RoM in its primary degrees of freedom (flexion / extension, abduction / adduction, intemal / external rotation) by rotating the implanted humerus about the three axes of rotation. However, patient posture and the static and dynamic position of the shoulder joint with respect to the torso are not included in this calculation in any currently available systems.

[0008] Currently, implant selection and placement are planned pre-operatively with considerations that are solely anatomic and limited to two bones. It has been shown that posture differs between people and, in particular, subjects with a more kyphotic posture present larger scapula protraction and internal rotation in a neutral position. In turn, scapula posture affects the position of the humerus with respect to the glenoid in the neutral position. Patient posture can be type classified pre-operatively by measuring the scapula internal rotation angle with respect to the torso, as illustrated in FIG. 1, which shows three different native or acquired posture types (A-C), with progressive scapularprotraction, tilt, and internal rotation, as well as drooping. CT-based scapula reconstruction can be used to assign patients to Type A, B, or C. As can be seen in FIG. 1, the patient’s scapular protraction, tilt, and internal rotation progress with each type.

[0009] Accounting for patient posture while planning glenoid and humerus component position provides the opportunity to maximize patient-specific post-operative ROM, as illustrated in FIG. 2, which shows that humeral stem orientation can be selected to achieve a perfect opposition of the glenosphere and liner in a neutral position and maximize ROM in internal / external rotation. Because humeral stem orientation is generally selected to replicate the native humerus anatomy, these considerations are typically ignored.

[0010] In addition, post-operative shoulder girdle kinematics has been shown to differ substantially from healthy kinematics. Reverse TSA patients present larger scapulothoracic motion than healthy subjects to compensate for the limited mobility of the glenohumeral joint. However, significant differences have also been demonstrated between reverse TSA patients, indicating that different patients will try to achieve the same motion in different ways, depending on their ability to generate muscle force, which may depend on the muscle state itself (reverse TSA is often used for patients that present substantial rotator cuff tears), and on the biomechanical changes inherent in a reverse TSA design (medialized center of rotation, arm lengthening). Currently, only anecdotal and generic knowledge about post-operative mobility and muscle state is used when planning TSA.Summary of the Disclosed Examples

[0011] Disclosed herein is a system and method using imaging, measurements, and 3D modeling tools to improve personalization of TSA planning. In various examples, the following methods can be used individually or in any combination to obtain a surgical plan that is personalized for a particular patient.

[0012] In a first method, CT-based shoulder reconstruction is used to classify patients based on their posture. The stem placement is then planned based on patient posture type to maximize ROM of the implant in internal / extemal rotation.

[0013] In a second method, scapulothoracic and glenohumeral positions in a set of representative poses (e.g., arm elevation, reach opposite shoulder, hand to back pocket) for either the ipsilateral or contralateral joint, are collected preoperatively to assess patient-specific mobility. Motion can be measured with simple x-rays in static positions, marker-based motion capture, inertial measurement units, EOS imaging, bi-plane fluoroscopy, or other kinematic measuring technologies. A set of healthy kinematics for shoulder-relevant activities of daily living (ADLs) is then conditioned to be representative of the patient motion. The personalized risk of impingement is assessed, and implant placement and selection is optimized to minimize risk of impingement for desired motions selected by either the surgeon or patient.

[0014] In a third method, CT or MR imaging is used to measure shoulder muscle volume and fatty infiltrations, which have been shown to accelerate rotator cuff tears and are correlated to post-operative function and recovery. MRI orstatistical methods are used to locate muscle attachments. Pre-operative force data generated by the patient’s shoulder muscles in static isometric positions may optionally be collected. Personalized 3D models of the shoulder including bone and muscle anatomy are generated and physician diagnosis, fatty infiltration measures, and force data are used to inform the force-generating capacity of single muscles and simulate the ability to perform ADLs, targeting either generic healthy motion or personalized motion. Lastly, pre-operative and post-operative simulated stress on the acromion are compared to evaluate potentially increased risk of acromial stress fracture and optimize implant selection and placement accordingly.

[0015] In a first example, a method of optimizing implant placement during total shoulder arthroplasty comprises obtaining imaging of the shoulder of a patient, reconstructing the bony anatomy of the scapula and humerus of the patient, based on the imaging; measuring an internal rotation of the scapula, assessing the posture of the patient, virtually resect the proximal humerus, bring the humerus to a neutral rotation and plan placement of the components of the implant based on the posture to maximize implant range-of-motion in internal and external rotation.

[0016] In the first example, or any other example disclosed herein, the method further comprises wherein the implant component is the glenoid component; and wherein the placement of the glenoid component is a rotation of the glenoid component about the longitudinal axis.

[0017] In the first example, or any other example disclosed herein, the method further comprises wherein the posture is assessed as a particular type based on internal rotation of the scapula.

[0018] In the first example, or any other example disclosed herein, the method further comprises wherein the internal rotation of the scapula is measured as an angle between a perpendicular line to the sagittal vertebral axis through the glenoid and a line from the glenoid to the root of the scapular spine in a transverse plane.

[0019] In the first example, or any other example disclosed herein, the method further comprises wherein the humerus is brought to a neutral rotation by rotating the humerus about a longitudinal axis to align the epicondylar axis of the humerus to the coronal plane of the bone.

[0020] In the first example, or any other example disclosed herein, the method further comprises wherein the implant range-of-motion is maximized when there is an equivalent range-of-motion in internal and external rotation.

[0021] In a second example, a method to plan implant selection and placement during total shoulder arthroplasty comprises obtaining imaging of the shoulder of a patient, preoperatively collecting scapulothoracic and glenohumeral positions in a set of representative poses from the imaging, conditioning a set of personalized kinematics for shoulder-related ADLs, selecting a set of ADLs; and optimizing implant selection and placement for the selected ADLs.

[0022] In the second example, or any other example disclosed herein, the method further comprises wherein the imaging is obtained for either ipsilateral or contralateral poses.

[0023] In the second example, or any other example disclosed herein, the method further comprises using 2D-to-3D registration to register 3D bone geometries from the imaging to 2D radiographic silhouettes.

[0024] In the second example, or any other example disclosed herein, the method further comprises wherein conditioning a set of kinematics comprises modifying healthy subject kinematics for the selected ADLs to personalize to the patient the trade-off between scapulothoracic elevation and glenohumeral elevation.

[0025] In the second example, or any other example disclosed herein, the method further comprising wherein the ADLs are selected from a list of ADLs based on preferences of the surgeon and patient.

[0026] In the second example, or any other example disclosed herein, the method further comprising wherein optimizing the implant selection and placement comprises placing components of the implant such as to minimize a risk of implant-to-bone or bone-to-bone impingement.

[0027] In the second example, or any other example disclosed herein, the method further comprises wherein minimizing the risk of impingement comprises calculating a distance to impingement or a percent of an activity cycle with impingement and applying an optimization algorithm to minimize the distanceto impingement or the percent of an activity cycle with impingement over the set of selected ADLs.

[0028] In a third example, a method of planning implant selection and placement for total shoulder arthroplasty comprises obtaining imaging of the shoulder of a patient, measuring muscle volume and fatty infiltration of the shoulder, locating muscle attachment points on the bones of the shoulder, generating 3D mathematical models of the shoulder that include bone and muscle anatomy, estimating, using the models, muscle forces required to perform selected ADLs, estimating stress on the acromion during the performance of the selected ADLs and personalizing implant selection and placement based on the estimated stress and the risk of an acromion stress fracture.

[0029] In the third example, or any other example disclosed herein, the method further comprises collecting pre-operative force data generated by shoulder muscles of the patient.

[0030] In the third example, or any other example disclosed herein, the method further comprising wherein the force data is used to reverse-engineer a personalized muscle mathematical model.

[0031] In the third example, or any other example disclosed herein, the method further comprises wherein the muscle volume and fatty infiltration are reconstructed from the imaging using segmentation algorithms.

[0032] In the third example, or any other example disclosed herein, the method further comprises wherein locating the muscle attachment points furthercomprises morphing a 3D bone geometry of an annotated template to identify muscle attachments points on the shoulder of the patient.

[0033] In the third example, or any other example disclosed herein, the method further comprises wherein the muscle forces required to perform selected ADLs are calculated based on either generic healthy motion or on motion personalized to the patient.

[0034] In the third example, or any other example disclosed herein, the method further comprises wherein the estimated muscle forces include a deltoid force, further comprising using finite element models of the scapula to estimate stress in the acromion during the selected ADLs, based on the deltoid force.

[0035] Various examples of the present disclosure provide numerous advantages. For example, pre-operative planning performance is improved via enhanced personalization of post-operative states, for example, the ability to predict post-operative mobility and stability for a particular patient. Additionally, the disclosed system and method provides more personalized planning which can lead to clinical benefits such as improved patient function and reduced risk of post-operative complications, such as dislocation, failure and acromion fracture, thereby reducing the risk of requiring revision surgery. The inclusion of posture-related considerations may change the humeral stem planning philosophy. The inclusion of muscle-related considerations allows a planning that accounts for patient-specific factors that are often ignored by the physician. The disclosed system and method further enhances the ability of the surgeon to interpret several patient-specific biomechanical quantities.

[0036] Further features and advantages of at least some of the examples of the systems and methods disclosed herein, as well as the structure and operation of various examples of the present disclosure, are described in detail below with reference to the accompanying drawings.Brief Description of the Drawings

[0037] By way of example, specific examples of the disclosed system and method will now be described, with reference to the accompanying drawings, in which:

[0038] FIG. 1 is an illustration showing native or acquired posture types.

[0039] FIG. 2 is an illustration showing how humeral stem orientation can be selected to achieve desired surgical outcomes.

[0040] FIG. 3 is an illustrated flowchart showing a method to plan humerus placement based on patient posture.

[0041] FIG. 4 is an illustrated flowchart showing a method to plan implant selection and select ADLs based on an assessment of the kinematics and ROM of a patient.

[0042] FIG. 5 is an illustrated flowchart showing a method to plan implant selection and placement based on personalized mathematical models and to predict the risk of stress failures.

[0043] FIG. 6 depicts an operating theatre including an illustrative computer-assisted surgical system with which various examples of the disclosed system and method could be used.Definitions

[0044] For the purposes of this disclosure, the term “implant’ is used to refer to a prosthetic device or structure manufactured to replace or enhance a biological structure.

[0045] Although much of this disclosure refers to surgeons or other medical professionals by specific job title or role, nothing in this disclosure is intended to be limited to a specific job title or function. As such, the term “user ’’ as used herein should be interpreted to include any doctor, nurse, medical professional, or technician, or any other user of the system or practitioner of the method. Any of these terms or job titles can be used interchangeably with the user of the systems disclosed herein unless otherwise explicitly stated.

[0046] As used herein, the terms “ADL” or ADLs describes one or more activities of daily living, and may include one or more functional ranges, examples of which are provided herein.Detailed Description

[0047] The system and method disclosed herein provides a tool for pre-operative planning of a joint replacement surgery which will assist the user in selecting the proper implant and planning and executing the surgery such as to minimize both intra-operative and post-operative negative outcomes for the patient. Note that, although the disclosed system and method are explained in terms of a total shoulder replacement procedure, all or portions of the disclosed system and method may be modified such as to be applicable to any j oint replacement procedure.

[0048] The system and method disclosed herein consists of a combination of several features, which, in various examples, may be used alone or in any combination to provide the desired results. The examples are not meant to be limited in any specific combination.

[0049] A first feature comprises a personalized posture assessment of the patient to inform the placement of the glenoid component of the implant. A second feature comprises a personalized kinematics and ROM assessment to further optimize the selection and placement of the components of the implant. The third feature comprises a personalized muscle assessment to further inform the selection and placement of the components of the implant, and to predict a risk of acromion stress fractures.

[0050] Personalized Posture Assessment - FIG. 3 is a flow chart showing the various steps to perform a personalized assessment of the patient’s posture and to use the assessment, in preferred examples, to inform the placement of the glenoid component of the implant in the humerus. The goal of the method is to plan the placement to maximize the internal / external rotation ratio, although, in other examples, the posture assessment could be further used to inform or optimize implant performance metrics other than internal / external rotation. The method will now be described with reference to FIG. 3.

[0051] At step 302 of the process, imaging of the shoulder of the patient to be operated on is collected. The imaging should include the distal extremity of the humerus to allow measurement of the orientation of the epicondyle axis to derive the neutral position of the humerus. Alternatively, the humerus neutralposition can be assumed to be equivalent to the humerus position in the scan. In addition, the orientation of the torso in the imaging needs to be known or assumed. Therefore, the imaging should include the T10 vertebra and the sternum to measure the sagittal vertebral axis (SVA). Alternatively, the sagittal plane of the scan can be assumed to be equivalent to the sagittal plane of the body.

[0052] In preferred examples, a CT scan of the shoulder is used. In alternate examples, the classification of the patient’s posture may be carried out without the use of a CT scan. For example, other methods like x-rays or EOS imaging can be used to measure patient posture in a standing position, which is more relevant to implant positioning because more functional than the supine position of a typical CT scan. Standing CT scans might be used too. Posture can also be determined by combining CT with a standing sagittal x-ray. If x- rays are used, 2D-to-3D registration methods can be used to register CT-based bone geometries to the 2D radiographic silhouettes.

[0053] In yet other alternate examples, different measurements could be used to classify the patient’s posture, such as thoracic kyphosis or scapula protraction. Further examples might use different classification methods, such as refining the three-level gradation defined here or including dynamic measurements, such as scapula orientation in a functional position (e.g., 90° of arm elevation).

[0054] At step 304, the anatomy of the scapula and humerus bones are reconstructed (i.e., segmented). The segmentation may be accomplished with image segmentation techniques, for example, automatic segmentation with AI / MLalgorithms or manual segmentation (i.e., bone painting on image slices), or a combination of the two.

[0055] At step 306, the internal rotation of the scapula is measured as the angle between the perpendicular line to the SVA through the glenoid and the line from the glenoid to the root of the scapular spine in the transverse plane, as shown graphically at 306a.

[0056] At step 308, the posture of the patient is classified as Type A, Type B or Type C, as illustrated in FIG. 1 and at 308a, based on the measure of the internal rotation of the scapula. Specifically, a Type A has in internal rotation < 36°; A Type B has an internal rotation in the range of 36° - 46°; and a Type C has an internal rotation > 47°. In various examples, these measures may differ slightly.

[0057] At step 310, the placement and orientation of the glenoid component is planned, based on pre-operative glenoid version and / or the preference of the surgeon. The pre-operative humerus geometry is virtually resected based on anatomical landmarks or based on the planned position of the stem. The stem geometry has some reference points that dictate where it should sit with respect to the resection plane. Typically, the plane that resects the humerus is attached to the stem. When moving the stem, the resection plane moves with it.

[0058] At step 312, the virtually resected humerus is brought to a neutral rotation based on the epicondylar axis. Specifically, the humerus is rotated about alongitudinal axis, as shown graphically at 312a, to align the humerus epicondylar axis to the coronal plane of the bone.

[0059] At step 314 , the placement of the stem of the glenoid component is planned based on patient posture to maximize implant ROM in internal / external rotation, as shown graphically at 314a. This may be accomplished virtually. The position of the stem usually depends on its fit within the humerus canal, but its rotation about its longitudinal axis (version angle) can be adjusted. In this case, the stem fit can be obtained manually (but always virtually on the 3D model), with software algorithms, machine learning models or optimization algorithms. Once the stem position is obtained, its version angle can be optimized via software algorithms or optimization algorithms to maximize the internal / external ROM ratio. Specifically, the component is placed such as to minimize the difference between the ROM in internal and external rotations (i.e., it is desired that the ROM be as close to equivalent as possible internal and external rotations - the ratio of the internal and external rotations is maximized).

[0060] Personalized Kinematics and ROM Assessment - With reference now to FIG. 4, in this feature of the examples disclosed herein, at step 402, scapulothoracic and glenohumeral positions are collected pre-operatively in a set of representative poses (e.g., arm elevation, reach opposite shoulder, hand to back pocket) for either the ipsilateral or contralateral joint, to assess patientspecific mobility. This is shown graphically at 402a in FIG. 4. Motion can be measured with simple x-rays or other imaging modalities in static positions,marker-based motion capture, inertial measurement units, EOS imaging, biplane fluoroscopy, or other kinematic measuring technologies. If x-rays are used, 2D-to-3D registration methods can be used to register CT-based bone geometries to the 2D radiographic silhouettes. In one example, contralateral kinematics might be chosen in place of ipsilateral motion because patients might not be able to perform the necessary motor tasks with the impaired joint. In case of a relatively healthy contralateral shoulder, contralateral motion can be assumed to be equivalent to the ipsilateral pre-arthritic motion. In an alternate example, the damaged / arthritic shoulder anatomy can be matched to an atlas-based model of healthy non-arthritic shoulder anatomy. With this "pre-arthritic" anatomy and generic or personalized soft- tissues / muscles, patient specific kinematics can be simulated as a goal for post-op kinematics. Arthritic anatomy can also be matched to healthy anatomy via an atlas-based model.

[0061] At step 404, a set of healthy kinematics for shoulder-relevant motions are conditioned to be representative of the patient’s motion. ADLs may include, for example, combing hair, washing, perineal care, attaching a bra, eating soup with a spoon, putting on a seatbelt, putting on a belt, reaching up for an object. The ADLs may include one or more functional ranges (FRs), which may include, for example, scapula elevation (arm elevation in the scapula plane), external rotation (maximum external rotation of the arm starting in a neutral position with the elbow at 90 degrees and the lower arm pointing forward), reach opposite (touch the opposite shoulder with the hand), reachhead back (touch the back of the head with the hand), reach back (reach behind the back, touching the lower back with the back of the hand), maximum reach back (reach behind the back, touching the upper back as high as possible with the back of the hand), coronal elevation (arm elevation in the coronal / frontal plane of the body) and sagittal elevation (arm elevation in the sagittal plane of the body). It should be noted that this is not an exhaustive list of all possible ADLs and FRs. Other ADLs and FRs may also be included.

[0062] Patient-specific scapulohumeral rhythm, defined as the ratio between scapulothoracic elevation and glenohumeral elevation during arm elevation, can be used to modify healthy kinematics for ADLs and FRs that include an arm elevation motion (e.g., washing, reaching up for an object). If the patient’s rhythm is lower than the one observed for a healthy subject (which is usually 1 :2), ADL and FR kinematics can be modified to account for larger scapulothoracic elevation and lower glenohumeral elevation. For example, if the measured ratio is 1: 1, when the arm is elevated at 90° scapulothoracic elevation and glenohumeral elevation should be changed from 30° and 60°, respectively (healthy rhythm), to 45° and 45°, respectively (personalized rhythm).

[0063] In other examples, sets of kinematics from different patient populations could be used to drive ADL and FR simulations. For example, motion data may be collected on healthy subjects, minor rotator cuff patients, major rotator cuff patients, subjects with different postures (as described above), anatomic TSA patients, reverse TSA patients, younger vs older TSA patients, etc.

[0064] The healthy kinematics can be obtained from marker-based motion capture data collections with healthy subjects. Other technologies may be used to measure kinematics, such as inertial measurement units or fluoroscopy.

[0065] At step 406, ADLs and sports activities are selected from an available list, shown graphically at 406a, based on surgeon and patient preference. The selected ADLs are those ADLs that are most likely to be need or desired by the patient on a day-to-day basis.

[0066] At step 408, the personalized risk of impingement is assessed and implant placement and selection is optimized to minimize the risk of impingement for the selected motions. For each ADL, distance to impingement (in case no virtual impingement is detected) or percent of activity cycle with impingement (in case impingement is detected) can be calculated. An optimization algorithm can be used to minimize the total percent cycle of selected impinging ADLs, for example by reducing the distance to impingement for the non-impinging ADLs. In various examples, any known optimization algorithms can be used. Typical optimization algorithms mathematically define an objective function that needs to be minimized (in various examples herein, the function could be the sum of the ADL percent cycles with impingement, or the volume of the impinging areas) and then they iteratively change some input parameters (in various examples described herein, some implant selection and placement parameters) until the minimum objective function is found.

[0067] Personalized Muscle Function Assessment - Muscle volume and fatty infiltrations are known to accelerate rotator cuff tears and are also correlated to post-operative function and recovery. In addition, muscle volume and fatty infiltrations are proportional to muscle force-generating capacity. Therefore, with reference to FIG. 5, at step 502, the muscle volume and fatty infiltrations of the shoulder of the patient are measured based on imaging, as shown graphically at 502a. The imaging is preferably collected using CT or MRI, but alternative imaging modality may be used. Manual, semi-automatic, or automatic segmentation algorithms can be used to reconstruct muscle volume and identify fatty infiltrations.

[0068] Once bony anatomy and muscle volume are reconstructed, MRI imaging, statistical methods, or a combination of the two can be used to locate muscle attachments on the patient’s virtual bones at step 504. For example, muscle attachment locations and muscle volume and muscle quality may be derived from ultrasound.

[0069] Morphing or non-rigid registration algorithms can be used to morph the 3D bone geometry of an annotated template to identify muscle attachments on the patient’s anatomy. If segmented tendons from MRI imaging are available, their attachment location can be used, or this information can be included in the morphing process with a selected weight. This is shown graphically at 504a.

[0070] At step 506, pre-operative force data generated by the patient’s shoulder muscles in static isometric positions is optionally collected. The force data isused to reverse-engineer personalized muscle mathematical models. Muscle force generating capacity does not depend only on volume, but also on stiffness and length parameters. Measures of force generated during maximum isometric contraction of different muscle groups in different static positions can be used to reverse-engineer personalized muscle mathematical models.

[0071] At step 508, personalized 3D mathematical models of the shoulder including bone and muscle anatomy are generated. Muscle mathematical models can be informed by and based on the data and calculations from the previous steps. In addition, physician diagnosis and planned surgical approach can be incorporated in the model by entirely or partially removing muscles. An example of such a model is shown at 508a.

[0072] To generate the mathematical model, a computer model is utilized. The muscle attachment locations, and / or the one or more measurements, the mathematical model of the shoulder may be generated to model the bony and muscular anatomy of the patient and properties thereof. In some examples, the mathematical model is a 3D mathematical model. In some examples, the mathematical model comprises one or more mathematical equations generated based on the computer model, the muscle attachment locations, and / or the one or more measurements. It should be understood that these models may utilize patient-specific images such as MRI images, statistical models that relate bone morphology to muscle attachment locations, or a combination thereof.

[0073] The 3D mathematical model may describe the bony and muscular anatomy of the patient in a variety of manners. Generally, 3D unidimensional orvolumetric models of the muscles spanning the hip are used to describe the muscular dynamics of the hip joint. In some examples, large muscles are divided into a plurality of musculotendon units to more accurately describe a force application distribution on the muscle attachment locations.

[0074] In some examples, each musculotendon unit may be defined by a Hill-type muscle model that describes the musculotendon unit as a force-generating cable-like element with active and passive properties controlled by, for example, the following four parameters: optimal fiber length, tendon slack length, maximum isometric force, and pennation angle. In some examples, the four parameters are scaled to a size application to the patient based on historical anatomical data. In some examples, the historical anatomical data comprises cadaver dissection average data. In some examples, the historical anatomical data comprises historical patient data. In some examples, the historical anatomical data comprises a combination of cadaver dissection average data and historical patient data.

[0075] In some examples, the 3D mathematical model includes and accounts for additional dynamics of the muscles with respect to the bony anatomy. For example, the 3D mathematical model may account for the sliding of muscles on underlying geometries (e.g., bones and other muscles). Wrapping of muscles on underlying anatomical structures may also be included.

[0076] At step 510, the muscle forces required to perform selected ADLs are estimated, targeting either generic healthy motion or personalized motion as previously discussed. The certain examples, the selected ADLs may be theT2.same as those selected during the personalized assessment of the patient’s kinematics and ROM, if that feature is performed as part of the overall process.

[0077] At step 512, finite element models of the patient’s scapula can be used to estimate stress in the acromion during ADLs. Finite element models and mathematical models can simulate stress and strain in the model components. Each component (e g., the scapula) is discretized in a mesh composed by small elements (e.g., small "pyramids" connected to each other). Then boundary conditions (constraints and external forces) are applied to the model, and stresses and strains in the bone can be calculated.

[0078] The models may use the deltoid force, which may be calculated as part of step 510. The change in stress between the pre-operative and post-operative states can be used to estimate risk of acromion stress fracture in the three regions where this event has been reported to happen. Arm lengthening caused by the implant tends to lengthen the deltoid muscle, which causes passive tension in it and consequently excessive forces applied to the acromion.

[0079] An exemplary graphical display of the stress is shown at 512a. The stress predictions can be used to inform and optimize implant selection and placement. For example, if predicted stress surpasses a specific threshold, changes in implant placement or selection may be recommended.

[0080] In various examples, the features discussed above may be implemented in a pre-operative surgical planning pipeline that includes the contribution of experienced engineers, who receive imaging scans and patient data,reconstruct the necessary anatomical structures, position the implant components, and send the plan back to the surgeon.

[0081] In yet other examples, modeling and simulation results from a number of patient-specific cases may be used to generate artificial intelligence or machine learning based implant selection and placement clusters. Such clusters may be representative of the planning philosophies or techniques of a particular surgeon, which may be used to provide initial placement and selection within the planning tool that better align with surgeon preference. The clusters may also be used to correlate implant selections and placements with post-operative implant survival and patient reported outcomes and eventually to inform patient-specific surgical plan to optimize implant survival, outcomes, function, or some other post-operative measure.

[0082] FIG. 6 provides an illustration of an exemplary computer-assisted surgical system 600, with which the systems and methods of the present disclosure may be implemented. System 600 uses computers, robotics, and imaging technology to aid surgeons in performing orthopedic surgery procedures such as total shoulder arthroplasty. For example, surgical navigation systems can aid surgeons in locating patient anatomical structures, guiding surgical instruments, and implanting medical devices with a high degree of accuracy. Surgical navigation systems 600 often employ various forms of computing technology to perform a wide variety of standard and minimally invasive surgical procedures and techniques. Moreover, these systems allow surgeons to more accurately plan, track and navigate the placement of instruments andimplants relative to the body of a patient, as well as conduct pre-operative and intra-operative body imaging.

[0083] An effector platform 605 positions surgical tools relative to a patient during surgery. The exact components of the effector platform 605 will vary, depending on the example employed. For example, for a knee surgery, the effector platform 605 may include an end effector 605B that holds surgical tools or instruments during their use. The end effector 605B may be a handheld device or instrument used by the surgeon (e.g., a NAVIO® hand piece or a cutting guide or jig) or, alternatively, the end effector 605B can include a device or instrument held or positioned by a robotic arm 605 A.

[0084] The effector platform 605 can include a limb positioner 605 C for positioning the patient’s limbs during surgery. One example of a limb positioner 605C is the SMITH & NEPHEW SPIDER2 system. The limb positioner 605C may be operated manually by the surgeon or alternatively change limb positions based on instructions received from the surgical computer 650 (described below).

[0085] Resection equipment (e.g., end effector 605B) in FIG. 6) performs bone or tissue resection using, for example, mechanical, ultrasonic, or laser techniques. Examples of resection equipment include drilling devices, burring devices, oscillatory sawing devices, vibratory impaction devices, reamers, ultrasonic bone cutting devices, radio frequency ablation devices, and laser ablation systems. In some examples, the resection equipment is held and operated by the surgeon during surgery. In other examples, the effector platform 605 may be used to hold the resection equipment during use.

[0086] The effector platform 605 can also include a cutting guide or jig 605D that is used to guide saws or drills used to resect tissue during surgery. Such cutting guides 605D can be formed integrally as part of the effector platform 605 or robotic arm 605A or cutting guides can be separate structures that can be removably attached to the effector platform 605 or robotic arm 605A. The effector platform 605 or robotic arm 605A can be controlled by the system 600 to position a cutting guide or jig 605D adjacent to the patient’s anatomy in accordance with a pre-operatively or intra-operatively developed surgical plan such that the cutting guide or jig will produce a precise bone cut in accordance with the surgical plan.

[0087] The tracking system 615 uses one or more sensors to collect real-time position data that locates the patient’s anatomy and surgical instruments. For example, for TKA procedures, the tracking system 615 may provide a location and orientation of the end effector 605B during the procedure. In addition to positional data, data from the tracking system 615 can also be used to infer velocity / acceleration of anatomy / instrumentation, which can be used for tool control. In some examples, the tracking system 615 may use a tracker array attached to the end effector 605B to determine the location and orientation of the end effector 605B. The position of the end effector 605B may be inferred based on the position and orientation of the tracking system 615 and a known relationship in three-dimensional space between the tracking system 615 and the end effector 605B. Various types of tracking systems may be used in various examples of the present disclosure including, without limitation,Infrared (TR) tracking systems, electromagnetic (EM) tracking systems, video or image-based tracking systems, and ultrasound registration and tracking systems.

[0088] The display 625 provides graphical user interfaces (GUIs) that display images and the user interface of the navigated surgical platform, as well as the user interface of the systems and methods of the present disclosure, shown in FIGS. 2-5, as well other information relevant to the surgery. For example, in some examples, the display 625 overlays image information collected from various modalities (e.g., CT, MRI, X-ray, fluorescent, ultrasound, etc.) collected pre-operatively or intra-operatively to give the surgeon various views of the patient’s anatomy as well as real-time conditions. The display 625 may include, for example, one or more computer monitors. As an alternative or supplement to the display 625, one or more members of the surgical staff may wear an augmented reality (AR) Head Mounted Device (HMD). For example, in FIG. 6 the surgeon 611 is wearing an AR HMD 655 that may, for example, overlay pre-operative image data on the patient or provide surgical planning suggestions. Various example uses of the AR HMD 655 in surgical procedures are detailed in the sections that follow.

[0089] Surgical computer 650 provides control instructions to various components of system 100, collects data from those components, and provides general processing for various data needed during surgery. In some examples, the surgical computer 650 is a general-purpose computer. In other examples, the surgical computer 650 may be a parallel computing platform that uses multipleT1central processing units (CPUs) or graphics processing units (GPU) to perform processing. In some examples, the surgical computer 650 is connected to a remote server over one or more computer networks (e.g., the Internet). The remote server can be used, for example, for storage of data or execution of computationally intensive processing tasks.

[0090] In various examples, the one or more applications 110, 112, 114 and 116, as well as workflow 100 of the present disclosure may be implemented as one or more software applications executing on surgical computer 650 integrated with or separate from the software controlling the navigated surgical platform. Also, in various examples, the pre-operative workflow 100 may be implemented as a software application executing on a computing platform other than surgical computer 650, such as a laptop or tablet computing device. The computing device executing workflow 100 may be in communication with surgical computer 650 such as to provide the ability to transfer the results of the analyses to surgical computer 650.

[0091] As used in this document, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art. As used in this document, the term “comprising” means “including, but not limited to.”

[0092] The present disclosure has been described in the context of specific examples, which are intended only as exemplars of the disclosure. As would be realized, many variations of the described examples are possible. For example,variations in the design, shape, size, location, function and operation of various components, including both software and hardware components, would still be considered to be within the scope of the disclosure, which is defined by the following claims.

Claims

ClaimsWe claim:

1. A method of optimizing implant placement during total shoulder arthroplasty comprising: obtaining imaging of a shoulder of a patient; reconstructing bony anatomy of a scapula and a humerus of the shoulder of the patient, based on the imaging; measuring an internal rotation of the scapula; assessing posture of the patient; virtually resecting a proximal portion of the humerus; bringing the humerus to a neutral rotation; and planning placement of one or more components of the implant based on the posture to maximize implant range-of-motion in internal and external rotation.

2. The method of claim 1, wherein one of the one or more components of the implant is a glenoid component and wherein placement of the glenoid component is a rotation of the glenoid component about a longitudinal axis.

3. The method of claim 1, wherein the posture is assessed as a particular type based on an internal rotation of the scapula.

4. The method of claim 3, wherein the internal rotation of the scapula is measured as an angle between a perpendicular line to a sagittal vertebral axis through the glenoid and a line from the glenoid to the root of a scapular spine in a transverse plane.

5. The method of claim 1, wherein the humerus is brought to the neutral rotation by rotating the humerus about a longitudinal axis to align an epicondylar axis of the humerus to a coronal plane of the bone.

6. The method of claim 1, wherein the implant range-of-motion is maximized when there is an equivalent range-of-motion in internal and external rotation.

7. A method to plan implant selection and placement during total shoulder arthroplasty comprising: obtaining imaging of the shoulder of a patient; preop eratively collecting scapulothoracic and glenohumeral positions in a set of representative poses from the imaging; conditioning a set of personalized kinematics for shoulder-related ADLs; selecting a set of ADLs; and optimizing selection and placement of the implant for the selected ADLs.

8. The method of claim 7, wherein the imaging is obtained for either ipsilateral or contralateral poses.

9. The method of claim 7, further comprising: using 2D-to-3D registration to register 3D bone geometries from the imaging to 2D radiographic silhouettes.

10. The method of claim 7, wherein conditioning a set of personalized kinematics comprises: modifying healthy subject kinematics for the selected ADLs to personalize to the patient using a trade-off between scapulothoracic elevation and glenohumeral elevation.

11. The method of claim 7, wherein the ADLs are selected from a list of ADLs based on preferences of a surgeon and patient.

12. The method of claim 7, wherein optimizing the implant selection and placement comprises: placing components of the implant such as to minimize a risk of implant-to- bone or bone-to-bone impingement.

13. The method of claim 12, wherein minimizing the risk of impingement comprises:calculating a distance to impingement or a percent of an activity cycle with impingement; and applying an optimization algorithm to minimize the distance to impingement or the percent of an activity cycle with impingement over the set of selected ADLs.

14. A method of planning implant selection and placement for total shoulder arthroplasty comprising: obtaining imaging of the shoulder of a patient; measuring muscle volume and fatty infiltration of the shoulder; locating muscle attachment points on the bones of the shoulder; generating 3D mathematical models of the shoulder that include bone and muscle anatomy; estimating, using the mathematical models, muscle forces required to perform selected ADLs; estimating stress on an acromion during the performance of the selected ADLs; and personalizing implant selection and placement based on the estimated stress and the risk of an acromion stress fracture.

15. The method of claim 14, further comprising:collecting pre-operative force data generated by shoulder muscles of the patient.

16. The method of claim 15, wherein the pre-operative force data is used to reverse-engineer a personalized muscle mathematical model.

17. The method of claim 14, wherein the muscle volume and fatty infdtration are reconstructed from the imaging using segmentation algorithms.

18. The method of claim 14, wherein locating the muscle attachment points further comprises: morphing a 3D bone geometry of an annotated template to identify muscle attachments points on the shoulder of the patient.

19. The method of claim 14, wherein the muscle forces required to perform selected ADLs are calculated based on either generic healthy motion or on motion personalized to the patient.

20. The method of claim 14, wherein the estimated muscle forces include a deltoid force, further comprising using finite element models of the scapula to estimate stress in the acromion during the selected ADLs, based on the deltoid force.

21. A method for performing pre-operative planning of j oint replacement surgery comprising: assessing posture of a patient;utilizing the assessed posture to optimize one or more implant performance metrics; assessing joint kinematics of the patient; selecting one or more ADLs; utilizing the assessed kinematics to select and place an implant to provide optimal performance during the selected ADLs; generating a personalized j oint model including bones and muscles; and estimate post-operative risk of failure of the implant based on the joint model.

22. A system comprising: a processor; and software that, when executed by the processor, performs the steps of the method of any of claims 1, 7, 14 or 21.

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