When a knee flex deeply, the posterior side of thigh and calf contact. The contact force is unignorable to estimate the load acting on a knee because the force generates extensional moment on the knee, and the moment might be about 20–80% of the flexional moment generated by a floor reacting force. Besides, the thigh-calf contact force varies so much even if the posture or the test subject are the same that it is hard to use the average value to estimate the knee load. We have assumed that the force might change not only by the individual physical size but also by a slight change of the posture, especially the angle of the upper body. Therefore we tried to create the estimation equation for the thigh-calf contact force using both anthropometric sizes and posture angles as parameters. The objective posture was kneeling, both plantarflexing and dorsiflexing the ankle joint. Test subjects were 10 healthy males. They were asked to sit on a floor with kneeling, and to tilt their upper body forward and backward. The estimation equations were created as the linear combinations of the parameters, determining the coefficient as to minimize the
The spinopelvic alignment is often assessed via the Pelvic Incidence-Lumbar Lordosis (PI-LL) mismatch. Here we describe and validate a simplified method to evaluating the spinopelvic alignment through the L1-Pelvis angle (L1P). This method is set to reduce the operator error and make the on-film measurement more practicable. 126 standing lateral radiographs of patients presenting for Total Hip Arthroplasty were examined. Three operators were recruited to label 6 landmarks. One operator repeated the landmark selection for intra-operator analysis. We compare PI-LL mismatch obtained via the conventional method, and our simplified method where we estimate this mismatch using PI-LL = L1P - 90°. We also assess the method's reliability and repeatability. We found no significant difference (p > 0.05) between the PI-LL mismatch from the conventional method (mean 0.22° ± 13.6) compared to L1P method (mean 0.0° ± 13.1). The overall average normalised
Introduction & Aims. In other medical fields, smart implantable devices are enabling decentralised monitoring of patients and early detection of disease. Despite research-focused smart orthopaedic implants dating back to the 1980s, such implants have not been adopted into regular clinical practice. The hardware footprint and commercial cost of components for sensing, powering, processing, and communicating are too large for mass-market use. However, a low-cost, minimal-modification solution that could detect loosening and infection would have considerable benefits for both patients and healthcare providers. This proof-of-concept study aimed to determine if loosening/infection data could be monitored with only two components inside an implant: a single-element sensor and simple communication element. Methods. The sensor and coil were embedded onto a representative cemented total knee replacement. The implant was then cemented onto synthetic bone using polymethylmethacrylate (PMMA). Wireless measurements for loosening and infection were then made across different thicknesses of porcine tissue to characterise the sensor's accuracy for a range of implantation depths. Loosening was simulated by taking measurements before and after compromising the implant-cement interface, with fluid influx simulated with phosphate-buffered saline solution. Elevated temperature was used as a proxy for infection, with the sensor calibrated wirelessly through 5 mm of porcine tissue across a temperature range of 26–40°C. Results. Measurements for loosening and infection could be acquired simultaneously with a duration of 4 s per measurement. For loosening, the debonded implant-cement interface was detectable up to 10 mm with 95% confidence. For temperature, the sensor was calibrated with a
INTRODUCTION. To assess and compare the effect of new orthopedic surgical procedures, in vitro evaluation remains critical during the pre-clinical validation. Focusing on reconstruction surgery, the ability to restore normal kinematics and stability is thereby of primary importance. Therefore, several simulators have been developed to study the kinematics and create controlled boundary conditions. To simultaneously capture the kinematics in six degrees of freedom as outlined by Grood & Suntay, markers are often rigidly connected to the moving bone segments. The position of these markers can subsequently be tracked while their position relative to the bones is determined using computed tomography (CT) of the test specimen with the markers attached. Although this method serves as golden standard, it clearly lacks real-time feedback. Therefore, this paper presents the validation of a newly developed real-time framework to assess knee kinematics at the time of testing. MATERIALS & METHODS. A total of five cadaveric fresh frozen lower limb specimens have been used to quantitatively assess the difference between the golden standard, CT based, method and the newly developed real-time method. A schematic of the data flow for both methods. Prior to testing, both methods require a CT scan of the full lower limb. During the tests, the proximal femur and distal tibia are necessarily resected to fit the knees in the test setup, thus also removing the anatomical landmarks needed to evaluate their mechanical axis. Subsequently, a set of three passive markers are rigidly attached to the femur and tibia, referred to as M3F and M3T respectively. For the CT based method, the marker positions are captured during the tests and a second CT scan is eventually performed to link the marker positions to the knee anatomy. Using in-house developed software, this allowed to offline evaluate the knee kinematics in six degrees of freedom by combining both CT datasets with the tracked marker positions. For the newly developed real-time method, a calibration procedure is first performed. This calibration aims to link the position of the 3D reconstructed bone and landmarks with the attached markers. A set of bone surface points is therefore registered. These surface points are obtained by tracking the position of a pen while touching the bone surface. The pen's position is thereby tracked by three rigidly attached markers, denoted M3P. The position of the pen tip is subsequently calculated from the known pen geometry. The iterative closest point (ICP) algorithm is then used to match the 3D reconstructed bone to the registered surface points. Two types of 3D reconstructions have therefore been considered. First, the original reconstructions were used, obtained from the CT data. Second, a modified reconstruction was used. This modification accounted for the finite radius (r = 1.0 mm) of the registration pen, by shifting the surface nodes 1.0 mm along the direction of the outer surface normal. During the tests, the positions of the femur and tibia markers are tracked and streamed in real-time to an in-house developed, Matlab based software framework (MathWorks Inc., Natick, Massachussets, USA). This software framework simultaneously calculates the bone positions and knee kinematics in six degrees of freedom, displaying this information to the surgeons and operators. To assess the accuracy, all knee specimens have been subjected to passive flexion-extension movement ranging from 0 to 120 degrees of flexion. For each degree of freedom, the average
INTRODUCTION. Statistical shape models (SSM) have become a common tool to create reference models for design input and verification of total joint implants. In a recent discussion paper around Artificial Intelligence and Machine Learning, the FDA emphasizes the importance of independent test data [1]. A leave-one-out test is a standard way to evaluate the generalization ability of an SSM [2]; however, this test does not fulfill the independence requirement of the FDA. In this study, we constructed an SSM of the knee (femur and tibia). Next to the standard leave-one-out validation, we used an independent test set of patients from a different geographical region than the patients used to build the SSM. We assessed the ability of the SSM to predict the shapes of knees in this independent test set. METHODS. A dataset of 82 computed tomography (CT) scans of Caucasian patients (42 male, 40 female) from 11 different geographic locations in France, Germany, Austria, Italy and Australia were used as training set to make an SSM of the femur and tibia. A leave-one-out test was performed to assess the ability of the SSM to predict shapes within the training set. A test dataset of 4 CT scans of Caucasian patients from Russia were used for the validation. The SSM was fitted onto each of the femur and tibia shapes and the
INTRODUCTION. While computational models have been used for many years to contribute to pre-clinical, design phase iterations of total knee replacement implants, the analysis time required has limited the real-time use as required for other applications, such as in patient-specific surgical alignment in the operating room. In this environment, the impact of variation in ligament balance and implant alignment on estimated joint mechanics must be available instantaneously. As neural networks (NN) have shown the ability to appropriately represent dynamic systems, the objective of this preliminary study was to evaluate deep learning to represent the joint level kinetic and kinematic results from a validated finite element lower limb model with varied surgical alignment. METHODS. External hip and ankle boundary conditions were created for a previously-developed finite element lower limb model [1] for step down (SD), deep knee bend (DKB) and gait to best reproduce in-vivo loading conditions as measured on patients with the Innex knee (. orthoload.com. ) (Figure1). These boundary conditions were subsequently used as inputs for the model with a current fixed-bearing total knee replacement to estimate implant-specific kinetics and kinematics during activities of daily living. Implant alignments were varied, including variation of the hip-knee-ankle angle-±3°, the frontal plane joint line −7° to +5°, internal-external femoral rotation ±3°, and the tibial posterior slope 5° and 0°. Through varying these parameters a total of 2464 simulations were completed. A NN was created utilizing the NN toolbox in MATLAB. Sequence data inputs were produced from the alignment and the external boundary conditions for each activity cycle. Sequence outputs for the model were the 6 degree of freedom kinetics and kinematics, totaling 12 outputs. All data was normalized across the entire data set. Ten percent of the simulation runs were removed at random from the training set to be used for validation, leaving 2220 simulations for training and 244 for validation. A nine-layer bi-long short-term memory (LSTM) NN was created to take advantage of bi-LSTM layers ability to learn from past and future data. Training on the network was undertaken using an RMSprop solver until the
Introduction. Machine learning is a relatively novel method to orthopaedics which can be used to evaluate complex associations and patterns in outcomes and healthcare data. The purpose of this study is to utilize 3 different supervised machine learning algorithms to evaluate outcomes from a multi-center international database of a single shoulder prosthesis to evaluate the accuracy of each model to predict post-operative outcomes of both aTSA and rTSA. Methods. Data from a multi-center international database consisting of 6485 patients who received primary total shoulder arthroplasty using a single shoulder prosthesis (Equinoxe, Exactech, Inc) were analyzed from 19,796 patient visits in this study. Specifically, demographic, comorbidity, implant type and implant size, surgical technique, pre-operative PROMs and ROM measures, post-operative PROMs and ROM measures, pre-operative and post-operative radiographic data, and also adverse event and complication data were obtained for 2367 primary aTSA patients from 8042 visits at an average follow-up of 22 months and 4118 primary rTSA from 11,754 visits at an average follow-up of 16 months were analyzed to create a predictive model using 3 different supervised machine learning techniques: 1) linear regression, 2) random forest, and 3) XGBoost. Each of these 3 different machine learning techniques evaluated the pre-operative parameters and created a predictive model which targeted the post-operative composite score, which was a 100 point score consisting of 50% post-operative composite outcome score (calculated from 33.3% ASES + 33.3% UCLA + 33.3% Constant) and 50% post-operative composite ROM score (calculated from S curves weighted by 70% active forward flexion + 15% internal rotation score + 15% active external rotation). 3 additional predictive models were created to control for the time required for patient improvement after surgery, to do this, each primary aTSA and primary rTSA cohort was subdivided to only include patient data follow-up visits >20 months after surgery, this yielded 1317 primary aTSA patients from 2962 visits at an average follow-up of 50 months and 1593 primary rTSA from 3144 visits at an average follow-up of 42 months. Each of these 6 predictive models were trained using a random selection of 80% of each cohort, then each model predicted the outcomes of the remaining 20% of the data based upon the demographic, comorbidity, implant type and implant size, surgical technique, pre-operative PROMs and ROM measures inputs of each 20% cohort. The error of all 6 predictive models was calculated from the
Osteophytes are bony spurs on normal bone that develop as an adaptive reparative process due to excessive stress at/near a joint. As osteophytes develop from normal bone, they are not always well depicted in common imaging techniques (e.g. CT, MRI). This creates a challenge for preoperative planning and image-guided surgical methods that are commonly incorporated in the clinical routine of orthopaedic surgery. The study examined the accuracy of osteophyte detection in clinical CT and MRI scans of varying types of joints. The investigation was performed on fresh-frozen ex-vivo human resected joints identified as having a high potential for presentation of osteophytes. The specimens underwent varying imaging protocols for CT scanning and clinical protocols for MRI. After dissection of the joint, the specimens were subjected to structured 3D light scanning to establish a reference model of the anatomy. Scans from the imaging protocols were segmented and their 3D models were co-registered to the light scanner models. The quality of the osteophyte images were evaluated by determining the
Introduction. Partial knee arthroplasty (PKA) has demonstrated the potential to improve patient satisfaction over total knee arthroplasty. It is however perceived as a more challenging procedure that requires precise adaptation to the complex mechanics of the knee. A recently developed PKA system aims to address these challenges by anatomical, compartment specific shapes and fine-tuned mechanical instrumentation. We investigated how closely this PKA system replicates the balance and kinematics of the intact knee. Materials and Methods. Eight post-mortem human knee specimens (age: 55±11 years, BMI: 23±5, 4 male, 4 female) underwent full leg CT scanning and comprehensive robotic (KUKA KR140 comp) assessments of tibiofemoral and patellofemoral kinematics. Specimens were tested in the intact state and after fixed bearing medial PKA. Implantations were performed by two experienced surgeons. Assessments included laxity testing (anterior-posterior: ±100 N, medial-lateral: ±100 N, internal-external: ±3 Nm, varus- valgus: ±12 Nm) under 2 compressive loads (44 N, 500 N) at 7 flexion angles and simulations of level walking, lunge and stair descent based on in-vivo loading profiles. Kinematics were tracked robotically and optically (OptiTrack) and represented by the femoral flexion facet center (FFC) motions. Similarity between intact and operated curves was expressed by the
Objective. This paper aims to analyze the kinetics of the over-ground wheel-type body weight supporting system (BWS); tendency changes of low extremity joint moment (hip, knee, ankle), 3 axis accelerations of a trunk, cadence and gait velocity as weight bearing level changes. Method. 15 subjects (11 males, 4 females, age:23.63.5, height:170.65.1cm, weight:69.0210.75kg) who had no history of surgery participated. 6 levels (0%, 10%, 20%, 30%, 40% and 50%) of BWS were given to subjects at self-selected gait velocity and kinetic data was calculated using a motion capture system, Vicon. ®. (Vicon, UK). Results. Maximum joint moments at the hip, knee, and ankle decrease as weight bearing increases on the sagittal plane. However, no significant decrease was found after 20% level of BWS at the hip and knee joint. On the other hand, the maximum ankle joint moment keeps decreasing. The
In computer assisted orthopaedic surgery, intraoperative registration is commonly performed by fitting features acquired from the exposed bone surface to a preoperative virtual model of the bone geometry. In cases where the acquired spatial measurements are unreliable or have been inappropriately chosen, the registration result can degenerate. Current performance indicators, such as the
In professional football a key factor regarding injury is the time to return to play. Accurate prediction of this would aid planning by the club in the event of injury. It would also aid the club medical staff. Gaussian processes may be used for machine learning tasks such as regression and classification. This study determines whether machine-learning methods may be used for predicting how many days a player is unavailable to play. A database of injuries at one English Premier League Professional Football Club was reviewed for a number of factors for each injury. Twenty-five variables were recorded for each injury, including time to return to play. This was determined to be the response variable. We used a Gaussian process model with a Laplacian kernel to determine whether the return to play could be predicted from the other variables. The
Introduction. Joint mechanics and implant performance have been shown to be sensitive to ligament properties [1]. Computational models have helped establish this understanding, where optimization is typically used to estimate ligament properties for recreation of physically measured specimen-specific kinematics [2]. If available, contact metrics from physical tests could be used to improve the robustness and validity of these predictions. Understanding specimen-specific relationships between joint kinematics, contact metrics, and ligament properties could further highlight factors affecting implant survivorship and patient satisfaction. Instrumented knee implants offer a means to measure joint contact data both in-vivo and intra-operatively, and can also be used in a controlled experimental environment. This study extends on previous work presented at ISTA [3], and the purpose here was to evaluate the use of instrumented implant contact metrics during optimization of ligament properties for two specimens. The overarching goal of this work is to inform clinical joint balancing techniques and identify factors that are critical to implant performance. Methods. Total knee arthroplasties were performed on 4 (two specimens modeled) cadeveric specimens by an experienced orthopaedic surgeon. An instrumented trial implant (VERASENSE, OrthoSensor, Inc., Dania Beach, FL) was used in place of a standard insert. Experimentation was performed using a simVITROTM controlled robotic musculoskeletal simulator (Cleveland Clinic, Cleveland, OH) to apply intra-operative style loading and measure tibiofemoral kinematics. Three successive laxity style tests were performed at 10° knee flexion: anterior-posterior force (±100 N), varus-valgus moment (±5 Nm), and internal-external moment (±3 Nm). Tibiofemoral kinematics and instrumented implant contact metrics were measured throughout testing (Fig. 1). Specimen-specific finite element models were developed for two of the tested specimens and solved using Abaqus/Explicit (Dassault Systèmes). Relevant ligaments and rigid bone geometries were defined using specimen-specific MRIs. Virtual implantation was achieved using registration and each ligament was modeled as a set of nonlinear elastic springs (Fig. 1). Stiffness values were adopted from the literature [2] while the ligament slack lengths served as control variables during optimization. The objective was to minimize the
Introduction. The longevity of total hip arthroplasty (THA) is dependent on acetabular component position. We measured the reliability and accuracy of a CT-based navigation system to achieve the intended acetabular component position and orientation using three dimensional imaging. The purpose of the current study was to determine if the CT-guided robotic navigation system could accurately achieve the desired acetabular component position (center of rotation (COR)) and orientation (inclination and anteversion). The postoperative orientation and location of the components was determined in 20 patients undergoing THA using CT images, the gold standard for acetabular component orientation. Methods. Twenty primary unilateral THA patients were enrolled in this IRB-approved, prospective cohort study to assess the accuracy of the robotic navigation system. Pre- and post-operative CT exams were obtained and aligned 3D segmented models were used to measure the difference in center of rotation and orientation (anteversion and inclination). Patients with pre-existing implants, posttraumatic arthritis, contralateral hip arthroplasty, septic arthritis, or previous hip fracture were excluded. All patients underwent unilateral THA using robotic arm CT-guided navigation (RIO Makoplasty; MAKO Surgical Corp). Results. Mean age was 59.25 years (±8.65 years), 55% of patients were female (11/20).
Introduction. Computer-assisted orthopaedic surgery (CAOS) has been shown to assist in achieving accurate and reproducible prosthesis position and alignment during total knee arthroplasty (TKA) [1]. The most prevalent modality of navigator tracking is optical tacking, which relies on clear line-of-sight (visibility) between the localizer and the instrumented trackers attached to the patient. During surgery, the trackers may not always be optimally positioned and orientated, sometimes forcing the surgeon to move the patient's leg or adjust the camera in order to maintain tracker visibility. Limited information is known about tracker visibility under clinical settings. This study quantified the rotational limits of the trackers in a contemporary CAOS system for maintaining visibility across the surgical field. Materials and Methods. A CAOS system (ExactechGPS®, Blue-Ortho, Grenoble, FR) was set up in an operating room by a standard surgical table according to the manufacture's recommendation. A grid with 10×10 cm sized cells was placed at the quadrant of the surgical table associated with the TKA surgical field [Fig. 1A,B]. The localizer was set up to aim at the center of the grid. A TKA surgical procedure was then initiated using the CAOS system. Once the trackers-localizer connection was established, the CAOS system constantly monitored the
Are there any patho-anatomical features that might predispose to primary knee OA? We investigated the 3D geometry of the load bearing zones of both distal femur and proximal tibias, in varus, straight and valgus knees. We then correlated these findings with the location of wear patches measured intra-operatively. Patients presenting with knee pain were recruited following ethics approval and consent. Hips, knees and ankles were CT-ed. Straight and Rosenburg weight bearing X-Rays were obtained. Excluded were: Ahlbäck grade “>1”, previous fractures, bone surgery, deformities, and any known secondary causes of OA. 72 knees were eligible. 3D models were constructed using Mimics (Materialise Inc, Belgium) and femurs oriented to a standard reference frame. Femoral condyle Extension Facets (EF) were outlined with the aid of gaussian curvature analysis, then best-fit spheres attached to the Extension, as well as Flexion Facets(FF). Resected tibial plateaus from surgery were collected and photographed, and Matlab combined the average tibia plateau wear pattern. Of the 72 knees (N=72), the mean age was 58, SD=11. 38 were male and 34 female. The average hip-knee-ankle (HKA) angle was 1° varus (SD=4°). Knees were assigned into three groups: valgus, straight or varus based on HKA angle.
INTRODUCTION. Thorough understanding and feedback of the post-operative implant position relative to the pre-operative anatomy is missing in today's clinical practice. However, three dimensional insights in the local under or oversizing of the implant can provide important feedback to the surgeon. For the knee for instance, to identify a shift in the sagittal joint line that potentially links to mid-flexion instability or to identify zones at risk for soft tissue impingement. Despite a proven inferior outcome, clinical post-operative implant evaluation remains primarily based on bi-planar, static 2D x-rays rather than 3D imaging. Along with the cost, a possible reason is the increased radiation dose and/or metal artifact scatter in computed tomography (CT) and/or magnetic resonance imaging (MRI). These detrimental effects are now avoided by using recently released x-ray processing software. This technique uses standard-of-care post-operative x-rays in combination with a pre-operative CT and 3D file of the implant to determine the implant position relative to the pre-operative situation. The accuracy of this new technique is evaluated in this paper using patient cases. Therefore, the obtained implant position is benchmarked against post-operative CT scans. MATERIALS & METHODS. Retrospectively, 19 patients were selected who underwent total knee arthroplasty and received pre- and post-operative CT of their diseased knee. The CT scans were performed with a pixel size of 0.39 mm and slice spacing of 0.60 mm (Somatom, Siemens, München, Germany). All patients underwent TKA surgery using the same bi-cruciate substituting total knee (Journey II, Smith&Nephew, Memphis, USA). Following surgery, standard bi-planar standing x-rays of the operated knee was additionally performed as standard of care. To evaluate the implant position relative to the pre-operative situation, the 3D implants are first positioned on the post-operative CT slices. Using Mimics (Materialise NV, Leuven, Belgium), the pre-operative bone was subsequently automatically matched onto the post-operative scan to identify the implant location relative to the reconstructed pre-operative bone. This has been independently repeated by three observers to assess the inter-observer variability. Second, the post-operative bi-planar x-rays are combined with the reconstructed pre-operative bone and 3D file of the implant. This combination is performed using the 2D-to-3D conversion integrated in the recently launched X-ray module of Mimics. This module uses a contour based registration method to determine the implant and bone position using the post-operative x-rays. For both reconstruction methods, the implant position has been evaluated in six degrees of freedom using an automated Matlab routine; resulting in three translations and three rotations. RESULTS. From the evaluated implant positions, the
Background. Total hip arthroplasty (THA) is one of the most successful surgical procedures ever performed. Nevertheless if procedure is performed by high or low volume surgeons; more than 50% of cups are still placed out of the safe zone, which is connected to lower survival rate of the prosthesis. The idea was to develop an imageless navigation system for safe and accurate positioning of the cup in THA procedures, without a need of any preoperative computer tomography (CT) or magnetic resonance imagining (MRI). Methods. The validation of the system was approved by National Ethics Committee. The committee allowed the validation on 10 patients who all signed the agreement for participation in the study. Unselected patients undergoing THA were included. All patients had had performed preoperative x-rays of pelvis and hips for standard preoperative planning. Immediately before skin incision, anterior pelvic plane (APP) was defined with help of specially developed electromagnetic navigation system (Guiding Star, E-Hip module, Ekliptik d.o.o., Ljubljana, Slovenia) and specificaly designed hardware tool which is essential for accurate APP determination [Fig.1]. In all patients THAs were performed through direct lateral approach and all implanted components (Allofit S cup and Alloclassic stem, Zimmer Inc., Warsaw, Indiana, USA) were implanted with freehand technique according to preoperative plan. After placement of the cups their inclination and anteversion angles were determined with aforementioned navigation system [Fig. 2]. The day after surgery, low dose CT scans of pelvises of operated patients were performed and DICOM format files were up-loaded into EBS software (Ekliptik d.o.o., Ljubljana, Slovenia), a multipurpose application for perioperative planning, measuring and constructing where virtual copies of pelvises were generated. On virtual pelvises the position of the cups was measured by independent person [Fig.3]. Measurements were compared, statistically analysed and the deviation calculated with
INTRODUCTION. The purpose of this study is to elucidate longitudinal kinematic changes of the hip joint during heels-down squatting after THA. METHODS. 66 patients with 76 primary cementless THAs using a CT-based navigation system were investigated using fluoroscopy. An acetabular component and an anatomical femoral component were used through the mini-posterior approach with repair of the short rotators. The femoral head size was 28mm (9 hips), 32mm (12 hips), 36mm (42 hips), and 40mm (12 hips). Longitudinal evaluation was performed at 3 months, 1 year, and 2≤ years postoperatively. Successive hip motion during heels-down squatting was recorded as serial digital radiographic images in a DICOM format using a flat panel detector. The coordinate system of the acetabular and femoral components based on the neutral standing position was defined. The images of the hip joint were matched to 3D-CAD models of the components using a2D/3D registration technique. In this system, the
INTRODUCTION. Understanding the relationship between knee specific tissue behavior and joint contact mechanics remains an area of focus. Seminal work from 1990's established the possibility to optimize tissue properties for recreation of laxity driven kinematics (Mommersteeg et al., 1996). Yet, the uniqueness and validity of such predictions could be strengthened, especially as they relate to joint contact conditions. Understanding this interplay has implications for the long term performance of joint replacements. Development of instrumented knee implants, highlighted by a single use tibial insert trial with embedded sensor technology (VERASENSE, Orthosensor Inc.), may offer an avenue to establish the relationship between tissue state and joint mechanics. Utilization of related data also has the potential to confirm computational predictions, where both rigid body motions and associated reactions are explicitly accounted for. Hence, the goal of this work was to evaluate an approach for optimization of ligament properties using joint mechanics data from an instrumented implant during laxity style testing. Such a framework could be used to inform joint balancing techniques, improve long term implant performance, and alternatively, qualify factors that may lead to poor outcomes. METHODS. Experimentation was performed on a 52 year old male, left, cadaveric specimen. Joint arthroplasty was performed using standard practice by an experienced orthopedic surgeon. To mimic passive intraoperative loading, laxity loading at 10°, 45° and 90° flexion, which consisted of discrete application of anterior-posterior (± 100N), varus-valgus (± 5 Nm) and internal-external (± 3 Nm) loads at each angle, was performed using a simVITROTM robotic musculoskeletal simulator (Cleveland Clinic, Cleveland, OH). Experimental results included relative tibiofemoral kinematics and sensor measured metrics (Fig 1). The finite element model was developed from specimen-specific MRIs and solved using Abaqus/Explicit. The model included the rigid bones, appropriately placed implants and relevant soft-tissue structures (Fig. 1). Ligament stiffness values were adopted from the literature and included a 6% strain toe region. Sets of nonlinear springs, defined using MR imaging, comprised each ligament/bundle. Optimization was performed, which minimized the