For a proper rehabilitation of the knee following knee arthroplasty, a comprehensive understanding of bony and soft tissue structures and their effects on biomechanics of the individual patient is essential. Musculoskeletal models have the potential, however, to predict dynamic interactions of the knee joint and provide knowledge to the understanding of knee biomechanics. Our goal was to develop a generic musculoskeletal knee model which is adaptable to subject-specific situations and to use in-vivo kinematic measurements obtained under full-weight bearing condition in a previous Upright-MRI study of our group for a proper validation of the simulation results. The simulation model has been developed and adapted to subject-specific cases in the multi-body simulation software AnyBody. For the implementation of the knee model a reference model from the AnyBody Repository was adapted for the present issue. The standard hinge joint was replaced with a new complex knee joint with 6DoF. The 3D bone geometries were obtained from an optimized MRI scan and then post-processed in the mesh processing software MeshLab. A homogenous dilation of 3 mm was generated for each bone and used as articulating surfaces. The anatomical locations of viscoelastic ligaments and muscle attachments were determined based on literature data. Ligament parameters, such as elongation and slack length, were adjusted in a calibration study in two leg stance as reference position. For the subject-specific adaptation a general scaling law, taking segment length, mass and fat into account, was used for a global scaling. The scaling law was further modified to allow a detailed adaption of the knee region, e.g. align the subject-specific knee morphology (including ligament and muscle attachments) in the reference model. The boundary conditions were solely described by analytical methods since body motion (apart from the knee region) or force data were not recorded in the Upright-MRI study. Ground reaction forces have been predicted and a single leg deep knee bend was simulated by kinematic constraints, such as that the centre of mass is positioned above the ankle joint. The contact forces in the knee joint were computed using the force dependent kinematic algorithm. Finally, the simulation model was adapted to three subjects, a single leg deep knee bend was simulated, subject-specific kinematics were recorded and then compared to their corresponding in-vivo kinematic measurements data. We were able to simulate the whole group of subjects over the complete range of motion. The tibiofemoral kinematics of three subjects could be simulated showing the overall trend correctly, whereas absolute values partially differ. In conclusion, the presented simulation model is highly adaptable to an individual situation and seems to be suitable to approximate subject-specific knee kinematics without consideration of cartilage and menisci. The model enables sensitivity analyses regarding changes in patient specific knee kinematics following e.g. surgical interventions on bone or soft tissue as well as related to the design and placement of partial or total knee joint replacement. However, model optimisation, a higher case number, sensitivity analyses of selected parameters and a semi-automation of the workflow are parts of our ongoing work

Introduction. Fretting corrosion at the taper interface of modular connections can be studied using Finite Element (FE) analyses. However, the loading conditions in FE studies are often simplified, or based on generic activity patterns. Using musculoskeletal modeling, subject-specific muscle and joint forces can be calculated, which can then be applied to a FE model for wear predictions. The objective of the current study was to investigate the effect of incorporating more detailed activity patterns on fretting simulations of modular connections. Methods. Using a six-camera motion capture system, synchronized force plates, and 45 optical markers placed on 6 different subjects, data was recorded for three different activities: walking at a comfortable speed, chair rise, and stair climbing. Musculoskeletal models, using the Twente Lower Extremity Model 2.0 implemented in the AnyBody modeling System™ (AnyBody Technology A/S, Aalborg, Denmark; figure1), were used to determine the hip joint forces. Hip forces for the subject with the lowest and highest peak force, as well as averaged hip forces were then applied to an FE model of a modular taper connection (Biomet Type-1 taper with a Ti6Al4V Magnum +9 mm adaptor; Figure 2). During the FE simulations, the taper geometry was updated iteratively to account for material removal due to wear. The wear depth was calculated based on Archard's Law, using contact pressures, micromotions, and a wear factor, which was determined from accelerated fretting experiments. Results. The forces for the comfortable walking speed had the highest peak forces for the maximum peak subject, with a maximum peak force of 3644 N, followed by walking up stairs, with a similar maximum peak force of 3626 N. The chair rise had a lower maximum peak force of 2240 N (−38.5%). The simulated volumetric wear followed the trends seen in the peaks of the predicted hip joint forces, with the largest wear volumes predicted for a comfortable walking speed, followed by the stairs up activity and the chair rise (Figure 3). The subjects with the highest peak forces produced the most volumetric wear in all cases. However, the lowest peak subject had a higher volumetric wear for the stairs up case than the average subject. Discussion. This study explored the effect of subject-specific variations in hip joint loads on taper fretting. The results indicate that taper wear was predominantly affected by the magnitudes of the peak forces, rather than by the orientation of the force. A more comprehensive study, capturing the full spectrum of patient variability, can help identifying parameters that accelerate fretting corrosion. Such a study should also incorporate other sources of variability, including surgical factors such as implant orientation, sizing, and offset. These factors also affect hip joint forces, and can be evaluated in musculoskeletal models such as presented here

Introduction. Total knee replacement (TKR) is an established and effective surgical procedure in case of advanced osteoarthritis. However, the rate of satisfied patients amounts only to about 75 %. One common cause for unsatisfied patients is the anterior knee pain, which is partially caused by an increase in patellofemoral contact force and abnormal patellar kinematics. Since the malpositioning of the tibial and the femoral component affects the interplay in the patellofemoral joint and therefore contributes to anterior knee pain, we conducted a computational study on a cruciate-retaining (CR) TKR and analysed the effect of isolated femoral and tibial component malalignments on patellofemoral dynamics during a squat motion. Methods. To analyse different implant configurations, a musculoskeletal multibody model was implemented in the software Simpack V9.7 (Simpack AG, Gilching, Germany) from the SimTK data set (Fregly et al.). The musculoskeletal model comprised relevant ligaments with nonlinear force-strain relation according to Wismans and Hill-type muscles spanning the lower extremity. The experimental data were obtained from one male subject, who received an instrumented CR TKR. Muscle forces were calculated using a variant of the computed muscle control algorithm. To enable roll-glide kinematics, both tibio- and patellofemoral joint compartments were modelled with six degrees of freedom by implementing a polygon-contact-model representing the detailed implant surfaces. Tibiofemoral contact forces were predicted and validated using data from experimental squat trials (SimTK). The validated simulation model has been used as reference configuration corresponding to the optimal surgical technique. In the following, implant configurations, i.e. numerous combinations of relative femoral and tibial component alignment were analysed: malposition of the femoral/tibial component in mediolateral (±3 mm) and anterior-posterior (±3 mm) direction. Results. Mediolateral translation/malposition of the tibial component did not show high influence on the maximal patellofemoral contact force. Regarding the mediolateral translation of the femoral component, similar tendencies were observed. However, lateralisation of the femoral component (3 mm) clearly increased the lateral patella shift and medialisation of the tibial component (3 mm) led to a slightly increased lateral patella shift. Compared to the reference model, pronounced posterior translation of the tibial and femoral component resulted in a lower patellofemoral contact force, further increasing with higher anterior translation of the components. The translation of the tibial component showed smaller influence on the patellofemoral contact force than the translation of the femoral component. Discussion. In our present study, the mediolateral malposition of the femoral and tibial component showed no major impact on patellofemoral contact force and contribution to anterior knee pain in patients with CR TKR. However, the influence of implant component positioning in anterior-posterior direction on patellofemoral contact force is evident, especially for the femoral component. Our generated musculoskeletal model can contribute to computer-assisted preclinical testing of TKR and may support clinical decision-making in preoperative planning

Introduction. Dislocation of total hip replacements (THRs) remains a severe complication after total hip arthroplasty. However, the contribution of influencing factors, such as implant positioning and soft tissue tension, is still not well understood due to the multi-factorial nature of the dislocation process. In order to systematically evaluate influencing factors on THR stability, our novel approach is to extract the anatomical environment of the implant into a musculoskeletal model. Within a hardware-in-the-loop (HiL) simulation the model provides hip joint angles and forces for a physical setup consisting of a compliant support and a robot which accordingly moves and loads the real implant components [2]. The purpose of this work was to validate the HiL test system against experimental data derived from one patient. Methods. The musculoskeletal model includes all segments of the right leg with a simplified trunk. Bone segments were reconstructed from a human computed tomography dataset. The segments were mutually linked in the multibody software SIMPACK (v8.9, Simpack AG, Gilching, Germany) by ideal joints starting from the ground-fixed foot. Furthermore, inertia properties were incorporated based on anthropometric data. Inverse dynamics was used to obtain muscle forces. Thus, optimization techniques were implemented to resolve the distribution problem of muscle forces whereas muscles were assumed to act along straight lines. For validation purposes the model was scaled to one patient with an instrumented THR [1]. Averaged kinematic measurements were used to obtain joint angles for a knee-bending motion. Then, the model was exported into real-time capable machine code and embedded into the HiL environment. Real implant components of a standard THR were attached to the endeffector of the robot and the compliant support. Finally, the HiL simulation was carried out simulating knee-bending. Experimentally measured hip joint forces from the patient [1] were used to validate the HiL simulation. Results. According to the joint angles obtained a knee-bending motion was carried out during the HiL simulation (Fig. 1). Predicted components of the hip joint force were in-between the envelopes of measured in-vivo data with partial deviation of the y-component (Fig. 2). The force application by the robot agreed well with the force values provided by the model. Discussion. Previous quasi-static mechanical setups for testing subluxation and dislocation of THRs neglected the impact of soft tissue structures on actual joint loading. Therefore, we combine the advantages of robot-based testing and numerical simulations within a HiL approach for dynamic analyses of THRs [2]. Thereby, validation is required to enhance the credibility of test results. The data presented demonstrate that the HiL test system with the embedded musculoskeletal model is capable of providing comparable THR loading as derived from in-vivo data. Certain deviations of the joint force's y-component will be the focus of up-coming model improvements. By considering dislocation-associated movements such as deep knee-bending, the influence of implant design and positioning on THR stability can be evaluated under reproducible, physiological-like conditions in subsequent studies

Introduction. Unicompartmental Knee Replacement Arthroplasty (UKA) is a treatment option for early knee OA that appears under-utilised, partly because of a lack of clear guidance on how to best restore lasting knee function using such devices. Computational tools can help consider inherent uncertainty in patient anatomy, implant positioning and loading when predicting the performance of any implant. In the present research an approach for creating patient-specific finite element models (FEM) incorporating joint and muscle loads was developed to assess the response of the underlying bone to UKA implantation. Methods. As a basis for future uncertainty modelling of UKA performance, the geometriesof 173 lower limbs weregenerated from clinical CT scans. These were segmented (ScanIP, Simpleware Ltd, UK) to reconstruct the 3D surfaces of the femur, tibia, patella and fibula. The appropriate UKA prosthesis (DePuy, U.S.) size was automatically selected according to tibial plateau size and virtually positioned (Figure 1). Boolean operations and mesh generation were accomplished with ScanIP. A patient-specific musculoskeletal model was generated in open-source software OpenSim (Delp et al. 2007) based on the Gait2392 model. The model was scaled to a specific size and muscle insertion points were modified to corresponding points on lower limb of patient. Hip joint load, muscle forces and lower limb posture during gait cycle were calculated from the musculoskeletal model. The FE meshes of lower limb bones were transformed to the corresponding posture at each time point of a gait cycle and FE analyses were performed (Ansys, Inc. U.S) to evaluate the strain distribution on the tibial plateau in the implanted condition. Results. With the tibial component positioned above, along or below the joint line, the lower limb alignment was more varus, remained unaltered or more valgus respectively (Figure 2). With the tibial component positioned 3mm above the joint line, the peak strain in the underlying bone was 670 µstrain on medial (UKA) side and 6780 µstrain on the intact side. With the tibial component positioned 3mm below the joint line, the peak strain was 3010 µstrain on the medial side and 5330 µstrain on the intact side. Here, the strains on the medial side increased by 2640 µstrain whilst they were reduced by 1450 µstrain on the intact side compared to the unimplanted case. Conclusion. The present research has delivered a framework which can be exploited in future uncertainty modelling of UKA performance predictions. The patient-specific model incorporates loading, anatomical and material property variability, and can be applied to evaluate the performance of UKA prostheses for metrics such as stress/strain/micromotions in larger patient populations. For figures/tables, please contact authors directly.

Introduction. Although total knee arthroplasty (TKA) is generally considered successful, 16–30% of patients are dissatisfied. There are multiple reasons for this, but some of the most frequent reasons for revision are instability and joint stiffness. A possible explanation for this is that the implant alignment is not optimized to ensure joint stability in the individual patient. In this work, we used an artificial neural network (ANN) to learn the relation between a given standard cruciate-retaining (CR) implant position and model-predicted post-operative knee kinematics. The final aim was to find a patient-specific implant alignment that will result in the estimated post-operative knee kinematics closest to the native knee. Methods. We developed subject-specific musculoskeletal models (MSM) based on magnetic resonance images (MRI) of four ex vivo left legs. The MSM allowed for the estimation of secondary knee kinematics (e.g. varus-valgus rotation) as a function of contact, ligament, and muscle forces in a native and post-TKA knee. We then used this model to train an ANN with 1800 simulations of knee flexion with random implant position variations in the ±3 mm and ±3° range from mechanical alignment. The trained ANN was used to find the implant alignment that resulted in the smallest mean-square-error (MSE) between native and post-TKA tibiofemoral kinematics, which we term the dynamic alignment. Results. Dynamic alignment average MSE kinematic differences to the native knees were 1.47 mm (± 0.89 mm) for translations and 2.89° (± 2.83°) for rotations. The implant variations required were in the range of ±3 mm and ±3° from the starting mechanical alignment. Discussion. In this study we showed that the developed tool has the potential to find an implant position that will restore native tibiofemoral kinematics in TKA. The proposed method might also be used with other alignment strategies, such as to optimize implant position towards native ligament strains. If native knee kinematics are restored, a more normal gait pattern can be achieved, which might result in improved patient satisfaction. The small changes required to achieve the dynamic alignment do not represent large modifications that might compromise implant survivorship. Conclusion. Patient-specific implant position predicted with MSM and ANN can restore native knee function in a post-TKA knee with a standard CR implant

Introduction. Recent studies have challenged the concept that a single ‘correct’ alignment to standardised anatomical references is the primary driver of TKA performance with regards to patient satisfaction outcomes. Patient specific variations in musculoskeletal anatomy are one explanation for this. Virtual simulated environments such as rigid body modelling allow for the impact of component alignment and variable patient specific musculoskeletal anatomy to be studied simultaneously. This study aims to determine if the output kinematics derived from consideration of both postoperative component alignment and patient specific musculoskeletal modelling has predictive potential of Patient Reported Outcomes. Method. Landmarking of key anatomical points and 3D registration of implants was performed on 96 segmented post-operative CT scans of TKAs. Both femoral and tibia implant components were registered. Acadaver rig validated platform for generating patient specific rigid body musculoskeletal models was used to assess the resultant motions and contact forces through a 0 to 140 degree deep knee bend cycle. Resultant kinematics were segmented and tested for differentiation with and correlation to a 12 month postoperative Knee injury and Osteoarthritis Outcome Score (KOOS). Results. Significant negative correlations (p<0.05) were found between the postoperative KOOS symptoms score and the rollback occurring in midflexion, quadriceps force in mid flexion, patella shear force and patella tilt at 90 degrees of flexion. A significant positive correlation was found between lateral shit of the patella through flexion and the symptoms score. (p<0.05) When segmenting those KOOS scores performing in the lowest 20% of patients, both rollback and the three patella measurements have statistically significantly different means (t test, p<0.05). There were other trends present that are discernible but do not have linear correlations, as they are cross-dependant on other kinematic factors or are not linear in nature. When segmenting the varus/valgus angular change into those with a varus angular change from extension to full flexion between 0 and 4 degrees (long leg axis, not implant to implant) and those with either further varus change or a valgus change, a statistically significant difference of 7 points (p<0.05) in the postoperative KOOS pain score is observed. Likewise, measured rollback of no more than 6mm without roll forward scored 10 points higher (p<0.05) in the postoperative KOOS score. These two parameters form a ‘kinematic safe zone' of outcomes in which the postoperative KOOS score is 12 points higher (p<0.05). Conclusions. The study showed statistically significant correlations between kinematic factors in a simulation of postoperative TKA and post-operative KOOS scores. The kinematic factors so captured are the result of both the variation in implant position and the subject specific, variable musculoskeletal anatomy. The presence of a ‘kinematic safe zone' in the data suggests a subject specific optimisation target for any given individual patient and the opportunity to preoperatively determine a subject specific implant position target

Introduction. Reverse Shoulder Arthroplasty (RSA) improves the mechanics of rotator cuff deficient shoulders. To optimize functional outcomes and minimize failures of the RSA manufacturers have recently made innovative design modifications with lateralized components. However, these innovations have their own set of biomechanical trade-offs, such as increased shear forces along the glenoid bone interface. The objective of this study was to develop an efficient musculoskeletal model to evaluate and compare both the muscle forces and joint reactive force of a normal shoulder to those implanted with varied RSA implant designs. We believe these findings will provide valuable insight into possible advantages or shortcomings of this new RSA design. Methods. A kinematic model of a normal shoulder joint was adapted from publically available musculoskeletal modeling software. Static optimizations then allowed for calculation of the individual muscle forces, moment arms and joint reactive forces relative to net joint moments. An accurate 3D computer models of humeral lateralized design (HLD) (Equinoxe, Exactech, Gainesville FL, USA), glenoid lateral design (GLD) (Encore, DJO Global, Vista CA, USA), and Grammont design (GD) (Aequalis, Tornier, Amsterdam, NV) reverse shoulder prostheses was also developed and parametric studies were performed based on the numerical simulation platform. Results. As expected, there were decreases in muscle forces in all RSA models (Table 1). These decreases were greatest in the middle deltoid of the HLD model for abduction and flexion (Figure 1) and in the rotator cuff muscles under both internal and external rotation (Figure 2). In all RSA models the muscle forces of the rotator cuff were diminished to near zero in all range of motions. The joint reactive forces in abduction and flexion decreased similarly for all RSA models compared to the normal shoulder model, with the greatest decrease again seen in the HLD model (Table 1). Conclusion. These findings demonstrate that the design characteristics implicit in these modified RSA prostheses result in kinematic differences most prominently seen in the deltoid muscle and overall joint reactive forces. These differences could have a profound effect on the ultimate clinical success and long term outcomes for RSA. These results can help guide continued optimization of RSA design and clinical outcomes. The developed innovative shoulder modeling simulation could serve as a prototype for testing of future implant design concepts

INTRODUCTION. The burden of Musculoskeletal (M-S) diseases and prosthetic revision operations is huge and increasing rapidly with the aging population. For patients that require a major surgical intervention, procedures are unsafe, uncertain in outcome and have a high complication rate. The goal of this project is to create an ICT-based patient-specific surgical navigation system that helps the surgeon safely reaching the optimal functional result for the patient and is a user friendly training facility for the surgeons. The purpose of this paper is to demonstrate the advancements in personalized musculoskeletal modeling for patients who require severe reconstructive surgery of the lower extremity. METHODS. TLEMsafe is a European Project dedicated to generating semi-automated 3-D image-analyzing tools to simulate the musculoskeletal (M-S) system. The patient-specific parameters are fed into models with which the patient specific functional outcome can be predicted. Hence, we can analyze the functional effect e.g. due to placement of prosthetic components in a patient. Surgeons can virtually operate on the patient-specific model after which the model predicts the functional effects. Once the optimal plan is selected, this is fed into a computer navigation system (see figure 1). RESULTS. The applicability of personalized musculoskeletal models is demonstrate in 10 healthy subjects: in the personalized models the muscle activation levels were much more physiological than those generated in scaled models. Hence, scaled models (as commonly used) were much less suitable to assess muscle forces and joint contact forces than personalized models. To enable usage of the personalized models for patient related issues we developed a Surgery Planning Environment 3D (SPE3D) (figure 1), which allows the surgeon to operate on the virtual patient. We have made personalized models of osteosarcoma patients and patients suffering from hip dysplasia (see figures 2 and 3). Currently we are comparing the functional predictions of the models to those occurring in these two patient categories. CONCLUSION. Considerable progress in patient specific modeling has been made. This capability in conjunction with a surgeon friendly virtual pre-planner has opened the way to quantify the functional outcome of severe musculoskeletal surgery in a clinically relevant manner. Significance. TLEMsafe aids the surgeon to preplan the surgery and optimize post-operative functional outcome for patients requiring reconstructive surgery for hip reconstruction or tumor surgery of the lower extremity. By using TLEMsafe the quality of the treatment can be improved

As treatments of knee osteoarthrosis are continually refined, increasingly sophisticated methods of evaluating their biomechanical function are required. Whilst TKA shows good preoperative pain relief and survivorship, functional outcomes are sub-optimal, and research focus has shifted towards their improvement. Restoration of physiological function is a common design goal that relies on clear, detailed descriptions of native biomechanics. Historical simplifications of true biomechanisms, for example sagittal plane approximation of knee kinematics, are becoming progressively less suitable for evaluation of new technologies. The patellar tendon moment arm (PTMA) is an example of such a metric of knee function that usefully informs design of knee arthroplasty but is not fully understood, in part due to limitations in its measurement. This research optimized PTMA measurement and identified the influence of knee size and sex on its variation. The PTMA about the instantaneous helical axis was calculated from optical tracked positional data. A fabricated knee model facilitated calculation optimization, comparing four data smoothing techniques (raw, Butterworth filtering, generalized cross-validated cubic spline-interpolation and combined filtering/interpolation). The PTMA was then measured for 24 fresh-frozen cadaveric knees, under physiologically based loading and extension rates. Sex differences in PTMA were assessed before and after size scaling. Large errors were measured for raw and interpolated-only techniques in the mid-range of extension, whilst both raw and filtered-only methods saw large inaccuracies at terminal extension and flexion. Combined filtering/interpolation enabled sub-mm PTMA calculation accuracy throughout the range of knee flexion, including at terminal extension/flexion (root-mean-squared error 0.2mm, max error 0.5mm) (Figure 1). Before scaling, mean PTMA throughout flexion was 46mm; mean, peak, and minimum PTMA values were larger in males, as was the PTMA at terminal flexion, the change in PTMA from terminal flexion to peak, and the change from peak to terminal extension (mean differences ranging from 5 to 10mm, p<0.05). Knee size was highly correlated with PTMA magnitude (r>0.8, p<0.001) (Figure 2). Scaling eliminated sex differences in PTMA magnitude, but peak PTMA occurred closer to terminal extension in females (female 15°, male 29°, p=0.01) (Figure 3). Improved measurement of the PTMA reveals previously undocumented characteristics that may help to improve the functional outcomes of knee arthroplasty. Knee size accounted for two-thirds of the variation in PTMA magnitude, but not the flexion angle at which peak PTMA occurred, which has implications for morphotype-specific arthroplasty and musculoskeletal models. The developed calculation framework is applicable both in vivo and vitro for accurate PTMA measurement and might be used to evaluate the relative performance of emerging technologies. For any figures or tables, please contact the authors directly

Introduction. Experimental testing reproducing activity specific joint-level loading has the potential to quantify structure-function relationships, evaluate intervention possibilities, perform device analysis, and quantify joint kinematics. Many recent technological advancements have been made in this field and inspire this study's aim to present a framework for the application of activity dependent tibiofemoral loading in a specific custom developed 6 degree of freedom (DOF) robotic test frame. This study demonstrates a pipeline wherein kinetic and kinematic data from subjects were collected in a gait lab, analyzed through musculoskeletal modeling techniques, and applied to cadaveric specimens in the robotic testing system in a real-time manner. This pipeline (Figure 1 blue dotted region) fits into a framework for synergistic development and refinement of arthroplasty techniques and devices. Methods. Gait lab kinetic and kinematic data for walking was collected from 5 subjects. Subject-specific musculoskeletal modeling was performed to determine 6 DOF active component joint loading (OpenSim version 2.4, . simtk.org. ). Kinetic profiles of the stance phase of gait were estimated and experimentally prescribed in a clinically relevant joint coordinate frame (as a function of time). Of note, knee flexion angle was the only kinematically applied DOF in the robotic testing system. Six fresh-frozen left cadaveric knee specimens (3 male, 3 female, age 49–70) were acquired. The specimens were rigidly secured to the robotic Universal Musculoskeletal Simulator (UMS) custom testing apparatus [1], which controlled joint loads with a real-time force feedback controller. Joint loads were scaled to 40% of predicted loads determined through modeling, because of system load capacity limitations and to prevent joint soft tissue damage potentially caused by additional loads without active muscle constraints. The loading profile for the walking activity was applied to each of the knees and the resulting kinematics were recorded. In addition, the force feedback controller performance was evaluated by calculating the root-mean-square (RMS) error between the desired and actual loads throughout these dynamic loading profiles. Results. Figure 2 shows representative graphs of the applied kinetics and resulting kinematics for gait stance phase. These display how the pipeline is able to utilize gait data to drive dynamic robotic testing conditions and elucidate high fidelity joint level kinematics. The RMS error in each of the load controlled degrees of freedom were 6.3 ± 2.8 N in lateral drawer, 17.0 ± 10.3 N in anterior drawer, 68.6 ± 23.8 N in distraction force, 1.02 ± 0.61 Nm in varus torque, and 0.33 ± 0.14 Nm in external rotation torque. Conclusions. The framework (Figure 1) for development of arthroplasty techniques and devices requires a robust data pipeline for handshaking between in vitro and in silico experiments. This study lays the foundation for future work and application of any desired activity dependent profiles, which may include statistically based “normal” loading (potentially specimen specific considering size, degradation, age, etc.). Further testing and analysis of simulation and experimental control parameters is required, but the pipeline demonstrates feasibility and promise

Introduction. Gait laboratory measurement of whole-body kinematics and ground reaction forces during a wide range of activities is frequently performed in joint replacement patient diagnosis, monitoring, and rehabilitation programs. These data are commonly processed in musculoskeletal modeling platforms such as OpenSim and Anybody to estimate muscle and joint reaction forces during activity. However, the processing required to obtain musculoskeletal estimates can be time consuming, requires significant expertise, and thus seriously limits the patient populations studied. Accordingly, the purpose of this study was to evaluate the potential of deep learning methods for estimating muscle and joint reaction forces over time given kinematic data, height, weight, and ground reaction forces for total knee replacement (TKR) patients performing activities of daily living (ADLs). Methods. 70 TKR patients were fitted with 32 reflective markers used to define anatomical landmarks for 3D motion capture. Patients were instructed to perform a range of tasks including gait, step-down and sit-to-stand. Gait was performed at a self-selected pace, step down from an 8” step height, and sit-to-stand using a chair height of 17”. Tasks were performed over a force platform while force data was collected at 2000 Hz and a 14 camera motion capture system collected at 100 Hz. The resulting data was processed in OpenSim to estimate joint reaction and muscle forces in the hip and knee using static optimization. The full set of data consisted of 135 instances from 70 patients with 63 sit-to-stands, 15 right-sided step downs, 14 left-sided step downs, and 43 gait sequences. Two classes of neural networks (NNs), a recurrent neural network (RNN) and temporal convolutional neural network (TCN), were trained to predict activity classification from joint angle, ground reaction force, and anthropometrics. The NNs were trained to predict muscle and joint reaction forces over time from the same input metrics. The 135 instances were split into 100 instances for training, 15 for validation, and 20 for testing. Results. The RNN and TCN yielded classification accuracies of 90% and 100% on the test set. Correlation coefficients between ground truth and predictions from the test set ranged from 0.81–0.95 for the RNN, depending on the activity. Predictions from both NNs were qualitatively assessed. Both NNs were able to effectively learn relationships between the input and output variables. Discussion. The objective of the study was to develop and evaluate deep learning methods for predicting patient mechanics from standard gait lab data. The resulting models classified activities with excellent performance, and showed promise for predicting exact values for loading metrics for a range of different activities. These results indicate potential for real-time prediction of musculoskeletal metrics with application in patient diagnostics and rehabilitation. For any figures or tables, please contact authors directly

Introduction. Malrotation of the tibial component would lead to various complications after total knee arthroplasty (TKA) such as improper joint kinematics, patellofemoral instability, or excessive wear of polyethylene. However, despite reports of internal rotation of the tibial component being associated with more severe pain or stiffness than external rotation, the biomechanical reasons remain largely unknown. In this study, we used a musculoskeletal computer model to simulate a squat (0°–130°–0° flexion) and analyzed the effects of malrotated tibial component on lateral and medial collateral ligament (LCL and MCL) tensions, tibiofemoral and patellofemoral contact stresses, during the weight-bearing deep knee flexion. Materials and Methods. A musculoskeletal model, replicating the dynamic quadriceps-driven weight-bearing knee flexion in previous cadaver studies, was simulated with a posterior cruciate-retaining TKA. The model included tibiofemoral and patellofemoral contact, passive soft tissue and active muscle elements. The soft tissues were modeled as nonlinear springs using previously reported stiffness parameters, and the bony attachments were also scaled to some cadaver reports. The neutral rotational alignment of the femoral and tibial components was aligned according to the femoral epicondylar axis and the tibial anteroposterior axis, respectively. Knee kinematics and ligament tensions were computed during a squat for malrotated conditions of the tibial component. The tibial rotational alignments were changed from 15° external rotation to 15° internal rotation in 5° increments. The MCL and LCL tensions, the tibiofemoral and patellofemoral contact stresses were compared among the knees with different rotational alignment. Results. For the MCL, the neutral rotated tibial components caused a maximum tension of 67.3 N. However, the 15° internally rotated tibial components increased tensions to 285.2N as a maximum tension [Fig.1]. By contrast, with external rotation of the tibial component, the MCL tensions increased only a small amount. The LCL tension also increased but up to less than half of the MCL value [Fig.2]. The tibiofemoral and patellofemoral contact stresses increased because of a decreased contact area [Fig.3]. Discussion and Conclusion: In this computer simulation, excessive internal rotation in the tibial component increased MCL tensions and patellofemoral and tibiofemoral contact stresses. The current study suggests that increased MCL tensions and patellofemoral and tibiofemoral contact stresses caused by a malrotated tibial component could be one cause of patient complaints and polyethylene problems after TKA

INTRODUCTION. The magnitude of principal strain is indicative of the risks of femoral fracture,. 1,2. while changes in femoral strain energy density (SED) after total hip arthroplasty (THA) have been associated with bone remodeling stimulus. 3. Although previous modeling studies have evaluated femoral strains in the intact and implanted femur under walking loads through successfully predicting physiological hip contact force and femoral muscle forces,. 1,2,3. strains during ‘high load’ activities of daily living have not typically been evaluated. Hence, the objective of this study was to compare femoral strain between the intact and the THA implanted femur under peak loads during simulated walking, stair descent, and stumbling. METHODS. CTs of three cadaveric specimens were used to develop finite element (FE) models of intact and implanted femurs. Implanted models included a commercially-available femoral stem (DePuy Synthes, Warsaw, IN, USA). Young's moduli of the composite bony materials were interpolated from Hounsfield units using a CT phantom and established relationships. 4. Peak hip contact force and femoral muscle forces during walking and stair descent were calculated using a lower extremity musculoskeletal model. 5. and applied to the femur FE models (Fig. 1). While maintaining the peak hip contact forces, muscle forces were further adjusted using an iterative optimization approach in FE models to reduce the femur deflection to the reported physiological range (< 5 mm). 2. Femoral muscle forces during stumbling were estimated utilizing the same optimization approach with literature-reported hip contact forces as input. 6. Maximum and minimum principal strains were calculated for each loading scenario. Changes in SED between intact and THA models were calculated in bony elements around the stem. RESULTS. As expected, high loads during stumbling resulted in the highest peak principal strains along femoral diaphysis (THA: 3179±523 and −4559±629 με; intact: 4232±818 and −5853±204 με) compared to stair descent and typically evaluated gait loads (THA: 1741±363 and −1893±76 με; intact: 2256±887 and −2509±493 με; Fig. 2). Principal strains in THA models peaked close to the tip of the femoral stem across three activities, compared with proximally located peak principal strains in the intact models (Fig. 2). Bony elements located medially and laterally to the femoral stem showed decreased SED after THA, while increased SED was observed in elements distal to the femoral stem (Fig. 3). DISCUSSION. Using appropriately distributed muscle forces, our model predicted similar peak principal strains and SED differences compared with reported values during walking (peak principal strains: ±1500 to ±2000 με. 1,2. ; SED differences: ± 0.02 MPa. 3. ). In addition to the close to failure level principal strains, stumbling showed the most noticeable changes in SED compared with the other two activities. Results suggest iterative bone remodeling simulations should include a composite of activities-of-daily-living loading conditions as well as appropriately distributed muscle forces. For any figures or tables, please contact authors directly

Musculoskeletal modeling techniques simulate reverse total shoulder arthroplasty (RTSA) shoulders and how implant placement affects muscle moment arms. Yet, studies have not taken into account how muscle-length changes affect force-generating capacity postoperatively. We develop a patient-specific model for RTSA patients to predict muscle activation. Patient-specific muscle parameters were estimated using an optimization scheme calibrating the model to isometric arm abduction data at 0°, 45°, and 90°. We compared predicted muscle activation to experimental electromyography recordings. A twelve-degree of freedom model with experimental measurements created patient-specific data estimating muscle parameters corresponding to strength. Optimization minimized the difference between measured and estimated joint moments and muscle activations, yielding parameters corresponding to subjects' strength that can predict muscle activation and lengths. Model calibration was performed on RTSA patients' arm abduction data. Predicted muscle activation ranged between 3% and 70% of maximum. The maximum joint moment produced was 10 Nm. The model replicated measured moments accurately (R. 2. > 0.99). The optimized muscle parameters produced feasible muscle moments and activations for dynamic arm abduction when using data from isometric force trials. A normalized correlation was found between predicted and experimental muscle activation for dynamic abduction (r > 0.9); the moment generation to lift the arm was tracked (R. 2. = 0.99). Statement of Clinical Significance: We developed a framework to predict patient-specific muscle parameters. Combined with patient-specific models incorporating joint configurations, kinematics, and bone anatomy, they can predict muscle activation in novel tasks and, e.g., predict how RTSA implant and surgical decisions may affect muscle function

Background. Clinical and anatomical complications from total knee replacement (TKR) procedures are debilitating, and include weakness, damage, and the loss of native anatomy. As the annual number of primary TKR surgeries in the United States has continued to rise, to a projected 3.48 million in 2030, there has been a concomitant rise in revision surgery. Damage to or loss of native knee anatomy as a result of TKR revision can leave the patient with irreversible knee dysfunction, which is a contra-indication for most TKR systems on the market. This leaves the multi-revision patient with limited medical options. Complete fusion of the joint, known as arthrodesis, is indicated in some cases. Arthrodesis is also commonly indicated for traumatic injury, bone loss, quadriceps extensor mechanism damage, and osteosarcoma. While this treatment may resolve pain and allow a patient to walk, the inability to flex the knee results in considerable functional complications. Patients with arthrodesis are unable to drive, sit in close-quarter spaces, or engage in a significant number of activities of daily living. Product Statement. The authors have developed and patented the Engage Knee System, a novel TKR system that allows a patient to lock and unlock the knee joint by means of a handheld, non-invasive device. An internal locking mechanism is constructed of materials that have been used in orthopedic joint replacements that have been approved through the FDA 510(k) process. A lightweight, handheld magnetic device is used to actuate the locking mechanism. No percutaneous components are required or present. This device allows a patient to lock their knee joint in full extension to ambulate with the functional equivalence of an arthrodesis, but allows a patient to unlock the device and bend the knee to engage in passive activities that would be otherwise difficult or impossible. The IP portfolio for this technology is owned by Clemson University, and they are seeking a partner/licensee to pursue further technology development and validation. Methods. A literature review of knee arthrodesis incidence and prevalence has been published by the inventors. Three- dimensional gait analysis was used to characterize rigid-knee gait kinematics and kinetics to verify potential implant design loads. Multiple physical prototypes of the design were created and implanted in Sawbones synthetic knee models, and a final prototype using industry-standard arthroplasty materials was contract-manufactured. Results. The Engage system is capable of locking and unlocking in full extension with the use of a non-invasive hand-held device. The device will support the loading patterns and magnitudes during stiff knee gait, as estimated through gait analysis and musculoskeletal modeling, when it is locked in full extension. Conclusion. The Engage Knee System bridges the gulf between existing treatments, and addresses not only patients who would otherwise undergo arthrodesis, but also patients who have avoided treatment or who currently undergo high-risk revision procedures. The device is also a viable option for arthrodesis takedown, providing patients who have already undergone arthrodesis a means of regaining knee flexion

Introduction. Many finite element (FE) studies have been performed in the past to assess the biomechanical performance of TKA and THA components. The boundary conditions have often been simplified to a few peak loads. With the availability of personalized musculoskeletal (MS) models we becomes possible to estimate dynamic muscle and prosthetic forces in a patient specific manner. By combining this knowledge with FE models, truly patient specific failure analyses can be performed. In this study we applied this combined technique to the femoral part of a cementless THR and calculated the cyclic micro-motions of the stem relative to the bone in order to assess the potential for bone ingrowth. Methods. An FE model of a complete femur with a CLS Spotorno stem inserted was generated. An ideal fit between the implant and the bone was modeled proximally, whereas distally an interface gap of 100μm was created to simulate a more realistic interface condition obtained during surgery. Furthermore, a gait analysis was performed on a young subject and fed into the Anybody™ MS modeling system. The anatomical data set (muscle attachment points) used by the Anybody™ system was morphed to the shape of the femoral reconstruction. In this way a set of muscle attachment points was obtained which was consistent with the FE model. The predicted muscle and hip contact forces by the Anybody™ modeling system were dynamic and divided into 37 increments including two stance phases and a swing phase of the right leg. Results. The magnitude and path of interface micromotions was heavily dependent on the location on the implant. In the proximal region, a unidirectional pattern was visible in proximal-distal direction (max. motion was 39μm). Mid stem micromotions were very small (in the order of 4μm), whereas in the distal region, micromotions had a tendency to develop in anterior-posterior and medial-lateral direction (max. motion was 96μm). Hence, in this example, ingrowth is most likely to start in the mid-region. Conclusion. By combining finite element models with musculoskeletal models more realistic, dynamical simulations can be generated to assess the biomechanical behavior of prosthetic components. Both, FE models as well as MS models can be personalized, which offers the possibility to perform truly patient specific predictions. Furthermore, by performing personalized MS and FE calculations, a database is established containing variability of kinematic, force and reconstructive parameters in patients. With this database new implants can be tested in a more robust and reliable manner than before, thereby reducing the chance that innovative ‘defective’ implants are launched on the market

Component alignment cannot fully explain total knee arthroplasty [TKA] performance with regards to patient reported outcomes and pain. Patient specific variations in musculoskeletal anatomy are one explanation for this. Computational simulations allow for the impact of component alignment and variable patient specific musculoskeletal anatomy on dynamics to be studied across populations. This study aims to determine if simulated dynamics correlate with Patient Reported Outcomes. Landmarking of key anatomical points and 3D registration of implants was performed on 96 segmented post-operative CT scans of TKAs. A cadaver rig validated platform for generating patient specific rigid body musculoskeletal models was used to assess the resultant motions. Resultant dynamics were segmented and tested for differentiation with and correlation to a 6 month postoperative Knee injury and Osteoarthritis Outcome Score (KOOS). Significant negative correlations were found between the postoperative KOOS symptoms score and the rollback occurring in midflexion (p<0.001), quadriceps force in mid flexion (p=0.025) and patella tilt throughout flexion (p=0.009, p=0.005, p=0.010 at 10°, 45° and 90° of flexion). A significant positive correlation was found between lateral shift of the patella through flexion and the symptoms score. (p=0.012) Combining a varus/valgus angular change from extension to full flexion between 0° and 4° (long leg axis) and measured rollback of no more than 6mm without roll forward forms a ‘kinematic safe zone’ of outcomes in which the postoperative KOOS score is 11.5 points higher (p=0.013). The study showed statistically significant correlations between kinematic factors in a simulation of postoperative TKR and post-operative KOOS scores. The presence of a ‘kinematic safe zone’ in the data suggests a patient specific optimisation target for any given individual patient and the opportunity to preoperatively determine a patient specific alignment target

Background. Though many advantages of reverse total shoulder arthroplasty (RTSA) have been demonstrated, a variety of complications indicate there is much to learn about how RTSA modifies normal shoulder function. This study assesses how RTSA affects deltoid muscle moment arms post-surgery using a subject-specific computational model driven by in vivo kinematic data. Methods. A subject-specific 12 degree-of-freedom (DOF) musculoskeletal model was used to analyze the shoulders of 26 subjects (14 RTSA, 12 Normal). The model was modified from the work of Holzbaur et al. to directly input 6 DOF humerus and scapula kinematics obtained using fluoroscopy. Results. The moment arm of the anterior, lateral and poster aspects of the deltoid was found to be significantly different when comparing RTSA and normal cohorts. Anterior and lateral deltoid moment arms were found to be larger at initial elevation. There was large inter-subject variability within the RTSA group. Conclusion. Placement of implant components during RTSA can directly affect the geometric relationship between the humerus and scapula and the muscle moment arms in the RTSA shoulder. RTSA shoulders maintain the same anterior and posterior deltoid muscle moment arm patterns as healthy shoulders, but they show much greater inter-subject variation and larger moment arm magnitudes. These observations provide a basis for determining optimal implant configuration and surgical placement to maximize RTSA function in a patient-specific manner

Introduction. A deep squat (DS) is a challenging motion at the level of the hip joint generating substantial reaction forces (HJRF). As a closed chain exercise, it has great value in rehabilitation and muscle strengthening of hip and knee. During DS, the hip flexion angle approximates the functional range of hip motion risking femoroacetabular impingement in some morphologies. In-vivo HJRF measurements have been limited to instrumented implants in a limited number of older patients performing incomplete squats (< 50° hip flexion and < 80° knee flexion). On the other hand, total hip arthroplasty is being increasingly performed in a younger and higher demanding patient population. These patients clearly have a different kinetical profile with hip and knee flexion ranges going well over 100 degrees. Since measurements of HJRF with instrumented prostheses in healthy subjects would be ethically unfeasible, this study aims to report a personalised numerical solution based on inverse dynamics to calculate realistic in-silico HJRF values during DS. Material and methods. Thirty-five healthy males (18–25 years old) were prospectively recruited for motion and morphological analysis. DS motion capture (MoCap) acquisitions and MRI scans with gait lab marker positions were obtained. The AnyBody Modelling System (v6.1.1) was used to implement a novel personalisation workflow of the AnyMoCap template model. Bone geometries, semi-automatically segmented from MRI, and corresponding markers were incorporated into the template human model by an automated procedure. A state of-the-art TLEM 2.0 dataset, included in the Anybody Managed Model Repository (v2.0), was used in the template model. The subject-specific MoCap trials were processed to compute kinematics of DS, muscle and joint reaction forces in the entire body. Resulting hip joint loads were compared with in-vivo data from OrthoLoad dataset. Additionally, hip and knee joint angles were computed. Results. An average HJRF of 274%BW (251.5 – 297.9%BW; 95% confidence interval) was calculated at the peak of DS. The HJRF on the pelvis was directed superior, medial and posterior throughout the DS. Peak knee and hip flexion angles were 112° (108.1° – 116.5°) and 107° (104.6° – 109.4°) on average. Discussion and conclusions. A comprehensive approach to construct an accurate personalised musculoskeletal model from subject-specific MoCap data, bone geometries, and palpatory landmarks was presented. Consistently higher HJR forces during DS in young adults were demonstrated as opposed to the Orthoload dataset. Similarly, knee and hip flexion angles were much higher, which could cause the increase in HJRF. It can be concluded that DS kinetics in young adults differ from the typical total hip arthroplasty population. These models will enable further in-silico joint biomechanics studies, and could serve the purpose of a virtual test bed for implant design