Musculoskeletal (MS) models should be able to integrate patient-specific MS architecture and undergo thorough validation prior to their introduction into clinical practice. We present a methodology to develop subject-specific models able to simultaneously predict muscle, ligament, and knee joint contact forces along with secondary knee kinematics. The MS architecture of a generic cadaver-based model was scaled using an advanced morphing technique to the subject-specific morphology of a patient implanted with an instrumented total knee arthroplasty (TKA) available in the fifth “grand challenge competition to predict in vivo knee loads” dataset. We implemented two separate knee models, one employing traditional hinge constraints, which was solved using an inverse dynamics technique, and another one using an 11-degree-of-freedom (DOF) representation of the tibiofemoral (TF) and patellofemoral (PF) joints, which was solved using a combined inverse dynamic and quasi-static analysis, called force-dependent kinematics (FDK). TF joint forces for one gait and one right-turn trial and secondary knee kinematics for one unloaded leg-swing trial were predicted and evaluated using experimental data available in the grand challenge dataset. Total compressive TF contact forces were predicted by both hinge and FDK knee models with a root-mean-square error (RMSE) and a coefficient of determination (R²) smaller than 0.3 body weight (BW) and equal to 0.9 in the gait trial simulation and smaller than 0.4 BW and larger than 0.8 in the right-turn trial simulation, respectively. Total, medial, and lateral TF joint contact force predictions were highly similar, regardless of the type of knee model used. Medial (respectively lateral) TF forces were over- (respectively, under-) predicted with a magnitude error of M < 0.2 (respectively > −0.4) in the gait trial, and under- (respectively, over-) predicted with a magnitude error of M > −0.4 (respectively < 0.3) in the right-turn trial. Secondary knee kinematics from the unloaded leg-swing trial were overall better approximated using the FDK model (average Sprague and Geers' combined error C = 0.06) than when using a hinged knee model (C = 0.34). The proposed modeling approach allows detailed subject-specific scaling and personalization and does not contain any nonphysiological parameters. This modeling framework has potential applications in aiding the clinical decision-making in orthopedics procedures and as a tool for virtual implant design.

Goal: To provide an overview of control strategies in commercial and research microprocessor-controlled prosthetic knees (MPKs). Methods: Five commercially available MPKs described in patents, and five research MPKs reported in scientific literature were compared. Their working principles, intent recognition, and walking controller were analyzed. Speed and slope adaptability of the walking controller was considered as well. Results: Whereas commercial MPKs are mostly passive, i.e., do not inject energy in the system, and employ heuristic rule-based intent classifiers, research MPKs are all powered and often utilize machine learning algorithms for intention detection. Both commercial and research MPKs rely on finite state machine impedance controllers for walking. Yet while commercial MPKs require a prosthetist to adjust impedance settings, scientific research is focused on reducing the tunable parameter space and developing unified controllers, independent of subject anthropometrics, walking speed, and ground slope. Conclusion: The main challenges in the field of powered, active MPKs (A-MPKs) to boost commercial viability are first to demonstrate the benefit of A-MPKs compared to passive MPKs or mechanical non-microprocessor knees using biomechanical, performance-based and patient-reported metrics. Second, to evaluate control strategies and intent recognition in an uncontrolled environment, preferably outside the laboratory setting. And third, even though research MPKs favor sophisticated algorithms, to maintain the possibility of practical and comprehensible tuning of control parameters, considering optimal control cannot be known a priori. Significance: This review identifies main challenges in the development of A-MPKs, which have thus far hindered their broad availability on the market.

The thickness of the tibial polyethylene (PE) insert is a critical parameter to ensure optimal soft-tissue balancing in the intraoperative decision-making procedure of total knee arthroplasty (TKA). However, there is a paucity of information about the kinetic response to PE insert thickness variations in the tibiofemoral (TF) joint, and subsequently, the secondary effects on the patellofemoral (PF) biomechanics. Therefore, the purpose of this study was to investigate the influence of varying PE insert thickness on the ligament and TF compressive forces, as well as on the PF forces and kinematics, after a cruciate-retaining TKA. A previous patient-specific musculoskeletal model of TKA was adapted to simulate a chair-rising motion in which PE insert thickness was varied with 2 mm increments or decrements compared to the reference case (9 mm), from 5 mm up to 13 mm. Greater PE insert thickness resulted in higher ligament forces and concurrently increased the TF compressive force by 21% (13 mm), but slightly unloaded the PF joint with 7% (13 mm) while shifting the patella distally in the trochlear groove, compared to the reference case. Thinner PE inserts showed an opposite trend. Our findings suggest that the optimal PE insert thickness selection is a trade-off between the kinetic outcomes of the TF and PF joints.

Robotic-assisted total knee arthroplasty can attain highly accurate implantation. However, the target for optimal positioning of the components remains debatable. One of the proposed targets is to recreate the functional status of the pre-diseased knee. The aim of this study was to demonstrate the feasibility of reproducing the pre-diseased kinematics and strains of the ligaments and, subsequently, use that information to optimize the position of the femoral and tibial components. For this purpose, we segmented the pre-operative computed tomography of one patient with knee osteoarthritis using an image-based statistical shape model and built a patient-specific musculoskeletal model of the pre-diseased knee. This model was initially implanted with a cruciate-retaining total knee system according to mechanical alignment principles; and an optimization algorithm was then configured seeking the optimal position of the components that minimized the root-mean-square deviation between the pre-diseased and post-operative kinematics and/or ligament strains. With concurrent optimization for kinematics and ligament strains, we managed to reduce the deviations from 2.4 ± 1.4 mm (translations) and 2.7 ± 0.7° (rotations) with mechanical alignment to 1.1 ± 0.5 mm and 1.1 ± 0.6°, and the strains from 6.5% to lower than 3.2% over all the ligaments. These findings confirm that adjusting the implant position from the initial plan allows for a closer match with the pre-diseased biomechanical situation, which can be utilized to optimize the pre-planning of robotic-assisted surgery.

In adult patients with developmental hip dysplasia, a surgical procedure (triple innominate osteotomy) of the pelvic bone can be performed to rotate the acetabulum in the frontal plane, establishing better acetabular coverage. Although common clinical hip scores demonstrate significant improvements after surgery, they provide only overall information about function. The purpose of this study was to quantify the long-term outcome of triple innominate osteotomy in more detail using gait analyses and muscle strength measurements. We performed gait analyses at self-selected walking speed as well as isometric hip and knee muscle strength tests in twelve women who had undergone a unilateral triple innominate osteotomy (age: 34 ± 12 y, time post surgery: 80 ± 18 m). We compared the results to reference values obtained from eight healthy peers (age: 33 ± 10 y). The patients exhibited slight asymmetries in step length (smaller steps) and stance time (longer stance) as well as lower hip abduction moments in the operated limb in early stance compared to the non-operated limb. However, there were no differences in gait compared to healthy controls, even though the patients showed reduced bilateral hip abduction strength compared to controls. Our results indicate that the patients’ gait pattern had generally recovered very well, despite slight asymmetries in spatiotemporal parameters. Subtle deviations in hip abduction moments were observed during gait, whereas hip abduction strength was substantially reduced. Hence, the patients walked at a higher percentage of their maximal capacity. They may, therefore, be prone to fatigue and adopt compensatory gait strategies more quickly than healthy peers when walking long distances.

Purpose: To (1) investigate the hypothesis that there is high variability in the reported methods to derive axes and joint orientations from three‐dimensional (3D) bone models to (a) perform 3D knee‐related leg alignment analysis and (b) define coordinate systems for the femur, tibia and leg and (2) identify underlying principles that might lead to agreement on a universal 3D leg alignment analysis framework. Methods: A systematic review of the literature between January 2006 and June 2024 was performed. Articles explicitly reporting methods to derive axes and joint orientations from CT‐based 3D bone models for alignment parameters and/or coordinate systems of the femur, tibia and leg were included. Study characteristics and reported methods were extracted and presented as a qualitative synthesis. Results: A total of 93 studies were included. There was high variability in the reported methods to derive axes and joint orientations from 3D bone models. Nevertheless, the reported methods could be categorized into four groups, and several underlying principles of the four groups could be identified. Furthermore, the definitions of femoral and tibial coordinate systems were most frequently based on the mechanical axis (femoral, 13/19 [68%]; tibial, 13/26 [50%]) and a central medial‐lateral axis (femoral, 16/19 [84%]; tibial, 12/26 [46%]); no leg coordinate system was reported. Interestingly, of the included studies that reported on leg alignment parameters (76/93, 82%), only a minority reported expressing these in a complete coordinate system (25/76, 33%). Conclusion: There is high variability in 3D knee‐related leg alignment analysis. Therefore, universal 3D reference values for alignment parameters cannot yet be defined, and comparison of alignment parameter values between different studies is impossible. However, several underlying principles to the reported methods were identified, which could serve to reach more agreement on a future universal 3D framework for leg alignment analysis. Level of Evidence: Level I.