Using Musculoskeletal Models to Estimate in vivo Total Knee Replacement Kinematics and Loads: Effect of Differences Between Models

On the consequences of intra-operative release versus over-tensioning of the posterior cruciate ligament in total knee arthroplasty

Intra-operative tensioning of the posterior cruciate ligament (PCL) in total knee arthroplasty (TKA) is commonly based on the surgeon's experience, resulting in a possibly loose or overly tight PCL. To date, the consequences of different PCL tensioning scenarios for the post-operative biomechanics of the knee remain unclear. Using a comprehensive musculoskeletal modelling approach that allows predictive joint kinematic and kinetic balance, we assessed variations in the movement and loading patterns of the knee as well as changes in ligament and muscle forces during walking in response to systematic variations in the PCL reference strain. The results indicate only small differences in the tibiofemoral and patellofemoral kinematics and kinetics for scenarios involving up to 10% release of the PCL (relative to the baseline reference scenario with 2% residual strain). These observations remain valid for simulations performed with high- as well as with low-conformity implant designs. However, over-tensioning of the ligament was found to considerably overload the tibiofemoral joint, including altered contact mechanics, and may therefore shorten the implant longevity. Finally, no meaningful impact of the PCL reference strain on the muscle force patterns was observed. This study therefore favours balancing the knee with a slightly loose rather than tense PCL, if appropriate intra-operative PCL tension cannot be objectively achieved.

Multibody dynamics-based musculoskeletal modeling for gait analysis: a systematic review

Beyond qualitative assessment, gait analysis involves the quantitative evaluation of various parameters such as joint kinematics, spatiotemporal metrics, external forces, and muscle activation patterns and forces. Utilizing multibody dynamics-based musculoskeletal (MSK) modeling provides a time and cost-effective non-invasive tool for the prediction of internal joint and muscle forces. Recent advancements in the development of biofidelic MSK models have facilitated their integration into clinical decision-making processes, including quantitative diagnostics, functional assessment of prosthesis and implants, and devising data-driven gait rehabilitation protocols. Through an extensive search and meta-analysis of over 116 studies, this PRISMA-based systematic review provides a comprehensive overview of different existing multibody MSK modeling platforms, including generic templates, methods for personalization to individual subjects, and the solutions used to address statically indeterminate problems. Additionally, it summarizes post-processing techniques and the practical applications of MSK modeling tools. In the field of biomechanics, MSK modeling provides an indispensable tool for simulating and understanding human movement dynamics. However, limitations which remain elusive include the absence of MSK modeling templates based on female anatomy underscores the need for further advancements in this area.

AST: An OpenSim-based tool for the automatic scaling of generic musculoskeletal models

BACKGROUND AND OBJECTIVES

The paper introduces a tool called Automatic Scaling Tool (AST) designed for improving and expediting musculoskeletal (MSK) simulations based on generic models in OpenSim. Scaling is a crucial initial step in MSK analyses, involving the correction of virtual marker locations on a model to align with actual experimental markers.

METHODS

The AST automates this process by iteratively adjusting virtual markers using scaling and inverse kinematics on a static trial. It evaluates the root mean square error (RMSE) and maximum marker error, implementing corrective actions until achieving the desired accuracy level. The tool determines whether to scale a segment with a marker-based or constant scaling factor based on checks on RMSE and segment scaling factors.

RESULTS

Testing on three generic MSK models demonstrated that the AST significantly outperformed manual scaling by an expert operator. The RMSE for static trials was one order of magnitude lower, and for gait tasks, it was five times lower (8.5 ± 0.76 mm vs. 44.5 ± 7.5 mm). The AST consistently achieved the desired level of accuracy in less than 100 iterations, providing reliable scaled MSK models within a relatively brief timeframe, ranging from minutes to hours depending on model complexity.

CONCLUSIONS

The paper concludes that AST can greatly benefit the biomechanical community by quickly and accurately scaling generic models, a critical first step in MSK analyses. Further validation through additional experimental datasets and generic models is proposed for future tests.

Explainable AI Elucidates Musculoskeletal Biomechanics: A Case Study Using Wrist Surgeries

As datasets increase in size and complexity, biomechanists have turned to artificial intelligence (AI) to aid their analyses. This paper explores how explainable AI (XAI) can enhance the interpretability of biomechanics data derived from musculoskeletal simulations. We use machine learning to classify the simulated lateral pinch data as belonging to models with healthy or one of two types of surgically altered wrists. This simulation-based classification task is analogous to using biomechanical movement and force data to clinically diagnose a pathological state. The XAI describes which musculoskeletal features best explain the classifications and, in turn, the pathological states, at both the local (individual decision) level and global (entire algorithm) level. We demonstrate that these descriptions agree with assessments in the literature and additionally identify the blind spots that can be missed with traditional statistical techniques.

Use of patellofemoral digital twins for patellar tracking and treatment prediction: comparison of 3D models and contact detection algorithms

Introduction: Poor patellar tracking can result in painful contact pressures, patella subluxation, or dislocation. The use of musculoskeletal models and simulations in orthopedic surgeries allows for objective predictions of post-treatment function, empowering clinicians to explore diverse treatment options for patients. Although a promising approach for managing knee surgeries, the high computational cost of the Finite Element Method hampers its clinical usability. In anticipation of minimal elastic deformations in the involved bodies, the exploration of the Multibody Dynamics approach emerged as a viable solution, providing a computationally efficient methodology to address clinical concerns related to the knee joint. Methods: This work, with a focus on high-performance computing, achieved the simulation of the patellofemoral joint through rigid-body multibody dynamics formulations. A comparison was made between two collision detection algorithms employed in the simulation of contact between the patellar and femoral implants: a generic mesh-to-mesh collision detection algorithm, which identifies potential collisions between bodies by checking for proximity or overlap between their discretized mesh surface elements, and an analytical contact algorithm, which uses a mathematical model to provide closed-form solutions for specific contact problems, but cannot handle arbitrary geometries. In addition, different digital twins (3D model geometries) of the femoral implant were compared. Results: Computational efficiency was considered, and histories of position, orientation, and contact force of the patella during the motion were compared with experimental measurements obtained from a sensorized 3D-printed test bench under pathological and treatment scenarios. The best results were achieved through a purely analytical contact detection algorithm, allowing for clinical usability and optimization of clinical outcomes.

Model Properties and Clinical Application in the Finite Element Analysis of Knee Joint: A Review

The knee is the most complex joint in the human body, including bony structures like the femur, tibia, fibula, and patella, and soft tissues like menisci, ligaments, muscles, and tendons. Complex anatomical structures of the knee joint make it difficult to conduct precise biomechanical research and explore the mechanism of movement and injury. The finite element model (FEM), as an important engineering analysis technique, has been widely used in many fields of bioengineering research. The FEM has advantages in the biomechanical analysis of objects with complex structures. Researchers can use this technology to construct a human knee joint model and perform biomechanical analysis on it. At the same time, finite element analysis can effectively evaluate variables such as stress, strain, displacement, and rotation, helping to predict injury mechanisms and optimize surgical techniques, which make up for the shortcomings of traditional biomechanics experimental research. However, few papers introduce what material properties should be selected for each anatomic structure of knee FEM to meet different research purposes. Based on previous finite element studies of the knee joint, this paper summarizes various modeling strategies and applications, serving as a reference for constructing knee joint models and research design.

Multi-Action Knee Contact Force Prediction by Domain Adaptation

Most recent musculoskeletal dynamics estimation methods are designed for predefined actions, such as gait, and don't generalize to various tasks. In this work, we address the problem of estimating internal biomechanical forces during more than one actions by introducing unsupervised domain adaptation into a deep learning model. More specifically, we developed a Bidirectional Long Short-Term Memory network for knee contact force prediction, enhanced with correlation alignment layers, in order to minimize the domain shift between kinematic data from different actions. Furthermore, we used the novel Neural State Machine (NSM) as a simulation platform to test and visualize our model predictions in a wide range of trajectories adapted to different 3D scene geometries in real-time. We conducted multiple experiments, including comparison with previous models, model alignment across action classes and real-to-synthetic data alignment. The results showed that the proposed deep learning architecture with domain adaptation performs better than the benchmark in terms of NRMSE and t-test. Overall, our method is capable of predicting knee contact forces for more than one action classes using a single architecture and thereby opens the path for estimating internal forces for intermediate actions, while the knowledge of the hidden state of motion may be used to support personalized rehabilitation. Moreover, our model can be easily integrated into any human motion simulation environment, which shows its potential in enabling biomechanical analysis in an automated and computationally efficient way.

Comparing three generic musculoskeletal models to estimate the tibiofemoral reaction forces during gait and sit-to-stand tasks

The choice of musculoskeletal (MSK) model is crucial for performing MSK estimations to evaluate muscle demands and joint forces. This study compared two previously published generic MSK models and a modified model to estimate tibiofemoral reaction forces (TFRF) during gait, sit-to-stand, and stand-to-sit. The estimated tibiofemoral reaction forces were compared with an in vivo dataset from six patients using an instrumented knee prosthesis. A correlation and root mean square error (RMSE) in the time-series analysis and relative peak error (RPE) were evaluated. The results showed that the three MSK models were similar in estimating the vertical forces, with a large correlation, and RPE was found around 20 % during gait. The RMSE and the RPE indicated that the modified model had lower total and lateral compartment forces errors for sit-to-stand and stand-to-sit, showing the best performance. The shear forces for all tasks and models showed significant errors. Future MSK studies should consider these findings when researching functional tasks. The modified model was found to be more effective in estimating the vertical tibiofemoral joint reaction forces in tasks that impose greater demands on muscle forces and require high knee and hip flexion.

The force-length relation of the young adult human tibialis anterior

Background

Knowledge of the muscle's lengths at which maximum active isometric force is attained is important for predicting forces during movement. However, there is limited information about the in vivo force-length properties of a human muscle that plays crucial roles during locomotion; the tibialis anterior (TA). We therefore aimed to estimate TA's force-length relation from dorsiflexor torque-angle curves constructed from eight women and eight men.

Methods

Participants performed maximal voluntary fixed-end contractions with their right ankle dorsiflexors from 0° to 30° plantar flexion. Muscle fascicle lengths were estimated from B-mode ultrasound images, and net ankle joint torques were measured using dynamometry. Fascicle forces were estimated by dividing maximal active torques by literature-derived, angle-specific tendon moment arm lengths while assuming a fixed 50% force contribution of TA to the total dorsiflexor force and accounting for fascicle angles.

Results

Maximal active torques were higher at 15° than 20° and 30° plantar flexion (2.4-6.4 Nm, p ≤ 0.012), whereas maximal active TA fascicle forces were higher at 15° than 0°, 20° and 30° plantar flexion (25-61 N, p ≤ 0.042), but not different between 15° and 10° plantar flexion (15 N, p = 0.277). TA fascicle shortening magnitudes during fixed-end contractions were larger at 15° than 30° plantar flexion (3.9 mm, p = 0.012), but less at 15° than 0° plantar flexion (-2.4 mm, p = 0.001), with no significant differences (≤0.7 mm, p = 0.871) between TA's superficial and deep muscle compartments. Series elastic element stiffness was lowest and highest at lengths 5% shorter and 5% longer than optimum fascicle length, respectively (-30 and 15 N/mm, p ≤ 0.003).

Discussion

TA produced its maximum active force at 10-15° plantar flexion, and its normalized force-length relation had ascending and descending limbs that agreed with a simple scaled sarcomere model when active fascicle lengths from within TA's superficial or deep muscle compartment were considered. These findings can be used to inform the properties of the contractile and series elastic elements of Hill-type muscle models.

Optimization Reduces Knee-Joint Forces During Walking and Squatting: Validating the Inverse Dynamics Approach for Full Body Movements on Instrumented Knee Prostheses

Because of the redundancy of our motor system, movements can be performed in many ways. While multiple motor control strategies can all lead to the desired behavior, they result in different joint and muscle forces. This creates opportunities to explore this redundancy, for example, for pain avoidance or reducing the risk of further injury. To assess the effect of different motor control optimization strategies, a direct measurement of muscle and joint forces is desirable, but problematic for medical and ethical reasons. Computational modeling might provide a solution by calculating approximations of these forces. In this study, we used a full-body computational musculoskeletal model to (a) predict forces measured in knee prostheses during walking and squatting and (b) study the effect of different motor control strategies (i.e., minimizing joint force vs. muscle activation) on the joint load and prediction error. We found that musculoskeletal models can accurately predict knee joint forces with a root mean squared error of <0.5 body weight (BW) in the superior direction and about 0.1 BW in the medial and anterior directions. Generally, minimization of joint forces produced the best predictions. Furthermore, minimizing muscle activation resulted in maximum knee forces of about 4 BW for walking and 2.5 BW for squatting. Minimizing joint forces resulted in maximum knee forces of 2.25 BW and 2.12 BW, that is, a reduction of 44% and 15%, respectively. Thus, changing the muscular coordination strategy can strongly affect knee joint forces. Patients with a knee prosthesis may adapt their neuromuscular activation to reduce joint forces during locomotion.