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

Abstract

Total knee replacement (TKR) is one of the most performed orthopedic surgeries to treat knee joint diseases in the elderly population. Although the survivorship of knee implants may extend beyond two decades, the poor outcome rate remains considerable. A recent computational approach used to better understand failure modes and improve TKR outcomes is based on the combination of musculoskeletal (MSK) and finite element models. This combined multiscale modeling approach is a promising strategy in the field of computational biomechanics; however, some critical aspects need to be investigated. In particular, the identification and quantification of the uncertainties related to the boundary conditions used as inputs to the finite element model due to a different definition of the MSK model are crucial. Therefore, the aim of this study is to investigate this problem, which is relevant for the model credibility assessment process. Three different generic MSK models available in the OpenSim platform were used to simulate gait, based on the experimental data from the fifth edition of the “Grand Challenge Competitions to Predict in vivo Knee Loads.” The outputs of the MSK analyses were compared in terms of relative kinematics of the knee implant components and joint reaction (JR) forces and moments acting on the tibial insert. Additionally, the estimated knee JRs were compared with those measured by the instrumented knee implant so that the “global goodness of fit” was quantified for each model. Our results indicated that the different kinematic definitions of the knee joint and the muscle model implemented in the different MSK models influenced both the motion and the load history of the artificial joint. This study demonstrates the importance of examining the influence of the model assumptions on the output results and represents the first step for future studies that will investigate how the uncertainties in the MSK models propagate on disease-specific finite element model results.

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

“…Finite element studies of knee protheses suggest that musculoskeletal models would affect the knee joint's kinematic and kinetic outcomes. 101 Shu et al proposed an FEM verified by in vivo fluoroscopy, pressure sensors, and EMG for preoperative testing of a newly invented knee prosthesis. 59 Fernando et al suggested that reducing the tibial prosthesis size could prevent excessive prosthesis internal rotation.…”

“…Further, finite element analysis can assist in designing and selecting knee prosthesis, guiding surgeons to optimize the operation in time. Finite element studies of knee protheses suggest that musculoskeletal models would affect the knee joint's kinematic and kinetic outcomes 101 . Shu et al .…”

A realistic upper-limb musculoskeletal model for overall muscle activation estimation

“…Tunable parameters are i) the bone inertial properties, ii) the skeletal morphologies, iii) the muscle architectures; all these features could allow for an improved inter-subject, healthy/impaired muscle activation evaluation in the future 21 . However, subject-specific models require complex modeling workflows 24,25 and well-defined methods, especially for musculotendon parameters estimation. For our study, given the nature of the recorded kinematics (all subjects using the same exoskeleton) and the collected information about the subjects (see following section), a fixed scaling was applied to match a generic model to all subjects, so that all subjects would have a common basis for the model outputs evaluations, based on their movement and their EMG recorded 26 .…”

Population-based analysis of knee joint loading in a knee osteoarthritis cohort: the impact of PCA-derived gait kinematic variations on estimated medial knee contact forces

“…However, this approach is costly, invasive, and not feasible in large cohorts. Thus, musculoskeletal (MSK) model-based simulations based on 3D motion capture (MoCap) data represent the state-of-the-art method to estimate in-vivo joint loading [24], [25]. Several groups have reported good agreement between MSK model-based and instrumented knee implant joint loading parameters [26], [27], hence making it a feasible alternative to instrumented prosthesis [26], [28] with the advantage of being able to be applied to larger clinicial cohorts.…”