A Patient-Specific Musculoskeletal Model of Total Knee Arthroplasty to Predict In Vivo Knee Biomechanics

Abstract

Musculoskeletal(MS) models are useful to gain information on in vivo biomechanics that would be otherwise very difficult to obtain.However, before entering the clinical routine MS models must be thoroughlyvalidated. This study presents a novel MS modelling framework capable ofintegrating the patient-specific MS architecture in a very detailed way, andsimultaneously simulating body level dynamics and secondary knee kinematics.The model predictions were further validated against publicly available in vivo experimental data. The bonegeometries were segmented from CT images of a patient with an instrumentedTotal Knee Arthroplasty (TKA) from the “Grand Challenge Competition to Predict In Vivo Knee Loads” dataset. These were inputtedinto an advanced morphing technique in order to scale the MS architecture of thenew TLEM 2.0 model1 to the specific patient. A detailed 11-DOF modelof the knee joint was constructed that included ligaments and rigid contact. Aninverse kinematic and a force-dependent kinematic technique2 wereutilized to simulate one gait cycle and one right-turn trial. Tibiofemoral (TF)joint contact force predictions were evaluated against experimental TF forcesrecorded by the TKA prosthesis, and secondary knee kinematics againstexperimental fluoroscopy data. The coefficientof determination and the root-mean-square error between predicted andexperimental tibiofemoral forces were larger than 0.9 and smaller than 0.3body-weights, respectively, for both gait and right-turn trials. Secondary kneekinematics were estimated with an average Sprague and Geers’ combined error assmall as 0.06. Themodelling strategy proposed permits a high level of patient-specificpersonalization and does not require any non-physiological parameter tuning.The very good agreement between predictions and experimental in vivo data is promising for the futureintroduction of the model into clinical applications