| description abstract | Musculoskeletal (MS) models should be able to integrate patientspecific MS architecture and undergo thorough validation prior to their introduction into clinical practice. We present a methodology to develop subjectspecific models able to simultaneously predict muscle, ligament, and knee joint contact forces along with secondary knee kinematics. The MS architecture of a generic cadaverbased model was scaled using an advanced morphing technique to the subjectspecific 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 11degreeoffreedom (DOF) representation of the tibiofemoral (TF) and patellofemoral (PF) joints, which was solved using a combined inverse dynamic and quasistatic analysis, called forcedependent kinematics (FDK). TF joint forces for one gait and one rightturn trial and secondary knee kinematics for one unloaded legswing 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 rootmeansquare 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 rightturn 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 rightturn trial. Secondary knee kinematics from the unloaded legswing 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 subjectspecific scaling and personalization and does not contain any nonphysiological parameters. This modeling framework has potential applications in aiding the clinical decisionmaking in orthopedics procedures and as a tool for virtual implant design. | |
