Aseptic loosening is the leading cause of total knee arthroplasty (TKA) failure in the long term, of which osteolysis from polyethylene wear debris remains a problem that can limit the lifetime of TKA past the second decade. To help speed up design innovations, our goal was to develop a computational framework that could efficiently predict the effect of many sources of variability on TKA wear—including design, surgical, and patient variability. We developed a computational framework for predicting TKA contact mechanics and wear. The framework accepts multiple forms of input data: patient-specific, population-specific, or standardized motions and forces. CAD models are used to create the FEA mesh. An analytical wear model, calibrated from materials testing (wheel-on-flat) experiments, is fully integrated into the FEA process. Isight execution engine runs a design of experiments (DOE) analysis with an outcome variable, such as volumetric wear, to guide statistical model output. We report two DOE applications to test the utility of the computational framework for performing large variable studies in an efficient manner: one to test the sensitivity of TKA wear to the femoral center of rotation, and the second to test the sensitivity of TKA wear to gait input perturbations.Background
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