There is intrinsic surgical variability in the practice of total knee arthroplasty (TKA), and thus computational analyses of TKA should account for this variability to ensure clinical applicability and robustness of results. Statistical inputs within computational analyses have been used to assess the biomechanical characteristics of TKA implants [1], and such methodologies are promising when applied to morphological analysis of TKA in order to motivate component design, assess current designs, and improve the understanding of surgical outcomes. Analyses to date either directly use actual TKA component placement or bone resection data [2], or assume a single set of parameters for placement and resection across the entire specimen group that was investigated [3], and thus do not account for surgical variability. This could be due to a lack of available data to quantify clinical variability in TKA component placement.

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