Identifying Sources of Error in Computer-Navigated Total Knee Arthroplasty Using Sensitivity Analyses in Knee Models.

Abstract

Long-term studies comparing computer-assisted total knee arthroplasty (TKA) to conventional techniques have not consistently demonstrated differences in outcomes. Errors may occur at various points of a navigated TKA, including surgeon variability in registering landmarks and when placing the cutting guide. The purpose of this study was to identify the sources of error during navigated TKA and to quantify their impact at each critical step of the procedure. Sawbones (n = 6) and three-dimensional-printed (n = 4) knee models were stabilized on a custom wooden platform. Landmarks were digitized with the navigation system, while a three-dimensional point capture system concurrently recorded identical points for precision assessment across multiple trials. Errors were quantified using transformation matrices (metric on SE(3)) to compare reference frames and resection planes. Sensitivity analyses using Monte Carlo simulations were performed to quantify the magnitude of specific errors in the navigation output. The largest registration errors occurred at the hip center (8.2 ± 4.4 mm) and lateral tibial plateau (4.8 ± 3.7 mm), while the femur center exhibited the least variability (0.8 ± 0.5 mm). The metric on SE(3) analysis revealed that errors in femoral and tibial center landmarks significantly influenced bone reference frames. No clear correlation was found between bone reference frame errors and cut plane errors. Error clouds highlighted compact femoral errors (average 1.3 mm), but broader tibial errors (average 5.8 mm). Sensitivity analyses confirmed the femoral center as the most critical landmark for registration accuracy. In computer-navigated TKA, landmarks closer to the knee joint demonstrated narrower safe zones and had the greatest impact on system accuracy. The lack of correlation between bone reference frame errors and cut plane errors suggests additional error sources. Improved intraoperative strategies, such as advanced imaging, refined algorithms, and enhanced training, are needed to optimize critical landmark registration and improve TKA outcomes.