Does Resident Rotation Affect the Learning Curve of Active Robotic TKA? A Study of Surgical Efficiency and Radiographic Precision

<i>Background and Objectives</i>: Learning curves robotic arm-assisted total knee arthroplasty (TKA) are well-documented for semi-active systems, but evidence for advanced fully active robotic systems remains scarce. This study aimed to characterize the learning curve for operative time, implant positioning, and lower-limb alignment using a fully active robotic TKA system, specifically accounting for the impact of rotating resident involvement in a tertiary center. <i>Materials and Methods</i>: Sixty consecutive primary TKAs were performed using the advanced active robotic system (CUVIS-Joint^®). The learning curve for operative time was evaluated using cumulative summation (CUSUM) analysis. To identify independent predictors of surgical duration and radiographic precision, a multivariate linear regression model was constructed, including case number, implant type, and resident rotation period as variables. <i>Results</i>: CUSUM analysis identified a statistically significant inflection point at the 39th case. Beyond this point, mean operative time decreased approximately 20 min (133.3 ± 13.5 vs. 113.8 ± 7.9 min, <i>p</i> < 0.001). Multivariate regression confirmed that case number was the sole independent predictor of operative time (<i>p</i> < 0.001). Notably, implant positioning and lower-limb alignment showed no detectable difference across the sequential cases (<i>p</i> > 0.05), maintaining high precision from the outset. <i>Conclusions</i>: Active robotic TKA demonstrated a learning curve for operative time that stabilized after 39 cases within a clinical setting of rotational resident participation. Radiographic accuracy remained consistent despite these educational requirements, supporting the technical feasibility and reliability of this advanced system for the management of end-stage knee osteoarthritis.