Phase-Specific Analysis of Robotic-Assisted Knee Arthroplasty: Identifying Critical Learning Curves Across Three...

Robotic-assisted total and unicompartmental knee arthroplasties (RA-TKA, RA-UKA) are increasingly used to enhance surgical precision and improve clinical outcomes. Despite their advantages, these systems introduce a learning curve, particularly for surgeons adapting to different procedural phases. The current literature lacks detailed analyses of phase-specific contributions to the learning curve and how these differ among implant designs. This study aimed to identify critical phases within RA-TKA and RA-UKA that contributed most to operative efficiency and to propose tailored educational strategies for each device. We hypothesized that device-specific learning curves could notably impact procedural durations.

A total of 204 surgeries of RA-TKAs (bicruciate retaining [BCR] and stabilized [BCS]) and RA-UKAs were done using an image-free robotic platform by three experienced surgeons. Operative time was divided into eight procedural phases. Learning curves were evaluated for each device and phase using linear regression. The slopes of the regression lines were analyzed to quantify learning effects, with statistical significance assessed for each phase. Phase-specific contributions were interpreted based on the relative steepness of significant slopes.

The learning curve varied across devices and phases. The BCR exhibited the steepest learning curve, particularly in phase 5 (bone resection), whereas BCS demonstrated significant improvements in phase 5 as well, but to a lesser extent. The UKA showed notable learning effects in earlier phases, particularly in phase 3 (three-dimensional model creation). These findings highlighted device-specific differences in learning effects and underscored the critical importance of phase-specific training.

The present study identified device-specific and phase-specific differences in the learning curve for image-free robotic systems. Both BCR and BCS required focused training in later phases, such as bone resection, whereas UKA benefited from targeted improvements in earlier phases like model creation. These insights emphasized the need for tailored training protocols to optimize outcomes in robotic knee arthroplasty.