Learning Curve and Evaluation of Robotic Assistance for Cochlear Implant Electrode Insertion

OBJECTIVE: Robotic assistance has been introduced to improve precision and reduce intracochlear trauma during cochlear implant electrode insertion. This study evaluates the learning curve associated with robotic assistance systems and assesses objective and subjective performance metrics during early clinical implementation. STUDY DESIGN: Prospective observational cohort study. SETTING: Tertiary referral center. PARTICIPANTS: Three surgeons with varying levels of experience performing robotic-assisted cochlear implantation. INTERVENTIONS: Robotic-assisted cochlear implant electrode insertion after a structured training program. MAIN OUTCOME MEASURES: Robot usage time, preparation time, insertion time, total operation time, and intraoperative workload assessed using the SURG-TLX questionnaire. Learning curves were evaluated using sequential group comparisons, CUSUM analysis, and nonlinear exponential decay modelling. Model fit was compared with linear alternatives. RESULTS: Sixty consecutive robotic-assisted insertions were analyzed. Robot usage time decreased from 19.1 minutes (cases 1-20) to 15.0 minutes (cases 41-60) (P=0.020). Preparation time decreased from 12.5 to 9.2 minutes (P=0.012), with the largest improvement in the first 20 cases. Exponential decay modeling demonstrated a nonlinear learning pattern with an estimated asymptotic robot usage time of 16.3 minutes (τ=1.65 procedures), outperforming linear regression based on AIC/BIC model comparison. Insertion time and total operation time did not differ significantly between groups. CUSUM analysis suggested group-level stabilization of robot usage time within 20-30 cases. SURG-TLX showed a significant reduction in situational stress between cases 21-40 and 41-60 (P=0.031). CONCLUSION: Robot-assisted cochlear implant electrode insertion shows a distinct learning curve primarily driven by adjustment in robotic setup and handling. Procedural efficiency improves rapidly, with early stabilization at the individual level and later stabilization at the aggregated level over all surgeons. Structured training and protocolized workflow support safe clinical adoption.