Learning Curves in Robotic Thyroid Surgery: A Systematic Review Comparing Transoral, Axillary, and Facelift Approaches

ImportanceThe learning curve for robotic thyroid surgery (RTS) remains incompletely characterized when considering the surgical approaches (axillary, transoral, transareolar, and facelift).ObjectiveThis systematic review investigated the learning curves and required case volumes for achieving proficiency according to the surgical approach.DesignPubMed, Scopus, and Cochrane Library systematic review using the PRISMA statements.SettingPublished clinical studies investigating learning curve outcomes related to the implementation of RTS.ParticipantsPractitioners.InterventionRTS.OutcomesLearning curve outcomes related to the implementation of RTS. The bias analysis was conducted with the MINORS.ResultsOf the 450 identified studies, 34 studies met our inclusion criteria (6438 patients). The mean minimum number of cases for achieving proficiency in axillary robotic partial and total thyroidectomies was 46 cases (range: 20-66) and 34 cases (range: 20-50), respectively. The minimum number of cases for reaching proficiency of transoral robotic and facelift approaches were 31 (15-55) and 32 (15-50) cases, respectively, with limited evidence for facelift approaches based on only 4 studies. Learning curves of facelift and transoral robotic approaches may be faster than the axillary one. While operative time significantly reduced in most approaches, the reduction of complication rates is inconsistent, with complications occurring in 16.7% of axillary, 11.1% of transoral, and 16.2% of facelift approaches throughout the learning process. There was substantial heterogeneity across studies for inclusion criteria, surgeon experience, and surgical outcomes.ConclusionThe number of cases required to achieve proficiency in robotic thyroid surgeries may depend on the surgical approach, with facelift and transoral approaches suggested as faster than axillary ones. Future prospective studies are needed to standardize learning definitions and analyze how surgeon-specific factors (experience, age, prior training) impact the learning curves.