Deep learning for Evaluation and Prediction of TecHnical Skills in robotic-assisted vaginal cuff closure study

BACKGROUND: To support surgical education, an increasing focus has been on integrating surgical data, including surgical motion and activity and process understanding, to develop predictive models to assess surgical skills. OBJECTIVE: This study aimed to develop deep learning models based on fine-grained analysis to predict technical errors and generic surgical skills during robotic-assisted vaginal cuff closures as part of a hysterectomy. STUDY DESIGN: This was a multicenter prospective observational cohort study of robotic-assisted total hysterectomy performed between 2023 and 2025. Vaginal cuff closure video segments, recorded on the Touch Surgery video platform via the DS1 computer, were extracted and double-annotated by 2 trained surgeons: errors via Objective Clinical Human Reliability Analysis and global skill via Modifiable Global Evaluative Assessment of Robotic Skills. Of note, 3 deep learning pipelines were developed: 2 crucial surgical tasks (surgical video error detection via temporal modeling models and surgical skill assessment via few-shot surgical skill assessment) and multimodal learning. RESULTS: =0.85±0.02; mean absolute error=1.85±0.16). CONCLUSION: This proof of concept shows that deep learning can objectively score generic surgical skill and initial flag frame-level errors in vaginal cuff closure videos, aligning with validated objective assessment tools. Although larger, multicenter datasets remain essential, these results lay the groundwork for artificial intelligence-driven quality monitoring and evidence-based credentialing in minimally invasive gynecologic surgery.