VR-based automated suturing skill assessment in pediatric robotic surgery

PURPOSE: Objective assessment of robotic surgical skills is particularly important in pediatric surgery, where limited case volume restricts training opportunities. This study presents a virtual reality (VR)-based framework for automated evaluation of robotic suturing skills in a neonatal surgical scenario and investigates its agreement with expert video-based assessment. METHODS: A real-time VR simulator was developed to emulate neonatal robotic suturing with the SmartArm system. An automated skills assessment module was implemented using an 11-point subset of a validated 29-point suturing checklist. Each checklist item was reformulated into quantitative geometric and kinematic criteria directly extracted from the simulation . Ten suturing trials were recorded and independently evaluated by an expert pediatric surgeon using video review. Automated scores were compared with expert scores using accuracy, precision, recall, and F1-score. RESULTS: The simulator enabled stable real-time execution of robotic suturing tasks and deterministic extraction of performance metrics. The automated assessment achieved an accuracy of 67.3%, with a precision of 0.933, recall of 0.560, and F1-score of 0.700 relative to expert evaluation. Higher agreement was observed for clearly defined metrics, while discrepancies were primarily associated with criteria dependent on visual judgment in 2D video assessment. CONCLUSION: VR-based automated assessment of robotic pediatric suturing is feasible and provides objective, repeatable evaluation of performance. By translating clinically defined checklist items into measurable simulation-derived parameters, the proposed framework offers a scalable alternative to manual video-based skills assessment in robotic surgery training.