Abstract Mastering microsurgical skills is essential for neurosurgical trainees. Video-based analysis of target tissue changes and surgical instrument motion provides an objective, quantitative method for assessing microsurgical proficiency, potentially enhancing training and patient safety. This study evaluates the effectiveness of an artificial intelligence (AI)-based video analysis model in assessing microsurgical performance and examines the correlation between AI-derived parameters and specific surgical skill components. A dual AI framework was developed, integrating a semantic segmentation model for artificial blood vessel analysis with an instrument tip-tracking algorithm. These models quantified dynamic vessel area fluctuation, tissue deformation error count, instrument path distance, and normalized jerk index during a single-stitch end-to-side anastomosis task performed by 14 surgeons with varying experience levels. The AI-derived parameters were validated against traditional criteria-based rating scales assessing instrument handling, tissue respect, efficiency, suture handling, suturing technique, operation flow, and overall performance. Rating scale scores correlated with microsurgical experience, exhibiting a bimodal distribution that classified performance into good and poor groups. Video-based parameters showed strong correlations with various skill categories. Receiver operating characteristic analysis demonstrated that combining these parameters improved the discrimination of microsurgical performance. The proposed method effectively captures technical microsurgical skills and can assess performance.
Sugiyama et al. (Thu,) studied this question.