Automated vision-based microsurgical skill analysis in neurosurgery using deep learning: Development and preclinical validation.

BACKGROUND Technical skill acquisition is an essential component of neurosurgical training. Educational theory suggests that optimal learning and improvement in performance depends on the provision of objective feedback. OBJECTIVE he aim of this study was to develop a vision-based framework based on a novel representation of surgical tool motion and interactions capable of automated and objective assessment of microsurgical skill. METHODS Videos were obtained from one expert, six intermediate and twelve novice surgeons performing arachnoid dissection in a validated clinical model using a standard operating microscope. A Mask Region Convolutional Neural Network (Mask RCNN) framework was used to segment the tools present within the operative field in a recorded video frame. Tool motion analysis was achieved using novel triangulation metrics. Performance of the framework in classifying skill levels was evaluated using the Area Under the Curve (AUC) and accuracy. Objective measures of classifying the surgeons' skill level were also compared using the Mann-Whitney U test, and a value of p < 0.05 was considered statistically significant. RESULTS The AUC was 0.977 and the accuracy was 84.21%. A number of differences were found, which included experts having a lower median dissector velocity (p=0.0004; 190.38 ms-1 vs 116.38 ms-1), and a smaller inter-tool tip distance (median 46.78 vs 75.92; p=0.0002) compared to novices. CONCLUSION Automated and objective analysis of microsurgery using a Mask RCNN and a novel tool motion and interaction representation is feasible, and may support technical skills training and assessment in neurosurgery.