Artificial intelligence assessment of tissue-dissection efficiency in laparoscopic colorectal surgery

Purpose Several surgical-skill assessment tools emphasize the importance of efficient tissue-dissection, whose assessment relies on human judgment and is thus subject to bias. Automated assessment may help solve this problem. This study aimed to verify the feasibility of surgical-skill assessment using a deep learning-based recognition model. Methods This retrospective study used multicenter intraoperative videos of laparoscopic colorectal surgery (sigmoidectomy or high anterior resection) for colorectal cancer obtained from 766 cases across Japan. Three groups with different skill levels were distinguished: high-, intermediate-, and low-skill. We developed a model to recognize tissue dissection by the monopolar device using deep learning-based computer-vision technology. Tissue-dissection time per monopolar device appearance time (efficient-dissection time ratio) was extracted as a quantitative parameter describing efficient dissection. We automatically measured the efficient-dissection time ratio using the recognition model of 8 surgical instruments and tissue-dissection on/off classification model. The efficient-dissection time ratio was compared among groups; the feasibility of distinguishing them was explored using the model. The model-calculated parameters were evaluated to determine whether they could differentiate high-, intermediate-, and low-skill groups. Results The tissue-dissection recognition model had an overall accuracy of 0.91. There was a moderate correlation (0.542; 95% confidence interval, 0.288-0.724; P < 0.001) between manually and automatically measured efficient-dissection time ratios. Efficient-dissection time ratios by this model were significantly higher in the high-skill than in intermediate-skill (P = 0.0081) and low-skill (P = 0.0249) groups. Conclusion An automated efficient-dissection assessment model using a monopolar device was constructed with a feasible automated skill-assessment method.