Automatic purse-string suture skill assessment in transanal total mesorectal excision using deep learning-based video analysis

Background Purse-string suture in transanal total mesorectal excision is a key procedural step. The aims of this study were to develop an automatic skill assessment system for purse-string suture in transanal total mesorectal excision using deep learning and to evaluate the reliability of the score output from the proposed system. Methods Purse-string suturing extracted from consecutive transanal total mesorectal excision videos was manually scored using a performance rubric scale and computed into a deep learning model as training data. Deep learning-based image regression analysis was performed, and the purse-string suture skill scores predicted by the trained deep learning model (artificial intelligence score) were output as continuous variables. The outcomes of interest were the correlation, assessed using Spearman's rank correlation coefficient, between the artificial intelligence score and the manual score, purse-string suture time, and surgeon's experience. Results Forty-five videos obtained from five surgeons were evaluated. The mean(s.d.) total manual score was 9.2(2.7) points, the mean(s.d.) total artificial intelligence score was 10.2(3.9) points, and the mean(s.d.) absolute error between the artificial intelligence and manual scores was 0.42(0.39). Further, the artificial intelligence score significantly correlated with the purse-string suture time (correlation coefficient = -0.728) and surgeon's experience (P< 0.001). Conclusion An automatic purse-string suture skill assessment system using deep learning-based video analysis was shown to be feasible, and the results indicated that the artificial intelligence score was reliable. This application could be expanded to other endoscopic surgeries and procedures. The aims of this study were to develop an automatic skill assessment system for purse-string suture in transanal total mesorectal excision (TaTME) using artificial intelligence (AI) and to evaluate the reliability of the score output. The score output by AI exhibited statistically significant correlation with the manual score, purse-string suture time, and surgeon's experience. To the best of our knowledge, this is the first report on automatic skill assessment for purse-string suture in TaTME.