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

Abstract

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.

Artificial Intelligence in Surgery and Its Potential for Gastric Cancer

“…Subsequently, they reported that the accuracy of an AI model dividing the score made in JSES examination into three categories, mean -2, mean ±1, and mean +2 standard deviation was 75.0% [ 36 ]. They also focused on purse-string suture (at transanal endoscopic mesorectal excision) skill assessment and reported that AI skill scores were significantly correlated with length of suture time and surgeon experience [ 37 ]. In addition, a study that related surgical skill to the frequency of recognizing bleeding in the surgical field reported that the number of blood pixels recognized by an AI model was significantly lower in the group with higher tissue handling skill [ 38 ].…”