SurgSmart: an artificial intelligent system for quality control in laparoscopic cholecystectomy: an observational study
Shangdi Wu, Zixin Chen, Runwen Liu, Ang Li, Yu Cao, Ailin Wei, Qingyu Liu, Jie Liu, Yuxian Wang, Jingwen Jiang, Zhiye Ying, Jingjing An, Bing Peng, Xin Wang
Abstract
BACKGROUND: The rate of bile duct injury in laparoscopic cholecystectomy (LC) continues to be high due to low critical view of safety (CVS) achievement and the absence of an effective quality control system. The development of an intelligent system enables the automatic quality control of LC surgery and, eventually, the mitigation of bile duct injury. This study aims to develop an intelligent surgical quality control system for LC and using the system to evaluate LC videos and investigate factors associated with CVS achievement. MATERIALS AND METHODS: SurgSmart, an intelligent system capable of recognizing surgical phases, disease severity, critical division action, and CVS automatically, was developed using training datasets. SurgSmart was also applied in another multicenter dataset to validate its application and investigate factors associated with CVS achievement. RESULTS: SurgSmart performed well in all models, with the critical division action model achieving the highest overall accuracy (98.49%), followed by the disease severity model (95.45%) and surgical phases model (88.61%). CVSI, CVSII, and CVSIII had an accuracy of 80.64, 97.62, and 78.87%, respectively. CVS was achieved in 4.33% in the system application dataset. In addition, the analysis indicated that surgeons at a higher hospital level had a higher CVS achievement rate. However, there was still considerable variation in CVS achievement among surgeons in the same hospital. CONCLUSIONS: SurgSmart, the surgical quality control system, performed admirably in our study. In addition, the system's initial application demonstrated its broad potential for use in surgical quality control.