Development, application and evaluation of an artificial intelligence (AI)-based platform (SurgSmart) for the automatic assessment of the critical view of safety (CVS) in laparoscopic cholecystectomy (LC)

Ming Tang; Ran Hu; Dian Qin; Jie Liu; Junyu Mi; Long Liang; Linxun Liu; Chunrong Wang; Yong Xiong; Wanli Wang; Yichuan Li; Yunqiang Cai; He Cai; Kan Wang; Zhong Wu; Pan Gao; Yongbin Li; Jingwen Jiang; Ang Li; Bing Peng; Xin Wang

doi:10.1007/s00464-026-12658-z

Development, application and evaluation of an artificial intelligence (AI)-based platform (SurgSmart) for the automatic assessment of the critical view of safety (CVS) in laparoscopic cholecystectomy (LC)

Surg Endosc. 2026 Mar 6. doi: 10.1007/s00464-026-12658-z. Online ahead of print.

Authors

Ming Tang^#^{1

2}, Ran Hu^#^{1

2}, Dian Qin³, Jie Liu³, Junyu Mi³, Long Liang³, Linxun Liu⁴, Chunrong Wang⁵, Yong Xiong⁶, Wanli Wang⁷, Yichuan Li⁸, Yunqiang Cai^{1

2}, He Cai^{1

2}, Kan Wang^{1

2}, Zhong Wu^{1

2}, Pan Gao^{1

2}, Yongbin Li^{1

2}, Jingwen Jiang^{1

9

10}, Ang Li^{1

2}, Bing Peng^{1

2}, Xin Wang^{11

12}

Affiliations

¹ Division of Pancreatic Surgery, Department of General Surgery, West China Hospital of Sichuan University, No.37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China.
² West China School of Medicine, West China Hospital of Sichuan University, Chengdu, China.
³ Chengdu Withai Innovations Technology Company, Chengdu, China.
⁴ Department of General Surgery, Qinghai Provincial People's Hospital, Xining, China.
⁵ Department of Hepatobiliary Surgery, Xuanhan People's Hospital, Dazhou, Sichuan, China.
⁶ Hepatobiliary Department, Panzhihua Central Hospital, Panzhihua, Sichuan Province, China.
⁷ Department of Hepatobiliary, Pancreatic, Splenic and Vascular Surgery, Bazhong Central Hospital, Bazhong, China.
⁸ Hepatobiliary and Pancreatic Surgery, Guang'an People's Hospital, Guang'an, Sichuan Province, China.
⁹ West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
¹⁰ Med-X Center for Informatics, Sichuan University, Chengdu, China.
¹¹ Division of Pancreatic Surgery, Department of General Surgery, West China Hospital of Sichuan University, No.37, Guoxue Alley, Chengdu, 610041, Sichuan Province, China. hxwangxin2012@hotmail.com.
¹² West China School of Medicine, West China Hospital of Sichuan University, Chengdu, China. hxwangxin2012@hotmail.com.

^# Contributed equally.

PMID: 41792491
DOI: 10.1007/s00464-026-12658-z

Abstract

Background: Laparoscopic Cholecystectomy (LC) is the standard surgical treatment for symptomatic benign gallbladder diseases. Bile Duct Injury (BDI) is a common and serious complication of LC. Critical View of Safety (CVS) has been proven crucial in preventing BDI. However, current attainment rate of CVS remains low. Recent advancements in AI offer an efficient method for this gap. This study aims to develop an intelligent surgical platform (SurgSmart) enabling real-time assessment of the Critical View of Safety (CVS), integrate it into routine surgical practice, and evaluate its performance and user acceptance based on intraoperative and post-operative feedback.

Materials and methods: A total of 377 LC videos from 17 hospitals were retrospectively collected for training, validation, and testing of the AI algorithm. The model's effectiveness was evaluated using accuracy, precision, recall, F1-score, and macro-average F1-score. Our platform was deployed in the operating rooms of three hospitals. From May to October 2024, we collected LC videos and surgical reports and assessing variations in CVS scores. Surgeons were surveyed to evaluate user satisfaction, surgical confidence and to gather suggestions.

Results: For CVS I, II, and III the overall accuracy and macro-average F1-score are 0.91 and 0.72, 0.86 and 0.67, 0.73 and 0.70, respectively. The overall CVS scores for the three hospitals showed significant improvement after the deployment of platform (P < 0.01). Fifteen out of the eighteen surgeons who used our platform demonstrated overall improvement (P < 0.05). Surgeons' satisfaction was high, with recommendations including more adequate training and guidance as well as further improvements in model performance.

Conclusion: This platform has demonstrated its feasibility for real-time and automated CVS assessment. Most surgeons improved after using our platform. Surgeons reported positive feedback and expressed hope for more adequate guidance and continuous improvements in model performance.

Keywords: Artificial intelligence; Clinical application; Laparoscopic cholecystectomy; Surgical quality control.

Abstract

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