Development and clinical application value of an artificial intelligence-assisted system for calculating effective colonoscopy withdrawal time
10.3760/cma.j.cn321463-20240330-00604
- VernacularTitle:人工智能辅助结肠镜检查有效退镜时间计算系统的构建及临床应用价值
- Author:
Rongrong GONG
1
;
Liwen YAO
;
Lianlian WU
;
Huiling WU
;
Xun LI
;
Honggang YU
;
Xiangwu DING
Author Information
1. 武汉市第四医院消化内科，武汉 430030
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Colonoscopy;
Effective withdrawal time;
Adenoma detection rate;
Polyp detection rate
- From:
Chinese Journal of Digestive Endoscopy
2025;42(1):42-46
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop an artificial intelligence (AI) calculation system for the effective withdrawal time of colonoscopy and to evaluate its clinical application value.Methods:First, 17 118 colonoscopy pictures from Renmin Hospital of Wuhan University were used for training and testing to establish a deep convolutional neural network model to recognize various colonoscopy fields. Then this model was integrated with the internal and external recognition model and cecum recognition model developed by the research group to create an AI system for automatic calculation of the effective withdrawal time. Finally, 944 colonoscopy videos from the Endoscopy Center of Renmin Hospital of Wuhan University from July 1, 2020 to October 10, 2020 were included in a retrospective analysis. AI automatic computing system was used to calculate the effective withdrawal time, and 89 of them were manually calculated to evaluate the accuracy of the AI automatic computing system. The remaining 855 cases were divided into two groups according to AI calculations, namely, the effective withdrawal time <6 min group ( n=615) and the effective withdrawal time ≥6 min group ( n=240), and the differences in the overall detection rate of adenoma and polyp were compared and analyzed. Results:The accuracy of AI automatic calculation system for effective withdrawal time reached 92.1% (82/89). The overall adenoma detection rate in the group with effective withdrawal time ≥6 min was 37.5% (90/240), that in the group with effective withdrawal time <6 min was 19.0% (117/615), and the difference was statistically significant ( χ2=32.11, P<0.001). The overall polyp detection rate in the group with effective withdrawal time ≥6 min was 75.0% (180/240), and that in the group with effective withdrawal time <6 min was 45.2% (278/615), with statistical significance ( χ2=61.62, P<0.001). Conclusion:AI automatic computing system can accurately calculate the effective withdrawal time of colonoscopy, and can be used to monitor the effective withdrawal time of clinical colonoscopy. In addition, effective withdrawal time ≥6 min can effectively improve the detection rate of adenoma and polyps.
