Application of deep learning-based artificial intelligence technology in bowel preparation assessment
10.3760/cma.j.cn321463-20231115-00297
- VernacularTitle:基于深度学习的人工智能技术在肠道准备评估中的应用
- Author:
Wen WANG
1
;
Liwen YAO
;
Huizhen XIONG
;
Qiucheng LI
;
Honglei CHEN
;
Honggang YU
Author Information
1. 武汉大学人民医院消化内科，武汉 430060
- Publication Type:Journal Article
- Keywords:
Artificial intelligence;
Colonoscopy;
Adenoma miss rate;
Bowel preparation
- From:
Chinese Journal of Digestive Endoscopy
2025;42(2):109-114
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the correlationship between an artificial intelligence-based e-Boston bowel preparation scale (e-BBPS) system score and the adenoma miss rate.Methods:Colonoscopy images of 4 373 patients at the Endoscopy Center of Renmin Hospital of Wuhan University from December 21, 2017 to December 31, 2019 were collected for model training. Patients who underwent colonoscopy at the Eighth Affiliated Hospital of Sun Yat-sen University from October 8, 2021 to November 9, 2022 were prospectively included. Patient's bowel preparation was evaluated by the e-BBPS system and endoscopists based on BBPS score. If both the endoscopists and e-BPPS system believed that the bowel preparation was sufficient, the patient immediately proceeded to a second colonoscopy. Otherwise, the patient underwent bowel preparation again. The differences in adenoma and polyp miss rate between the qualified group (e-BBPS system score ≤3) and the unqualified group (e-BBPS system score >3) were compared.Results:The adenoma miss rate in the qualified group was significantly lower than that in the unqualified group [26.72% (62/232) VS 42.53% (37/87), χ2=7.384， P=0.007, OR=2.029 (95% CI: 1.212-3.396)], and the polyp miss rate in the qualified group was significantly lower than that in the unqualified group [27.28% (195/702) VS 41.24% (113/274), χ2=16.539， P<0.001, OR=1.825 (95% CI: 1.363-2.443)]. Conclusion:The deep learning-based e-BBPS system demonstrates accuracy and reliability in bowel preparation assessment, offering potential to standardize the process of evaluating bowel preparation and reduce missed lesions.
