Establishment of an artificial intelligence-assisted system for automatic lesion recognition in small intestinal capsule endoscopy based on convolutional networks
10.3760/cma.j.cn321463-20240306-00103
- VernacularTitle:基于卷积网络建立的小肠胶囊内镜人工智能辅助自动识别系统
- Author:
Jian CHEN
1
;
Bin SUN
;
Ganhong WANG
;
Kaijian XIA
;
Xiaodan XU
Author Information
1. 常熟市第一人民医院消化内科，苏州 215500
- Publication Type:Journal Article
- Keywords:
Capsule endoscopy;
Deep learning;
Artificial intelligence;
Computer-assisted;
Convolutional neural networks
- From:
Chinese Journal of Digestive Endoscopy
2025;42(11):853-863
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop and validate an artificial intelligence-assisted system based on convolutional neural networks (CNN) for automatic lesion recognition in small intestinal capsule endoscopy.Methods:Three small intestinal capsule endoscopy datasets were used for training ( n=26 638), validating ( n=6 652), and testing ( n=1 013) the deep learning model, covering 12 lesion categories, including vascular malformations, hemorrhage, erosion, erythema, stenosis, lymphangiectasia, submucosal tumors, polyps, lymphoid follicles, foreign bodies, veins, and normal mucosa. CNN performance was measured by area under receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, and F1 score, with comparisons with endoscopists of different experience levels. Results:The top-performing model (EfficientNet-CE) achieved 86.28% sensitivity, 98.67% specificity, and AUC of 0.987 4 across all categories. It demonstrated high accuracy (86.28%) and a processing speed of 52.43 frames per second, approximately 42.4 times faster than junior endoscopists (<3 years' experience) and 40.3 times faster than senior endoscopists (>5 years' experience).Conclusion:The CNN-based model allows rapid, accurate identification of 12 small intestinal lesion types and effectively supports endoscopists in reviewing capsule endoscopy examinations due to its high sensitivity.
