Automated detection of small bowel lesions based on capsule endoscopy using deep learning algorithm

• Published in: Clinics and research in hepatology and gastroenterology 2024-05, Vol.48 (5), p.102334, Article 102334
• Main Authors: Li, Lan, Yang, Liping, Zhang, Bingling, Yan, Guofei, Bao, Yaqing, Zhu, Renke, Li, Shengjie, Wang, Huogen, Chen, Ming, Jin, Chaohui, Chen, Yishu, Yu, Chaohui
• Format: Article
• Language: English
• Subjects: Adult; Aged; Algorithms; Artificial intelligence; Capsule endoscopy; Capsule Endoscopy - methods; Deep Learning; Female; Humans; Intestinal Diseases - diagnosis; Intestinal Diseases - diagnostic imaging; Intestine, Small - diagnostic imaging; Intestine, Small - pathology; Male; Middle Aged; Prospective Studies; Sensitivity and Specificity; Small bowel
• ISSN: 2210-7401; 2210-741X; 2210-741X
• DOI: 10.1016/j.clinre.2024.102334

•Artificial intelligence is widely used for detection and diagnosis of CE.•CE-YOLOv5 not only identifies abnormalities of small bowel but also differentiates between lesion types.•CE-YOLOv5 provides an approach for automated detection lesions captured by CE videos in real-life setting. In order to overcome the challenges of lesion detection in capsule endoscopy (CE), we improved the YOLOv5-based deep learning algorithm and established the CE-YOLOv5 algorithm to identify small bowel lesions captured by CE. A total of 124,678 typical abnormal images from 1,452 patients were enrolled to train the CE-YOLOv5 model. Then 298 patients with suspected small bowel lesions detected by CE were prospectively enrolled in the testing phase of the study. Small bowel images and videos from the above 298 patients were interpreted by the experts, non-experts and CE-YOLOv5, respectively. The sensitivity of CE-YOLOv5 in diagnosing vascular lesions, ulcerated/erosive lesions, protruding lesions, parasite, diverticulum, active bleeding and villous lesions based on CE videos was 91.9 %, 92.2 %, 91.4 %, 93.1 %, 93.3 %, 95.1 %, and 100 % respectively. Furthermore, CE-YOLOv5 achieved specificity and accuracy of more than 90 % for all lesions. Compared with experts, the CE-YOLOv5 showed comparable overall sensitivity, specificity and accuracy (all P > 0.05). Compared with non-experts, the CE-YOLOv5 showed significantly higher overall sensitivity (P < 0.0001) and overall accuracy (P < 0.0001), and a moderately higher overall specificity (P = 0.0351). Furthermore, the time for AI-reading (5.62 ± 2.81 min) was significantly shorter than that for the other two groups (both P < 0.0001). CE-YOLOv5 diagnosed small bowel lesions in CE videos with high sensitivity, specificity and accuracy, providing a reliable approach for automated lesion detection in real-world clinical practice.