Computer-aided diagnosis of serrated colorectal lesions using non-magnified white-light endoscopic images

• Published in: International journal of colorectal disease Vol. 37; no. 8; pp. 1875 - 1884
• Main Authors: Nemoto, Daiki, Guo, Zhe, Peng, Boyuan, Zhang, Ruiyao, Nakajima, Yuki, Hayashi, Yoshikazu, Yamashina, Takeshi, Aizawa, Masato, Utano, Kenichi, Lefor, Alan Kawarai, Zhu, Xin, Togashi, Kazutomo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2022; Springer; Springer Nature B.V
• Subjects: Adenoma; Colon; Colonoscopy; Diagnosis; Endoscopy; Gastroenterology; Hepatology; Internal Medicine; Lesions; Medical imaging equipment; Medicine; Medicine & Public Health; Polyps; Proctology; Surgery; Tumors; Artificial intelligence; Colonoscopy; Colonic polyps; Serrated lesion
• ISSN: 1432-1262; 0179-1958; 1432-1262
• DOI: 10.1007/s00384-022-04210-x

Purpose Computer-aided diagnosis systems for polyp characterization are commercially available but cannot recognize subtypes of sessile lesions. This study aimed to develop a computer-aided diagnosis system to characterize polyps using non-magnified white-light endoscopic images. Methods A total of 2249 non-magnified white-light images from 1030 lesions including 534 tubular adenomas, 225 sessile serrated adenoma/polyps, and 271 hyperplastic polyps in the proximal colon were consecutively extracted from an image library and divided into training and testing datasets (4:1), based on the date of colonoscopy. Using ResNet-50 networks, we developed a classifier (1) to differentiate adenomas from serrated lesions, and another classifier (2) to differentiate sessile serrated adenoma/polyps from hyperplastic polyps. Diagnostic performance was assessed using the testing dataset. The computer-aided diagnosis system generated a probability score for each image, and a probability score for each lesion was calculated as the weighted mean with a log 10 -transformation. Two experts (E1, E2) read the identical testing dataset with a probability score. Results The area under the curve of classifier (1) for adenomas was equivalent to E1 and superior to E2 (classifier 86%, E1 86%, E2 69%; classifier vs. E2, p  < 0.001). In contrast, the area under the curve of classifier (2) for sessile serrated adenoma/polyps was inferior to both experts (classifier 55%, E1 68%, E2 79%; classifier vs. E2, p  < 0.001). Conclusion The classifier (1) developed using white-light images alone compares favorably with experts in differentiating adenomas from serrated lesions. However, the classifier (2) to identify sessile serrated adenoma/polyps is inferior to experts.