Artificial intelligence using a convolutional neural network for automatic detection of small‐bowel angioectasia in capsule endoscopy images. Issue 3 (2nd October 2019)

• Record Type: Journal Article
• Authors: Tsuboi, Akiyoshi; Oka, Shiro; Aoyama, Kazuharu; Saito, Hiroaki; Aoki, Tomonori; Yamada, Atsuo; Matsuda, Tomoki; Fujishiro, Mitsuhiro; Ishihara, Soichiro; Nakahori, Masato; Koike, Kazuhiko; Tanaka, Shinji; Tada, Tomohiro
• Is Part Of: Digestive endoscopy. Volume 32:Issue 3(2020)
• Page Start: 382
• Page End: 390
• Publication Date: 2019-10-02
• Subjects: angioectasia -- capsule endoscopy -- convolutional neural network -- deep learning -- small bowel; Digestive organs -- Diseases -- Periodicals; Digestive organs -- Diseases -- Diagnosis -- Periodicals; Endoscopy -- Periodicals; Digestive System Diseases -- diagnosis -- Periodicals; Digestive System Diseases -- therapy -- Periodicals; Endoscopy -- Periodicals; 616.3
• DOI: 10.1111/den.13507
• Languages: English
• ISSNs: 0915-5635

Abstract

Abstract : Background and Aim: Although small‐bowel angioectasia is reported as the most common cause of bleeding in patients and frequently diagnosed by capsule endoscopy (CE) in patients with obscure gastrointestinal bleeding, a computer‐aided detection method has not been established. We developed an artificial intelligence system with deep learning that can automatically detect small‐bowel angioectasia in CE images. Methods: We trained a deep convolutional neural network (CNN) system based on Single Shot Multibox Detector using 2237 CE images of angioectasia. We assessed its diagnostic accuracy by calculating the area under the receiver operating characteristic curve (ROC‐AUC), sensitivity, specificity, positive predictive value, and negative predictive value using an independent test set of 10 488 small‐bowel images, including 488 images of small‐bowel angioectasia. Results: The AUC to detect angioectasia was 0.998. Sensitivity, specificity, positive predictive value, and negative predictive value of CNN were 98.8%, 98.4%, 75.4%, and 99.9%, respectively, at a cut‐off value of 0.36 for the probability score. Conclusions: We developed and validated a new system based on CNN to automatically detect angioectasia in CE images. This may be well applicable to daily clinical practice to reduce the burden of physicians as well as to reduce oversight.