Artificial intelligence using a convolutional neural network for automatic detection of small‐bowel angioectasia in capsule endoscopy images

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 artific...

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Bibliographic Details
Published in:Digestive endoscopy 2020-03, Vol.32 (3), p.382-390
Main 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
Format: Article
Language:English
Subjects:
Aged
angioectasia
Capsule Endoscopy
convolutional neural network
Deep Learning
Dilatation, Pathologic
Female
Gastrointestinal Hemorrhage - diagnostic imaging
Humans
Intestine, Small - diagnostic imaging
Intestine, Small - pathology
Male
Middle Aged
Neural Networks, Computer
Predictive Value of Tests
Retrospective Studies
ROC Curve
small bowel
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Summary: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.
ISSN:0915-5635
1443-1661
DOI:10.1111/den.13507