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Endoscopy 2020; 52(09): 786-791
DOI: 10.1055/a-1167-8157
Innovations and brief communications

Automatic detection of different types of small-bowel lesions on capsule endoscopy images using a newly developed deep convolutional neural network

Keita Otani
 1   Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan
,
Ayako Nakada
 2   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
,
Yusuke Kurose
 1   Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan
 3   Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
,
Ryota Niikura
 2   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
,
Atsuo Yamada
 2   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
,
Tomonori Aoki
 2   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
,
Hiroyoshi Nakanishi
 4   Department of Gastroenterology, Ishikawa Prefectural Central Hospital, Kanazawa-shi, Ishikawa, Japan
,
Hisashi Doyama
 4   Department of Gastroenterology, Ishikawa Prefectural Central Hospital, Kanazawa-shi, Ishikawa, Japan
,
Kenkei Hasatani
 5   Department of Gastroenterology, Fukui Prefectural Hospital, Fukui-shi, Fukui, Japan
,
Tetsuya Sumiyoshi
 6   The Center for Digestive Disease, Tonan Hospital, Sapporo-shi, Hokkaido, Japan
,
Masaru Kitsuregawa
 7   Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
 8   National Institute of Informatics, Tokyo, Japan
,
Tatsuya Harada
 3   Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
 9   Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
10   Research Center for Medical Bigdata, National Institute of Informatics, Tokyo, Japan
,
Kazuhiko Koike
 2   Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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Abstract

Background Previous computer-aided detection systems for diagnosing lesions in images from wireless capsule endoscopy (WCE) have been limited to a single type of small-bowel lesion. We developed a new artificial intelligence (AI) system able to diagnose multiple types of lesions, including erosions and ulcers, vascular lesions, and tumors.

Methods We trained the deep neural network system RetinaNet on a data set of 167 patients, which consisted of images of 398 erosions and ulcers, 538 vascular lesions, 4590 tumors, and 34 437 normal tissues. We calculated the mean area under the receiver operating characteristic curve (AUC) for each lesion type using five-fold stratified cross-validation.

Results The mean age of the patients was 63.6 years; 92 were men. The mean AUCs of the AI system were 0.996 (95 %CI 0.992 – 0.999) for erosions and ulcers, 0.950 (95 %CI 0.923 – 0.978) for vascular lesions, and 0.950 (95 %CI 0.913 – 0.988) for tumors.

Conclusion We developed and validated a new computer-aided diagnosis system for multiclass diagnosis of small-bowel lesions in WCE images.

Tab. 1s – 3s, Fig. 1s – 3s



Publication History

Received: 25 December 2019

Accepted: 14 April 2020

Article published online:
17 June 2020

© Georg Thieme Verlag KG
Stuttgart · New York

 

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