Improved Object Detection Artificial Intelligence Using the Revised RetinaNet Model for the Automatic Detection of Ulcerations, Vascular Lesions, and Tumors in Wireless Capsule Endoscopy
Ayako Nakada,
Ryota Niikura,
Keita Otani,
Yusuke Kurose,
Yoshito Hayashi,
Kazuya Kitamura,
Hiroyoshi Nakanishi,
Seiji Kawano,
Testuya Honda,
Kenkei Hasatani,
Tetsuya Sumiyoshi,
Tsutomu Nishida,
Atsuo Yamada,
Tomonori Aoki,
Tatsuya Harada,
Takashi Kawai,
Mitsuhiro Fujishiro
Affiliations
Ayako Nakada
Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo 1138655, Japan
Ryota Niikura
Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo 1138655, Japan
Keita Otani
Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 1538904, Japan
Yusuke Kurose
Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 1538904, Japan
Yoshito Hayashi
Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, Osaka 5650871, Japan
Kazuya Kitamura
Department of Gastroenterology, Kanazawa University Hospital, 9208641 Kanazawa, Japan
Hiroyoshi Nakanishi
Department of Gastroenterology, Ishikawa Prefectural Central Hospital, Ishikawa 9208530, Japan
Seiji Kawano
Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama 7008558, Japan
Testuya Honda
Department of Gastroenterology, Nagasaki Harbor Medical Center, 8508555 Nagasaki, Japan
Kenkei Hasatani
Department of Gastroenterology, Fukui Prefectural Hospital, Fukui 9108526, Japan
Tetsuya Sumiyoshi
The Center for Digestive Disease, Tonan Hospital, Sapporo 0600004, Japan
Tsutomu Nishida
Department of Gastroenterology, Toyonaka Municipal Hospital, 5608565 Toyonaka, Japan
Atsuo Yamada
Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo 1138655, Japan
Tomonori Aoki
Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo 1138655, Japan
Tatsuya Harada
Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 1538904, Japan
Takashi Kawai
Department of Gastroenterological Endoscopy, Tokyo Medical University Hospital, Tokyo 1600023, Japan
Mitsuhiro Fujishiro
Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo 1138655, Japan
The use of computer-aided detection models to diagnose lesions in images from wireless capsule endoscopy (WCE) is a topical endoscopic diagnostic solution. We revised our artificial intelligence (AI) model, RetinaNet, to better diagnose multiple types of lesions, including erosions and ulcers, vascular lesions, and tumors. RetinaNet was trained using the data of 1234 patients, consisting of images of 6476 erosions and ulcers, 1916 vascular lesions, 7127 tumors, and 14,014,149 normal tissues. The mean area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for each lesion were evaluated using five-fold stratified cross-validation. Each cross-validation set consisted of between 6,647,148 and 7,267,813 images from 217 patients. The mean AUC values were 0.997 for erosions and ulcers, 0.998 for vascular lesions, and 0.998 for tumors. The mean sensitivities were 0.919, 0.878, and 0.876, respectively. The mean specificities were 0.936, 0.969, and 0.937, and the mean accuracies were 0.930, 0.962, and 0.924, respectively. We developed a new version of an AI-based diagnostic model for the multiclass identification of small bowel lesions in WCE images to help endoscopists appropriately diagnose small intestine diseases in daily clinical practice.
