Tomonori Aoki1, Atsuo Yamada1, Kazuharu Aoyama2, Hiroaki Saito3, Akiyoshi Tsuboi4, Ayako Nakada1, Ryota Niikura1, Mitsuhiro Fujishiro5, Shiro Oka4, Soichiro Ishihara6, Tomoki Matsuda3, Shinji Tanaka4, Kazuhiko Koike1, Tomohiro Tada7. 1. Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 2. AI Medical Service Inc., Tokyo, Japan. 3. Department of Gastroenterology, Sendai Kousei Hospital, Miyagi, Japan. 4. Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan. 5. Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Department of Endoscopy and Endoscopic Surgery, The University of Tokyo, Tokyo, Japan. 6. Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan; Surgery Department, Sanno Hospital, International University of Health and Welfare, Tokyo, Japan; Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 7. AI Medical Service Inc., Tokyo, Japan; Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan; Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Abstract
BACKGROUND AND AIMS: Although erosions and ulcerations are the most common small-bowel abnormalities found on wireless capsule endoscopy (WCE), a computer-aided detection method has not been established. We aimed to develop an artificial intelligence system with deep learning to automatically detect erosions and ulcerations in WCE images. METHODS: We trained a deep convolutional neural network (CNN) system based on a Single Shot Multibox Detector, using 5360 WCE images of erosions and ulcerations. We assessed its performance by calculating the area under the receiver operating characteristic curve and its sensitivity, specificity, and accuracy using an independent test set of 10,440 small-bowel images including 440 images of erosions and ulcerations. RESULTS: The trained CNN required 233 seconds to evaluate 10,440 test images. The area under the curve for the detection of erosions and ulcerations was 0.958 (95% confidence interval [CI], 0.947-0.968). The sensitivity, specificity, and accuracy of the CNN were 88.2% (95% CI, 84.8%-91.0%), 90.9% (95% CI, 90.3%-91.4%), and 90.8% (95% CI, 90.2%-91.3%), respectively, at a cut-off value of 0.481 for the probability score. CONCLUSIONS: We developed and validated a new system based on CNN to automatically detect erosions and ulcerations in WCE images. This may be a crucial step in the development of daily-use diagnostic software for WCE images to help reduce oversights and the burden on physicians.
BACKGROUND AND AIMS: Although erosions and ulcerations are the most common small-bowel abnormalities found on wireless capsule endoscopy (WCE), a computer-aided detection method has not been established. We aimed to develop an artificial intelligence system with deep learning to automatically detect erosions and ulcerations in WCE images. METHODS: We trained a deep convolutional neural network (CNN) system based on a Single Shot Multibox Detector, using 5360 WCE images of erosions and ulcerations. We assessed its performance by calculating the area under the receiver operating characteristic curve and its sensitivity, specificity, and accuracy using an independent test set of 10,440 small-bowel images including 440 images of erosions and ulcerations. RESULTS: The trained CNN required 233 seconds to evaluate 10,440 test images. The area under the curve for the detection of erosions and ulcerations was 0.958 (95% confidence interval [CI], 0.947-0.968). The sensitivity, specificity, and accuracy of the CNN were 88.2% (95% CI, 84.8%-91.0%), 90.9% (95% CI, 90.3%-91.4%), and 90.8% (95% CI, 90.2%-91.3%), respectively, at a cut-off value of 0.481 for the probability score. CONCLUSIONS: We developed and validated a new system based on CNN to automatically detect erosions and ulcerations in WCE images. This may be a crucial step in the development of daily-use diagnostic software for WCE images to help reduce oversights and the burden on physicians.
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