Akiyoshi Tsuboi1, Shiro Oka1, Kazuharu Aoyama2, Hiroaki Saito3, Tomonori Aoki4, Atsuo Yamada4, Tomoki Matsuda3, Mitsuhiro Fujishiro5, Soichiro Ishihara6,7, Masato Nakahori3, Kazuhiko Koike4, Shinji Tanaka1, Tomohiro Tada2,6,7. 1. Department of Endoscopy, Hiroshima University Hospital, Hiroshima, Japan. 2. AI Medical Service Inc., Tokyo, Japan. 3. Department of Gastroenterology, Sendai Kousei Hospital, Miyagi, Japan. 4. Department of Gastroenterology, Tokyo, Japan. 5. Department of Gastroenterology & Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan. 6. Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 7. Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan.
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.
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.
Authors: Stefania Chetcuti Zammit; Lawrence A Bull; David S Sanders; Jessica Galvin; Nikolaos Dervilis; Reena Sidhu; Keith Worden Journal: J Med Syst Date: 2020-10-02 Impact factor: 4.460