Ren Togo1, Nobutake Yamamichi2, Katsuhiro Mabe3, Yu Takahashi2, Chihiro Takeuchi2, Mototsugu Kato4, Naoya Sakamoto5, Kenta Ishihara1, Takahiro Ogawa1, Miki Haseyama1. 1. Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan. 2. Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. 3. Department of Gastroenterology, National Hospital Organization Hakodate Hospital, 18-16, Kawahara-cho, Hakodate City, Hokkaido, 041-8512, Japan. katsumabe@me.com. 4. Department of Gastroenterology, National Hospital Organization Hakodate Hospital, 18-16, Kawahara-cho, Hakodate City, Hokkaido, 041-8512, Japan. 5. Department of Gastroenterology, Hokkaido University Graduate School of Medicine, Sapporo, 060-8648, Japan.
Abstract
BACKGROUND: Deep learning has become a new trend of image recognition tasks in the field of medicine. We developed an automated gastritis detection system using double-contrast upper gastrointestinal barium X-ray radiography. METHODS: A total of 6520 gastric X-ray images obtained from 815 subjects were analyzed. We designed a deep convolutional neural network (DCNN)-based gastritis detection scheme and evaluated the effectiveness of our method. The detection performance of our method was compared with that of ABC (D) stratification. RESULTS: Sensitivity, specificity, and harmonic mean of sensitivity and specificity of our method were 0.962, 0.983, and 0.972, respectively, and those of ABC (D) stratification were 0.925, 0.998, and 0.960, respectively. Although there were 18 false negative cases in ABC (D) stratification, 14 of those 18 cases were correctly classified into the positive group by our method. CONCLUSIONS: Deep learning techniques may be effective for evaluation of gastritis/non-gastritis. Collaborative use of DCNN-based gastritis detection systems and ABC (D) stratification will provide more reliable gastric cancer risk information.
BACKGROUND: Deep learning has become a new trend of image recognition tasks in the field of medicine. We developed an automated gastritis detection system using double-contrast upper gastrointestinal barium X-ray radiography. METHODS: A total of 6520 gastric X-ray images obtained from 815 subjects were analyzed. We designed a deep convolutional neural network (DCNN)-based gastritis detection scheme and evaluated the effectiveness of our method. The detection performance of our method was compared with that of ABC (D) stratification. RESULTS: Sensitivity, specificity, and harmonic mean of sensitivity and specificity of our method were 0.962, 0.983, and 0.972, respectively, and those of ABC (D) stratification were 0.925, 0.998, and 0.960, respectively. Although there were 18 false negative cases in ABC (D) stratification, 14 of those 18 cases were correctly classified into the positive group by our method. CONCLUSIONS: Deep learning techniques may be effective for evaluation of gastritis/non-gastritis. Collaborative use of DCNN-based gastritis detection systems and ABC (D) stratification will provide more reliable gastric cancer risk information.
Authors: Jacques Ferlay; Isabelle Soerjomataram; Rajesh Dikshit; Sultan Eser; Colin Mathers; Marise Rebelo; Donald Maxwell Parkin; David Forman; Freddie Bray Journal: Int J Cancer Date: 2014-10-09 Impact factor: 7.396
Authors: Wanderson Gonçalves E Gonçalves; Marcelo Henrique de Paula Dos Santos; Fábio Manoel França Lobato; Ândrea Ribeiro-Dos-Santos; Gilderlanio Santana de Araújo Journal: BMJ Open Gastroenterol Date: 2020-03-26