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Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks
Authors:Yoshimasa Horie  Toshiyuki Yoshio  Kazuharu Aoyama  Shoichi Yoshimizu  Yusuke Horiuchi  Akiyoshi Ishiyama  Toshiaki Hirasawa  Tomohiro Tsuchida  Tsuyoshi Ozawa  Soichiro Ishihara  Youichi Kumagai  Mitsuhiro Fujishiro  Iruru Maetani  Junko Fujisaki  Tomohiro Tada
Institution:1. Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan;2. Division of Gastroenterology and Hepatology, Department of Internal Medicine, Toho University Ohashi Medical Center, Tokyo, Japan;3. Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan;4. AI Medical Service Inc, Tokyo, Japan;5. Surgery Department, Sanno Hospital, International University of Health and Welfare, Tokyo, Japan;6. Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Saitama, Japan;7. Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan;8. Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
Abstract:
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  • Keywords:AI  artificial intelligence  CNN  convolutional neural network  EAC  esophageal adenocarcinoma  ESCC  esophageal squamous cell carcinoma  NBI  narrow-band imaging  PPV  positive predictive value  NPV  negative predictive value  WLI  white-light imaging
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