Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation.

Journal: Journal of gastroenterology
Published Date: Aug 16, 2022

Abstract

BACKGROUND: Improved optical diagnostic technology is needed that can be used by also outside expert centers. Hence, we developed an artificial intelligence (AI) system that automatically and robustly predicts the pathological diagnosis based on the revised Vienna Classification using standard colonoscopy images.

Authors

  • Masayoshi Yamada
    Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan. masyamad@ncc.go.jp.
  • Ryosaku Shino
    Biometrics Research Laboratories, NEC Corporation, Kawasaki, Kanagawa, Japan.
  • Hiroko Kondo
    Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo, Japan.
  • Shigemi Yamada
    Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo, Japan.
  • Hiroyuki Takamaru
    Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan.
  • Taku Sakamoto
    Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan.
  • Pradeep Bhandari
    Department of Gastroenterology, Queen Alexandra Hospital, Portsmouth, UK.
  • Hitoshi Imaoka
    Biometrics Research Laboratories, NEC Corporation, Kanagawa, Japan.
  • Aya Kuchiba
    Graduate School of Health Innovation, Kanagawa University of Human Services, Kanagawa, Japan.
  • Taro Shibata
    Biostatistics Division, National Cancer Center, Tokyo, Japan.
  • Yutaka Saito
    National Cancer Center Hospital, Tokyo, Japan (Y.S.).
  • Ryuji Hamamoto
    Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, Tokyo, Japan.

Keywords

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