A deep learning approach for gastroscopic manifestation recognition based on Kyoto Gastritis Score.

Journal: Annals of medicine
PMID: 39498518

Abstract

OBJECTIVE: The risk of gastric cancer can be predicted by gastroscopic manifestation recognition and the Kyoto Gastritis Score. This study aims to validate the applicability of AI approaches for recognizing gastroscopic manifestations according to the definition of Kyoto Gastritis Score, with the goal of improving early gastric cancer detection and reducing gastric cancer mortality.

Authors

  • Ao Liu
    Department of Thoracic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Xilin Zhang
    School of Mathematical Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China.
  • Jiaxin Zhong
    School of Software Technology, Dalian University of Technology, Dalian, China.
  • Zilu Wang
  • Zhenyang Ge
    Department of Gastroenterology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
  • Zhong Wang
    Department of Intensive Care Unit, The First Hospital of China Medical University, Shenyang, Liaoning, China.
  • Xiaoya Fan
    Bio-, Electro- And Mechanical Systems, Université Libre de Bruxelles, Avenue F.D. Roosevelt 50 CP165/56, 1050, Brussels, Belgium. xiaoya.fan@ulb.ac.be.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.

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