Using deep learning to assess the function of gastroesophageal flap valve according to the Hill classification system.

Journal: Annals of medicine
PMID: 37949083

Abstract

BACKGROUND: The endoscopic Hill classification of the gastroesophageal flap valve (GEFV) is of great importance for understanding the functional status of the esophagogastric junction (EGJ). Deep learning (DL) methods have been extensively employed in the area of digestive endoscopy. To improve the efficiency and accuracy of the endoscopist's Hill classification and assist in incorporating it into routine endoscopy reports and GERD assessment examinations, this study first employed DL to establish a four-category model based on the Hill classification.

Authors

  • Zhenyang Ge
    Department of Gastroenterology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
  • Youjiang Fang
    Department of Computer Science, Dalian University of Technology, Dalian, Liaoning, China.
  • Jiuyang Chang
    Department of Cardiology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
  • Zequn Yu
    Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
  • Yu Qiao
    Department of English and American Studies, RWTH Aachen University, Aachen, North Rhine-Westphalia, Germany.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.
  • Xin Yang
    Department of Oral Maxillofacial-Head Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China.
  • Zhijun Duan
    Department of Gastroenterology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.

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