Using deep learning and explainable artificial intelligence to assess the severity of gastroesophageal reflux disease according to the Los Angeles Classification System.

Journal: Scandinavian journal of gastroenterology
PMID: 36625026

Abstract

OBJECTIVES: Gastroesophageal reflux disease (GERD) is a complex disease with a high worldwide prevalence. The Los Angeles classification (LA-grade) system is meaningful for assessing the endoscopic severity of GERD. Deep learning (DL) methods have been widely used in the field of endoscopy. However, few DL-assisted researches have concentrated on the diagnosis of GERD. This study is the first to develop a five-category classification DL model based on the LA-grade using explainable artificial intelligence (XAI).

Authors

  • Zhenyang Ge
    Department of Gastroenterology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
  • Bowen Wang
    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, 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.
  • Zhenyuan Zhou
    Information Management Department, Dalian Municipal Central Hospital, Dalian, China.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.
  • Zhijun Duan
    Department of Gastroenterology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.

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