Deep learning to predict esophageal variceal bleeding based on endoscopic images.

Journal: The Journal of international medical research
PMID: 37818651

Abstract

OBJECTIVE: Esophageal varix (EV) bleeding is a particularly serious complications of cirrhosis. Prediction of EV bleeding requires extensive endoscopy experience; it remains unreliable and inefficient. This retrospective cohort study evaluated the feasibility of using deep learning (DL) to predict the 12-month risk of EV bleeding based on endoscopic images.

Authors

  • Yu Hong
    Jiangsu Province Key Laboratory of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing, Jiangsu, 210009, China.
  • Qianqian Yu
    Department of Oncology, Jintan Affiliated Hospital of Jiangsu University, Jintan, China.
  • Feng Mo
    Department of General Surgery, Jintan Affiliated Hospital of Jiangsu University, Changzhou 213200, China.
  • Minyue Yin
    Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China.
  • Chang Xu
    Institute of Cardio-Cerebrovascular Medicine, Central Hospital of Dalian University of Technology, Dalian 116089, China.
  • Shiqi Zhu
    Department of Gastroenterology, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou , Jiangsu, 215000, China.
  • Jiaxi Lin
    Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China.
  • Guoting Xu
    Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China.
  • Jingwen Gao
    Department of Gastroenterology, the First Affiliated Hospital of Soochow University, Suzhou, 215006, Jiangsu, China.
  • Lu Liu
    College of Pharmacy, Harbin Medical University, Harbin, China.
  • Yu Wang
    Clinical and Technical Support, Philips Healthcare, Shanghai, China.

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