An Interpretable Artificial Intelligence System for Crohn's Disease Ulcer Identification and Grading on Double-Balloon Enteroscopy Images.

Journal: United European gastroenterology journal
Published Date: Jul 3, 2025

Abstract

BACKGROUND: Crohn's disease (CD) is an incurable inflammatory bowel disease that can lead to a variety of complications and requires lifelong treatment. However, the diagnosis and management of Crohn's disease exhibit high rates of misdiagnosis and missed diagnoses, along with significant variability, among primary care facilities and novice endoscopists. Therefore, we established an interpretable artificial intelligence (AI) system using double-balloon enteroscopy to facilitate Crohn's disease ulcer identification and grading.

Authors

  • Qiuyuan Liu
    Department of Gastroenterology, First Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Wanqing Xie
  • Aodi Wang
    Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China.
  • Wei Han
    Department of Pharmacology, The Key Laboratory of Neural and Vascular Biology, The Key Laboratory of New Drug Pharmacology and Toxicology, Ministry of Education, Collaborative Innovation Center of Hebei Province for Mechanism, Diagnosis and Treatment of Neuropsychiatric Diseases, Hebei Medical University, Shijiazhuang, Hebei, China.
  • Yaonan Zhu
    Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China.
  • Jing Hu
    College of Chemistry, Sichuan University Chengdu 610064 People's Republic of China xmpuscu@scu.edu.cn +86 028 8541 2290.
  • Pengcheng Liang
    Biomedical Engineering Department, Columbia University, New York, NY, United States of America.
  • Juan Wu
  • Xiaofeng Liu
    Changzhou Key Laboratory of Robots & Intelligent Technology, Hohai University, China.
  • Xiaodong Yang
    Clinical and Research Center for Infectious Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, China.
  • Baoliang Zhang
    Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China.
  • Nannan Zhu
    School of Pharmaceutical Sciences, Jiangnan University, Wuxi, 214122, Jiangsu, China.
  • Bingqing Bai
    Department of Gastroenterology, First Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Yiqing Mei
    Department of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Zhen Liang
    Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China. Electronic address: jane-l@sys.i.kyoto-u.ac.jp.
  • Mingmei Cheng
    Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China; Department of Psychology, School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China. Electronic address: chengmm68@gmail.com.
  • Qiao Mei
    Department of Gastroenterology, First Affiliated Hospital of Anhui Medical University, Hefei, China.

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