Novel deep learning-based computer-aided diagnosis system for predicting inflammatory activity in ulcerative colitis.

Journal: Gastrointestinal endoscopy
PMID: 35985375

Abstract

BACKGROUND AND AIMS: Endoscopy is increasingly performed for evaluating patients with ulcerative colitis (UC). However, its diagnostic accuracy is largely affected by the subjectivity of endoscopists' experience and scoring methods, and scoring of selected endoscopic images cannot reflect the inflammation of the entire intestine. We aimed to develop an automatic scoring system using deep-learning technology for consistent and objective scoring of endoscopic images and full-length endoscopic videos of patients with UC.

Authors

  • Yanyun Fan
    Department of Gastroenterology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; Department of Digestive Diseases, School of Medicine, Xiamen University, Xiamen, China.
  • Ruochen Mu
    Department of Computer Science, Xiamen University, Xiamen, China.
  • Hongzhi Xu
    Department of Gastroenterology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; Department of Digestive Diseases, School of Medicine, Xiamen University, Xiamen, China.
  • Chenxi Xie
    School of Biomedical Engineering, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China.
  • Yinghao Zhang
    Department of Computer Science, Xiamen University, Xiamen, China.
  • Lupeng Liu
    Department of Gastroenterology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; Xiamen Key Laboratory of Intestinal Microbiome and Human Health, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
  • Lin Wang
    Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.
  • Huaxiu Shi
    Department of Gastroenterology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; Department of Digestive Diseases, School of Medicine, Xiamen University, Xiamen, China.
  • Yiqun Hu
    Department of Gastroenterology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; Xiamen Key Laboratory of Intestinal Microbiome and Human Health, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
  • Jianlin Ren
    Department of Gastroenterology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; Department of Digestive Diseases, School of Medicine, Xiamen University, Xiamen, China.
  • Jing Qin
    School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China.
  • Liansheng Wang
    Department of Computer Science, Xiamen University, Xiamen 361005, China.
  • Shuntian Cai
    Department of Gastroenterology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; Xiamen Key Laboratory of Intestinal Microbiome and Human Health, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China. Electronic address: xuhongzhi@xmu.edu.cn.

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