Deep learning for tracing esophageal motility function over time.

Journal: Computer methods and programs in biomedicine
PMID: 34126411

Abstract

BACKGROUND AND OBJECTIVE: Esophageal high-resolution manometry (HRM) is widely performed to evaluate the representation of manometric features in patients for diagnosing normal esophageal motility and motility disorders. Clinicians commonly assess esophageal motility function using a scheme termed the Chicago classification, which is difficult, time-consuming and inefficient with large amounts of data.

Authors

  • Zheng Wang
    Department of Infectious Diseases, Renmin Hospital of Wuhan University, Wuhan 430060, China.
  • Muzhou Hou
    School of Mathematics and Statistics, Central South University, Changsha, 410083, China. houmuzhou@sina.com.
  • Lu Yan
    Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Environment, Zhejiang University of Technology, Hangzhou 310032, China.
  • Yuzhuo Dai
    School of Mathematics and Statistics, Central South University, Changsha 410083, China.
  • Yani Yin
    Department of Gastroenterology of Xiangya hospital, Central South University, Changsha 410008, China. Electronic address: yinyani@csu.edu.cn.
  • Xiaowei Liu
    Greater Bay Area Center for Drug Evaluation and Inspection of National Medical Products Administration, Shenzhen 518017, China.

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