AI based colorectal disease detection using real-time screening colonoscopy.

Journal: Precision clinical medicine
Published Date: May 20, 2021

Abstract

Colonoscopy is an effective tool for early screening of colorectal diseases. However, the application of colonoscopy in distinguishing different intestinal diseases still faces great challenges of efficiency and accuracy. Here we constructed and evaluated a deep convolution neural network (CNN) model based on 117 055 images from 16 004 individuals, which achieved a high accuracy of 0.933 in the validation dataset in identifying patients with polyp, colitis, colorectal cancer (CRC) from normal. The proposed approach was further validated on multi-center real-time colonoscopy videos and images, which achieved accurate diagnostic performance on detecting colorectal diseases with high accuracy and precision to generalize across external validation datasets. The diagnostic performance of the model was further compared to the skilled endoscopists and the novices. In addition, our model has potential in diagnosis of adenomatous polyp and hyperplastic polyp with an area under the receiver operating characteristic curve of 0.975. Our proposed CNN models have potential in assisting clinicians in making clinical decisions with efficiency during application.

Authors

  • Jiawei Jiang
    Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.
  • Qianrong Xie
    Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.
  • Zhuo Cheng
    Digestive endoscopy center, Dazhou Central Hospital, Dazhou 635000, China.
  • Jianqiang Cai
    Department of Hepatobiliary Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
  • Tian Xia
    National Center of Biomedical Analysis, Beijing 100850, China.
  • Hang Yang
    Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.
  • Bo Yang
    Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China.
  • Hui Peng
    College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
  • Xuesong Bai
    Digestive endoscopy center, Dazhou Central Hospital, Dazhou 635000, China.
  • Mingque Yan
    Digestive endoscopy center, Dazhou Central Hospital, Dazhou 635000, China.
  • Xue Li
    Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.
  • Jun Zhou
    Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.
  • Xuan Huang
    Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.
  • Liang Wang
    Information Department, Dazhou Central Hospital, Dazhou 635000, China.
  • Haiyan Long
    Digestive endoscopy center, Quxian People's Hospital, Dazhou 635000, China.
  • Pingxi Wang
    Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.
  • Yanpeng Chu
    Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.
  • Fan-Wei Zeng
    Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.
  • Xiuqin Zhang
    Institute of Molecular Medicine, Peking University, Beijing 100871, China.
  • Guangyu Wang
    State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Fanxin Zeng
    Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.

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