A novel machine learning-based algorithm to identify and classify lesions and anatomical landmarks in colonoscopy images.

Journal: Surgical endoscopy
Published Date: Feb 16, 2021

Abstract

OBJECTIVES: Computer-aided diagnosis (CAD)-based artificial intelligence (AI) has been shown to be highly accurate for detecting and characterizing colon polyps. However, the application of AI to identify normal colon landmarks and differentiate multiple colon diseases has not yet been established. We aimed to develop a convolutional neural network (CNN)-based algorithm (GUTAID) to recognize different colon lesions and anatomical landmarks.

Authors

  • Ying-Chun Jheng
    Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Yen-Po Wang
    Endoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Hung-En Lin
    Endoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Kuang-Yi Sung
    Endoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Yuan-Chia Chu
    Information Management Office, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Huann-Sheng Wang
    Endoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Jeng-Kai Jiang
    Division of Colon and Rectum Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Ming-Chih Hou
    Endoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Fa-Yauh Lee
    Division of Gastroenterology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Ching-Liang Lu
    Endoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taipei, Taiwan. cllu@ym.edu.tw.

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