Deep learning-based endoscopic anatomy classification: an accelerated approach for data preparation and model validation.

Journal: Surgical endoscopy
Published Date: Sep 29, 2021

Abstract

BACKGROUND: Photodocumentation during endoscopy procedures is one of the indicators for endoscopy performance quality; however, this indicator is difficult to measure and audit in the endoscopy unit. Emerging artificial intelligence technology may solve this problem, which requires a large amount of material for model development. We developed a deep learning-based endoscopic anatomy classification system through convolutional neural networks with an accelerated data preparation approach.

Authors

  • Yuan-Yen Chang
    Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
  • Pai-Chi Li
    Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
  • Ruey-Feng Chang
    Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan and Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.
  • Chih-Da Yao
    Division of Gastroenterology, Lukang Christian Hospital, Changhua, Taiwan.
  • Yang-Yuan Chen
    Division of Gastroenterology, Changhua Christian Hospital, Changhua, Taiwan.
  • Wen-Yen Chang
    Department of Medical Education, National Taiwan University Hospital, Taipei, Taiwan.
  • Hsu-Heng Yen
    Artificial Intelligence Development Center, Changhua Christian Hospital, Changhua, Taiwan. 91646@cch.org.tw.

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