Clin Endosc.  2020 Mar;53(2):117-126. 10.5946/ce.2020.054.

Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy

  • 1Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul, Korea
  • 2Promedius, Inc., Seoul, Korea
  • 3Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
  • 4Department of Radiology, Asan Medical Center, Seoul, Korea


Recently, significant improvements have been made in artificial intelligence. The artificial neural network was introduced in the 1950s. However, because of the low computing power and insufficient datasets available at that time, artificial neural networks suffered from overfitting and vanishing gradient problems for training deep networks. This concept has become more promising owing to the enhanced big data processing capability, improvement in computing power with parallel processing units, and new algorithms for deep neural networks, which are becoming increasingly successful and attracting interest in many domains, including computer vision, speech recognition, and natural language processing. Recent studies in this technology augur well for medical and healthcare applications, especially in endoscopic imaging. This paper provides perspectives on the history, development, applications, and challenges of deep-learning technology.


Artificial intelligence; Convolutional neural network; Deep learning; Endoscopic imaging; Machine learning
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