Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging.

Journal: Journal of gastroenterology and hepatology
Published Date:

Abstract

BACKGROUND AND AIM: Magnifying endoscopy with narrow-band imaging (ME-NBI) has made a huge contribution to clinical practice. However, acquiring skill at ME-NBI diagnosis of early gastric cancer (EGC) requires considerable expertise and experience. Recently, artificial intelligence (AI), using deep learning and a convolutional neural network (CNN), has made remarkable progress in various medical fields. Here, we constructed an AI-assisted CNN computer-aided diagnosis (CAD) system, based on ME-NBI images, to diagnose EGC and evaluated the diagnostic accuracy of the AI-assisted CNN-CAD system.

Authors

  • Hiroya Ueyama
    Department of Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan.
  • Yusuke Kato
    AI Medical Service Inc., Tokyo, Japan.
  • Yoichi Akazawa
    Department of Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan.
  • Noboru Yatagai
    Department of Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan.
  • Hiroyuki Komori
    Department of Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan.
  • Tsutomu Takeda
    Department of Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan.
  • Kohei Matsumoto
    Department of Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan.
  • Kumiko Ueda
    Department of Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan.
  • Kenshi Matsumoto
    Department of Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan.
  • Mariko Hojo
    Department of Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan.
  • Takashi Yao
    Department of Human Pathology, Juntendo University School of Medicine, Tokyo, Japan.
  • Akihito Nagahara
    Department of Gastroenterology, Juntendo University School of Medicine, Tokyo, Japan.
  • Tomohiro Tada
    AI Medical Service Inc Tokyo Japan.