Artificial intelligence and polyp detection in colonoscopy: Use of a single neural network to achieve rapid polyp localization for clinical use.

Journal: Journal of gastroenterology and hepatology
Published Date:

Abstract

BACKGROUND AND AIM: Artificial intelligence has been extensively studied to assist clinicians in polyp detection, but such systems usually require expansive processing power, making them prohibitively expensive and hindering wide adaption. The current study used a fast object detection algorithm, known as the YOLOv3 algorithm, to achieve real-time polyp detection on a laptop. In addition, we evaluated and classified the causes of false detections to further improve accuracy.

Authors

  • James Weiquan Li
    Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore.
  • Tiongsun Chia
    GI Tech Ltd., Singapore.
  • Kwong Ming Fock
    Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore.
  • Kenny De Wei Chong
    GI Tech Ltd., Singapore.
  • Yu Jun Wong
    Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore.
  • Tiing Leong Ang
    Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore.