Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy.

Journal: Gastroenterology
Published Date: Jun 18, 2018

Abstract

BACKGROUND & AIMS: The benefit of colonoscopy for colorectal cancer prevention depends on the adenoma detection rate (ADR). The ADR should reflect the adenoma prevalence rate, which is estimated to be higher than 50% in the screening-age population. However, the ADR by colonoscopists varies from 7% to 53%. It is estimated that every 1% increase in ADR lowers the risk of interval colorectal cancers by 3%-6%. New strategies are needed to increase the ADR during colonoscopy. We tested the ability of computer-assisted image analysis using convolutional neural networks (CNNs; a deep learning model for image analysis) to improve polyp detection, a surrogate of ADR.

Authors

  • Gregor Urban
    Department of Computer Science, University of California, Irvine , Irvine, California 92697, United States.
  • Priyam Tripathi
    Department of Medicine, University of California, Irvine, California.
  • Talal Alkayali
    Department of Medicine, University of California, Irvine, California; H.H. Chao Comprehensive Digestive Disease Center, University of California, Irvine, California.
  • Mohit Mittal
    Department of Medicine, University of California, Irvine, California.
  • Farid Jalali
    Department of Medicine, University of California, Irvine, California; H.H. Chao Comprehensive Digestive Disease Center, University of California, Irvine, California.
  • William Karnes
    Department of Medicine, University of California, Irvine, California; H.H. Chao Comprehensive Digestive Disease Center, University of California, Irvine, California.
  • Pierre Baldi
    Department of Computer Science, Department of Biological Chemistry, University of California-Irvine, Irvine, CA 92697, USA.

Keywords

External Resources

Popular Topics
Recent Journals