Prediction of Polyp Pathology Using Convolutional Neural Networks Achieves "Resect and Discard" Thresholds.

Journal: The American journal of gastroenterology
Published Date: Jan 1, 2020

Abstract

OBJECTIVES: Reliable in situ diagnosis of diminutive (≤5 mm) colorectal polyps could allow for "resect and discard" and "diagnose and leave" strategies, resulting in $1 billion cost savings per year in the United States alone. Current methodologies have failed to consistently meet the Preservation and Incorporation of Valuable endoscopic Innovations (PIVIs) initiative thresholds. Convolutional neural networks (CNNs) have the potential to predict polyp pathology and achieve PIVI thresholds in real time.

Authors

  • Robin Zachariah
    Department of Gastroenterology and Department of Internal Medicine, University of California Irvine Medical Center, Orange, California, USA.
  • Jason Samarasena
    Department of Gastroenterology and Department of Internal Medicine, University of California Irvine Medical Center, Orange, California, USA.
  • Daniel Luba
    Monterey Bay GI Consultants Medical Group, Monterey, California, USA.
  • Erica Duh
    Department of Gastroenterology and Department of Internal Medicine, University of California Irvine Medical Center, Orange, California, USA.
  • Tyler Dao
    Docbot Inc, Irvine, California, USA.
  • James Requa
    Docbot Inc, Irvine, California, USA.
  • Andrew Ninh
    Docbot Inc, Irvine, California, USA.
  • William Karnes
    Department of Medicine, University of California, Irvine, California; H.H. Chao Comprehensive Digestive Disease Center, University of California, Irvine, California.

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