Frame-by-Frame Analysis of a Commercially Available Artificial Intelligence Polyp Detection System in Full-Length Colonoscopies.

Journal: Digestion
Published Date: Jun 29, 2022

Abstract

INTRODUCTION: Computer-aided detection (CADe) helps increase colonoscopic polyp detection. However, little is known about other performance metrics like the number and duration of false-positive (FP) activations or how stable the detection of a polyp is.

Authors

  • Markus Brand
    Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany.
  • Joel Troya
    Department of Internal Medicine II, Interventional and Experimental Endoscopy (InExEn), University Hospital Wuerzburg, Würzburg, Germany.
  • Adrian Krenzer
    Department of Artificial Intelligence and Knowledge Systems, University of Würzburg, Germany.
  • Costanza De Maria
    Department of Gastroenterology and Hepatology, Ente Ospedaliero Cantonale (EOC), Bellinzona, Switzerland.
  • Niklas Mehlhase
    Department of Internal Medicine I, University Hospital Ulm, Ulm, Germany.
  • Sebastian Götze
    Department of Internal Medicine I, University Hospital Ulm, Ulm, Germany.
  • Benjamin Walter
    Department of Internal Medicine I, University Hospital Ulm, Ulm, Germany.
  • Alexander Meining
    Department of Gastroenterology, University of Würzburg, Würzburg, Germany.
  • Alexander Hann
    Department of Internal Medicine II, Interventional and Experimental Endoscopy (InExEn), University Hospital Wuerzburg, Würzburg, Germany.

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