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Related Experiment Video

Updated: Jul 20, 2025

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Direct comparison of multiple computer-aided polyp detection systems.

Joel Troya1,2, Boban Sudarevic1,3, Adrian Krenzer4

  • 1Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany.

Endoscopy
|August 2, 2023
PubMed
Summary
This summary is machine-generated.

Performance of artificial intelligence (AI) computer-aided detection (CADe) systems for polyps varied. Clinicians can use this comparison to select the best AI CADe system for their needs.

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Gastroenterology technology

Background:

  • Artificial intelligence (AI) systems for computer-aided detection (CADe) of polyps are updated regularly, impacting performance.
  • Customizable detection thresholds in AI CADe systems also affect their efficacy.
  • Limited data exists on how these updates and settings influence AI CADe system performance.

Purpose of the Study:

  • To compare the performance of different AI CADe systems using a standardized benchmark dataset.
  • To evaluate the impact of system updates and configuration modes on polyp detection rates.

Main Methods:

  • Utilized 101 colonoscopy videos as a benchmark dataset.
  • Manually annotated 129,705 polyp images from video frames.
  • Analyzed videos using three distinct AI CADe systems (GI Genius, Endo-AID, EndoMind) under various conditions.

Main Results:

  • Endo-AID Type A, early GI Genius, and EndoMind detected all 93 polyps.
  • Late GI Genius and Endo-AID Type B missed one polyp each.
  • Per-frame sensitivities ranged from 50.63% to 67.85% across systems and versions.

Conclusions:

  • The study highlights performance variations among different AI CADe systems, versions, and configurations.
  • Findings can assist clinicians in choosing the most suitable AI CADe system for specific clinical applications.