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Related Experiment Video

Updated: Jun 19, 2025

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Impact of User's Background Knowledge and Polyp Characteristics in Colonoscopy with Computer-Aided Detection.

Jooyoung Lee1, Woo Sang Cho2, Byeong Soo Kim2

  • 1Department of Internal Medicine and Healthcare Research Institute, Healthcare System Gangnam Center, Seoul National University Hospital, Seoul, Korea.

Gut and Liver
|July 26, 2024
PubMed
Summary

Computer-aided detection (CADe) systems significantly improve polyp detection accuracy during colonoscopies. However, the benefits of CADe assistance are most pronounced for less experienced users and can be diminished by challenging polyp characteristics.

Keywords:
Artificial intelligenceColonoscopyPolyps

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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Interactions between human endoscopists and computer-aided detection (CADe) systems are crucial for improving colonoscopy outcomes.
  • User experience and polyp characteristics significantly influence the effectiveness of CADe in clinical practice.

Purpose of the Study:

  • To investigate how user experience and polyp characteristics affect the performance of human-CADe interactions.
  • To evaluate the added value of CADe assistance in polyp detection based on histopathology and detection difficulty.

Main Methods:

  • A CADe system utilizing YOLOv4 was developed and trained on a large dataset of polyp and sessile serrated lesion (SSL) images.
  • The performance of CADe-assisted polyp detection was assessed through a computerized test module involving 18 participants with varying levels of colonoscopy experience (nurses, fellows, experts).
  • Statistical analysis, including odds ratios and confidence intervals, was used to quantify the impact of CADe assistance on detection accuracy and sensitivity.

Main Results:

  • CADe assistance improved overall polyp detection accuracy from 69.7% to 77.7% (OR, 1.88).
  • The positive impact of CADe was most significant for nurses (OR, 1.97) and least for experts (OR, 1.42).
  • While CADe enhanced detection of adenomas and easy polyps, it showed decreased sensitivity for SSLs and difficult-to-detect polyps, especially for less experienced users.

Conclusions:

  • CADe systems offer a significant boost in polyp detection rates during colonoscopies.
  • The effectiveness of CADe is modulated by the endoscopist's experience level, with greater benefits observed in less experienced individuals.
  • Careful consideration of CADe system performance with challenging lesions and diverse user expertise is essential for optimizing its clinical utility.