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A prospective study evaluating an artificial intelligence-based system for withdrawal time measurement.

Ioannis Kafetzis1, Philipp Sodmann1, Bianca-Elena Herghelegiu1

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Artificial intelligence (AI) significantly improves colonoscopy withdrawal time calculation accuracy compared to physicians, especially during interventions. This AI system offers a promising solution for standardizing this critical colorectal cancer screening quality measure.

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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Withdrawal time is a key quality indicator in colonoscopy for colorectal cancer screening.
  • Current manual calculation methods for withdrawal time exhibit high variability.
  • Prospective validation of artificial intelligence (AI) for standardizing withdrawal time is needed.

Purpose of the Study:

  • To prospectively compare the accuracy of AI-assisted withdrawal time calculation against physician performance.
  • To evaluate the AI system's ability to standardize withdrawal time measurement in colonoscopy.

Main Methods:

  • A prospective superiority trial was conducted from December 2023 to March 2024.
  • AI-assisted and physician withdrawal times were compared against a gold standard from manual video annotation.
  • AI-generated image reports were qualitatively assessed by endoscopists.

Main Results:

  • The AI system showed a significantly lower mean absolute error (MAE) in estimating withdrawal time (2.2 min) versus physicians (4.2 min; P < 0.001).
  • AI accuracy was particularly superior in colonoscopies with endoscopic interventions (MAE 2.1 min vs. 5.2 min; P < 0.001).
  • AI generated high-quality image reports, with 97% satisfactory timeline representation and 81% overall satisfaction.

Conclusions:

  • The AI system demonstrated superiority in calculating colonoscopy withdrawal time compared to physicians.
  • AI significantly enhances accuracy, especially in complex colonoscopies involving interventions.
  • This AI application shows potential for streamlining clinical workflows in endoscopy.