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Related Experiment Video

Updated: Jul 26, 2025

Structured Approach to Colonoscopy Technique Optimization: A Single-Center Experience with Novice Endoscopists
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Automated colonoscopy withdrawal phase duration estimation using cecum detection and surgical tasks classification.

Thomas De Carvalho1,2, Rawen Kader3,4, Patrick Brandao1

  • 1Odin Vision, London, UK.

Biomedical Optics Express
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Summary

This study introduces an automated method to accurately measure colonoscopy withdrawal time, crucial for detecting colorectal cancer polyps. The system precisely identifies the cecum and classifies procedure phases, improving polyp detection rates.

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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Colorectal cancer is a leading global cancer, often preventable by removing polyps during colonoscopy.
  • Missed polyp detection during colonoscopy remains a significant challenge, impacting cancer prevention.
  • Accurate measurement of the colonoscopy withdrawal time is essential for effective polyp detection.

Purpose of the Study:

  • To develop an automated method for precise colonoscopy withdrawal time estimation.
  • To accurately detect the cecum and classify colonoscopy procedure phases.
  • To enhance polyp detection rates by optimizing the exploration phase duration.

Main Methods:

  • Utilized a Resnet model for automated cecum detection and colonoscopy phase classification.
  • Trained the Resnet model using two public and one private dataset comprising 96 full colonoscopy procedures.
  • Evaluated the method on 19 independent testing procedures.

Main Results:

  • The automated system accurately estimated withdrawal time in 18 out of 19 testing procedures.
  • Achieved a mean error of 5.52 seconds per minute in withdrawal time estimation.
  • Demonstrated the feasibility of automatic phase classification for precise time measurement.

Conclusions:

  • Automated cecum detection and phase classification can reliably determine colonoscopy withdrawal time.
  • This method offers a significant improvement over manual time measurement, enhancing procedural efficiency.
  • Potential to improve polyp detection rates and reduce colorectal cancer incidence through optimized colonoscopy protocols.