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Development of a convolutional neural network-based AI-assisted multi-task colonoscopy withdrawal quality control

Jian Chen1,2, Menglin Zhu1, Zhijia Shen3

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This study introduces EWT-SpeedNet, an AI system for colonoscopy withdrawal quality control. It accurately monitors withdrawal speed, total withdrawal time, and effective withdrawal time, enhancing colorectal cancer screening.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Gastroenterology

Background:

  • Colonoscopy is vital for colorectal cancer screening and diagnosis.
  • The withdrawal phase is critical for mucosal inspection and lesion detection.
  • Current methods lack standardized quality control for the withdrawal phase.

Purpose of the Study:

  • To develop an AI system for multitask withdrawal quality control in colonoscopy.
  • To monitor withdrawal speed, total withdrawal time, and effective withdrawal time.
  • To improve the adequacy of mucosal inspection and lesion detection rates.

Main Methods:

  • Utilized a convolutional neural network (YOLOv11) with transfer learning on colonoscopy data.
  • Annotated images/videos for ileocecal intubation, instrument operation, and normal mucosa.
  • Integrated Laplacian operator for blur detection and perceptual hash algorithm for speed monitoring.

Main Results:

  • YOLOv11 m achieved 96.58% weighted average precision and 0.9975 AUC on the test set.
  • The AI system demonstrated high consistency with expert endoscopists in measuring effective withdrawal time (ICC = 0.969).
  • EWT-SpeedNet provides real-time speed visualization and automatic calculation of withdrawal times.

Conclusions:

  • The developed EWT-SpeedNet system effectively supports standardized and efficient colonoscopy withdrawal monitoring.
  • AI-driven quality control can enhance the reliability of colonoscopy procedures.
  • This system has the potential to improve colorectal cancer detection rates through optimized colonoscopy quality.