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Artificial intelligence alert system based on intraluminal view for colonoscopy intubation.

Yigeng Huang1,2, Suwen Li3,4, Syeda Sadia Rubab1,2

  • 1State Key Laboratory of Transducer Technology, Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.

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Summary

Artificial intelligence (AI) can now quantify colonoscopy red-out views, a common issue that can lead to perforation. This AI system effectively assesses red-out avoidance skills, aiding in safer and more efficient colonoscopy training.

Keywords:
Artificial intelligenceColonoscopyFeedbackIntubation

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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Colonoscopy intubation can cause mucosal injury, visualized as "red-out" views, increasing perforation risk.
  • Quantitative methods for assessing red-out views during colonoscopy are currently lacking.
  • Developing objective assessment tools is crucial for improving colonoscopy safety and training.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI)-based system for quantifying red-out views during colonoscopy intubation.
  • To assess the correlation between AI-derived red-out avoidance scores and endoscopist experience levels.
  • To evaluate the utility of the AI system as a training tool for colonoscopists.

Main Methods:

  • A semi-supervised AI system was developed to analyze 479 colonoscopies from 34 colonoscopists.
  • AI-based red-out avoiding scores were compared across novice, intermediate, and experienced endoscopist groups.
  • Scores were also compared against expert-rated Direct Observation of Procedure or Skill (DOPS)-based tip control assessments.

Main Results:

  • Significant differences in actual red-out view percentages and AI-based red-out avoiding scores were observed across experience levels (p < 0.001).
  • Endoscopists with higher DOPS tip control scores demonstrated better AI-based red-out avoidance.
  • AI red-out avoiding scores showed a negative correlation with cecal intubation time and actual red-out percentage.

Conclusions:

  • The AI-based system provides a quantitative measure of red-out avoidance during colonoscopy.
  • This AI tool can differentiate skill levels and correlate with established assessment methods.
  • Feedback from the AI system can enhance colonoscopy training, promoting safer and more effective procedures.