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Predicting semantic segmentation quality in laryngeal endoscopy images.

Andreas M Kist1, Sina Razi1, René Groh1

  • 1Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Bavaria, Germany.

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This study introduces an AI system for evaluating laryngeal endoscopy image segmentation quality. The system matches human performance, identifying problematic frames for clinical adaptation of AI analysis.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Otolaryngology

Background:

  • Endoscopy is crucial for assessing internal organ physiology.
  • Artificial intelligence (AI) enables semantic segmentation for medical image analysis, aiding in tasks like cancer detection and laryngeal physiology assessment.
  • Assessing the quality of AI-driven semantic segmentation is vital due to patient diversity.

Purpose of the Study:

  • To present a fully automatic system for evaluating segmentation performance in laryngeal endoscopy images.
  • To demonstrate the system's capability in assessing glottal area segmentation quality.
  • To facilitate human-in-the-loop improvements for clinical adaptation of AI analysis procedures.

Main Methods:

  • Development of a fully automatic system to evaluate segmentation performance.
  • Application of the system to glottal area segmentation in laryngeal endoscopy images.
  • Utilizing a traffic light system to identify problematic segmentation frames.

Main Results:

  • The system's predicted segmentation quality, measured by the intersection over union (IoU) metric, is comparable to human raters.
  • Problematic segmentation frames can be effectively identified using the traffic light system.
  • The developed system aids in quality control for AI-based laryngeal image analysis.

Conclusions:

  • The automatic system provides reliable segmentation quality evaluation for laryngeal endoscopy.
  • The findings support the clinical integration of AI in laryngeal physiology assessment.
  • Human-in-the-loop quality control is essential for robust AI application in medical procedures.