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Related Experiment Video

Updated: Sep 8, 2025

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing
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Feasibility of Real-Time Automated Vocal Fold Motion Tracking for In-Office Laryngoscopy.

Aki Koivu1,2, Obinna I Nwosu1,2, Mitsuki Ota3

  • 1Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye & Ear, Boston, Massachusetts, USA.

The Laryngoscope
|September 4, 2025
PubMed
Summary

This study developed a real-time AI model for automated vocal fold tracking during laryngoscopy. The system accurately tracks laryngeal structures, offering a feasible solution for clinical use.

Keywords:
artificial intelligencevideolaryngoscopyvocal fold tracking

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Area of Science:

  • Medical imaging
  • Artificial intelligence
  • Laryngology

Background:

  • Advancements in AI and computer vision for videolaryngoscopy analysis.
  • Current models are limited to post hoc analysis and research settings.

Purpose of the Study:

  • To assess the feasibility of a real-time solution for automated vocal fold tracking during in-office laryngoscopy.

Main Methods:

  • Trained a keypoint detection model to track 39 laryngeal keypoints on 1254 video frames.
  • Evaluated model performance using mean keypoint accuracy (mKA) and temporal consistency (mTC) on validation and test datasets.
  • Deployed the model in real-time using a flexible laryngoscope feed and a laptop.

Main Results:

  • Achieved robust detection accuracy with mKA of 85% (validation) and 75% (test).
  • Demonstrated promising temporal consistency with an average of 5.85 pixel movement.
  • Operated at 30 frames per second with minimal latency, not affecting clinical workflow.

Conclusions:

  • The AI model accurately tracks laryngeal structures with high detection, localization, and consistency.
  • The system is compatible with affordable hardware, enabling real-time metric development.
  • This represents a feasible real-time solution for clinical laryngoscopy.