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An Open-Source Computer Vision Tool for Automated Vocal Fold Tracking From Videoendoscopy.

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Summary

A new AI tool accurately quantifies vocal fold motion from videos, aiding in diagnosing unilateral vocal fold paralysis. This automated glottic action tracking by artificial intelligence (AGATI) offers objective assessment for vocal fold movement disorders.

Keywords:
Vocal cords, vocal cord paralysis, artificial intelligence, outcome assessment (health care), laryngoscopy, dysphonia

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Area of Science:

  • Otolaryngology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Current clinical assessment of vocal fold adduction and abduction is subjective.
  • Objective, quantitative methods are needed for diagnosing vocal fold movement disorders.

Purpose of the Study:

  • To develop and validate a novel computer vision tool for automated, quantitative tracking of vocal fold motion.
  • To assess the utility of this software as a diagnostic aid for unilateral vocal fold paralysis.

Main Methods:

  • A deep-learning algorithm was developed to estimate glottic opening angles from videolaryngoscopy.
  • The algorithm's accuracy was validated against manual expert markings.
  • Maximal glottic opening angles were compared between healthy adults and patients with unilateral vocal fold paralysis.

Main Results:

  • The AI tool demonstrated high accuracy, with a correlation coefficient of 0.97 compared to manual markings.
  • Patients with unilateral vocal fold paralysis exhibited significantly lower maximal glottic opening angles (49.44°) than controls (68.75°).
  • A threshold of <58.65° for maximal opening angle predicted unilateral vocal fold paralysis with 85% sensitivity and 85% specificity.

Conclusions:

  • Automated Glottic Action Tracking by Artificial Intelligence (AGATI) provides a user-friendly, automated method for quantifying vocal fold movement.
  • This AI-powered tool shows significant potential for improving the diagnosis and outcomes tracking of vocal fold movement disorders.