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Related Experiment Video

Updated: Nov 5, 2025

Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing
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A Deep Learning Enhanced Novel Software Tool for Laryngeal Dynamics Analysis.

Andreas M Kist1, Pablo Gómez1, Denis Dubrovskiy1

  • 1Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology-Head & Neck Surgery, University Hospital Erlangen, Germany.

Journal of Speech, Language, and Hearing Research : JSLHR
|May 17, 2021
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Summary

High-speed videoendoscopy (HSV) software, Glottis Analysis Tools (GAT), enables quantitative analysis of vocal fold oscillations for voice disorder diagnosis. This user-friendly tool offers automatic glottis segmentation and 79 parameters for clinical research and treatment.

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Area of Science:

  • Laryngology
  • Biomedical Engineering
  • Medical Imaging

Background:

  • High-speed videoendoscopy (HSV) is an underutilized clinical tool for voice disorder assessment.
  • Existing methods for analyzing HSV data, particularly glottis segmentation, are limited and not suitable for routine clinical use.
  • Quantifying vocal fold oscillations requires accurate segmentation of the glottal area.

Purpose of the Study:

  • To develop user-friendly software for the analysis of high-speed videoendoscopy (HSV) data.
  • To provide automated glottis segmentation using deep neural networks for clinical application.
  • To extract quantitative parameters from HSV and audio data for voice disorder assessment.

Main Methods:

  • Development of Glottis Analysis Tools (GAT) software in C# for editing, motion correction, segmentation, and analysis of HSV data.
  • Integration of pretrained deep neural networks for fully automatic glottis segmentation in HSV recordings.
  • Implementation of a threshold-based region growing platform for analyzing diverse HSV data sources (in vivo, ex vivo, artificial vocal folds).
  • Parallel evaluation of video and audio data to extract features like the glottal area waveform.

Main Results:

  • The freely available GAT software offers a robust platform for HSV data analysis.
  • Three neural networks provide automatic glottis segmentation for in vivo recordings, suitable for untrained personnel.
  • GAT extracts 79 unique quantitative parameters from video and audio signals.
  • Many extracted parameters correlate with known voice disorders, demonstrating clinical relevance.

Conclusions:

  • Glottis Analysis Tools (GAT) is a unique software solution for processing HSV and audio data.
  • GAT facilitates the determination of quantitative, clinically relevant parameters for laryngeal disorder research, diagnosis, and treatment.
  • The software enhances the clinical utility of high-speed videoendoscopy in voice and laryngeal disorder management.