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Area of Science:

  • Laryngology
  • Medical Imaging
  • Computational Biology

Background:

  • A healthy voice relies on symmetric vocal fold oscillation in the larynx.
  • Current clinical assessment uses subjective videoendoscopy.
  • High-speed videoendoscopy offers quantification but requires complex analysis.

Purpose of the Study:

  • To evaluate methods for fully automatic glottal midline detection.
  • To develop a deep learning approach for automated laryngeal endoscopy analysis.
  • To improve the clinical applicability of quantitative vocal fold oscillation assessment.

Main Methods:

  • Utilized a biophysical model to simulate vocal fold oscillations.
  • Extended the BAGLS dataset with manual annotations.
  • Trained deep neural networks on simulated and real endoscopic data.
  • Compared deep learning models against traditional computer vision algorithms.

Main Results:

  • Deep neural networks outperformed classical computer vision for glottal midline detection.
  • GlottisNet, a multi-task network, simultaneously predicts vocal fold opening and symmetry axis.
  • Fully automated segmentation and midline detection were achieved.

Conclusions:

  • Deep learning significantly improves glottal midline detection accuracy.
  • GlottisNet represents a major advancement for quantitative, deep learning-assisted laryngeal endoscopy.
  • Automated analysis paves the way for wider clinical adoption of objective voice assessment.