ArticleScientific reports2023
Localization and quantification of glottal gaps on deep learning segmentation of vocal folds.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 16 citations in OpenAlex.
- The Laryngovibrogram as a normalized spatiotemporal representation of vocal fold dynamics.Scientific reports · 2025Article
- Vocal registers expand signal diversity in vertebrate vocal communication.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- GIRAFE: Glottal imaging dataset for advanced segmentation, analysis, and facilitative playbacks evaluation.Data in brief · 2025Review
- A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method.Scientific reports · 2024Article
- Development and Application of Automated Vocal Fold Tracking Software in a Rat Surgical Model.The Laryngoscope · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The entire glottis has mostly been the focus in the tracking of the vocal folds, both manually and automatically. From a treatment point of view, the various regions of the glottis are of specific interest. The aim of the study was to test if it was possible to supplement an existing convolutional neural network (CNN) with post-network calculations for the localization and quantification of posterior glottal gaps during phonation, usable for vocal fold function analysis of e.g. laryngopharyngeal reflux findings. 30 subjects/videos with insufficient closure in the rear glottal area and 20 normal subjects/videos were selected from our database, recorded with a commercial high-speed video setup (HSV with 4000 frames per second), and segmented with an open-source CNN for validating voice function. We made post-network calculations to localize and quantify the 10% and 50% distance lines from the rear part of the glottis. The results showed a significant difference using the algorithm at the 10% line distance between the two groups of p < 0.0001 and no difference at 50%. These novel results show that it is possible to use post-network calculations on CNNs for the localization and quantification of posterior glottal gaps.
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