ArticleScientific reports2020
Machine learning based identification of relevant parameters for functional voice disorders derived from endoscopic high-speed recordings.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Diagnostic Accuracy of Artificial Intelligence in Laryngeal Disorders: An Integrative Review.Journal of personalized medicine · 2026Review
- Glottal Area Waveform Measurements for Healthy Female and Male Speakers in Typical, High-Frequency, and Soft Phonation.Journal of speech, language, and hearing research : JSLHR · 2026Article
- Objective and Subjective Voice Outcomes in Post-COVID-19 Dysphonia: A High-Speed Videoendoscopy Pre-Post Study.Journal of clinical medicine · 2025Article
- GIRAFE: Glottal imaging dataset for advanced segmentation, analysis, and facilitative playbacks evaluation.Data in brief · 2025Review
- Machine learning based assessment of hoarseness severity: a multi-sensor approach centered on high-speed videoendoscopy.Frontiers in artificial intelligence · 2025Article
- A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method.Scientific reports · 2024Article
- Deep Learning-Based Detection of Glottis Segmentation Failures.Bioengineering (Basel, Switzerland) · 2024Article
- Register transitions in an in vivo canine model as a function of intrinsic laryngeal muscle stimulation, fundamental frequency, and sound pressure level.The Journal of the Acoustical Society of America · 2024Article
- Value of high-speed videoendoscopy as an auxiliary tool in differentiation of benign and malignant unilateral vocal lesions.Journal of cancer research and clinical oncology · 2024Article
- Influence of flow rate and fiber tension on dynamical, mechanical and acoustical parameters in a synthetic larynx model with integrated fibers.Frontiers in physiology · 2024Article
- Effect of Ligament Fibers on Dynamics of Synthetic, Self-Oscillating Vocal Folds in a Biomimetic Larynx Model.Bioengineering (Basel, Switzerland) · 2023Article
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- Predicting 3D soft tissue dynamics from 2D imaging using physics informed neural networks.Communications biology · 2023Article
- Localization and quantification of glottal gaps on deep learning segmentation of vocal folds.Scientific reports · 2023Article
- Article
- Artificial intelligence in clinical endoscopy: Insights in the field of videomics.Frontiers in surgery · 2022Review
- Analysis of vibratory mode changes in symmetric and asymmetric activation of the canine larynx.PloS one · 2022Article
- OpenHSV: an open platform for laryngeal high-speed videoendoscopy.Scientific reports · 2021Article
- A Portable Smartphone-Based Laryngoscope System for High-Speed Vocal Cord Imaging of Patients With Throat Disorders: Instrument Validation Study.JMIR mHealth and uHealth · 2021Article
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In voice research and clinical assessment, many objective parameters are in use. However, there is no commonly used set of parameters that reflect certain voice disorders, such as functional dysphonia (FD); i.e. disorders with no visible anatomical changes. Hence, 358 high-speed videoendoscopy (HSV) recordings (159 normal females (N
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.