ArticleApplied sciences (Basel, Switzerland)2019
Bayesian Inference of Vocal Fold Material Properties from Glottal Area Waveforms Using a 2D Finite Element Model.
Article in Applied sciences (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Subject-Specific Modeling by Domain Adaptation for the Estimation of Subglottal Pressure from Neck-Surface Acceleration Signals.Biomedical signal processing and control · 2025Article
- Sensitivity of Phonation Onset Pressure to Vocal Fold Stiffness Distribution.Journal of biomechanical engineering · 2024Article
- Synthetic, self-oscillating vocal fold models for voice production researcha).The Journal of the Acoustical Society of America · 2024Review
- The effect of swelling on vocal fold kinematics and dynamics.Biomechanics and modeling in mechanobiology · 2023Article
- Voice Feature Selection to Improve Performance of Machine Learning Models for Voice Production Inversion.Journal of voice : official journal of the Voice Foundation · 2023Article
- Predicting 3D soft tissue dynamics from 2D imaging using physics informed neural networks.Communications biology · 2023Article
- Vocal fold dynamics in a synthetic self-oscillating model: Contact pressure and dissipated-energy dose.The Journal of the Acoustical Society of America · 2021Article
- The influence of flow model selection on finite element model parameter estimation using Bayesian inference.JASA express letters · 2021Article
- A one-dimensional flow model enhanced by machine learning for simulation of vocal fold vibration.The Journal of the Acoustical Society of America · 2021Article
- Numerical and experimental investigations on vocal fold approximation in healthy and simulated unilateral vocal fold paralysis.Applied sciences (Basel, Switzerland) · 2021Article
- Estimation of Subglottal Pressure, Vocal Fold Collision Pressure, and Intrinsic Laryngeal Muscle Activation From Neck-Surface Vibration Using a Neural Network Framework and a Voice Production Model.Frontiers in physiology · 2021Article
- Bayesian estimation of vocal function measures using laryngeal high-speed videoendoscopy and glottal airflow estimates: An in vivo case study.The Journal of the Acoustical Society of America · 2020Article
- Estimation of vocal fold physiology from voice acoustics using machine learning.The Journal of the Acoustical Society of America · 2020Article
- Toward Development of a Vocal Fold Contact Pressure Probe: Bench-Top Validation of a Dual-Sensor Probe Using Excised Human Larynx Models.Applied sciences (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Bayesian estimation has been previously demonstrated as a viable method for developing subject-specific vocal fold models from observations of the glottal area waveform. These prior efforts, however, have been restricted to lumped-element fitting models and synthetic observation data. The indirect relationship between the lumped-element parameters and physical tissue properties renders extracting the latter from the former difficult. Herein we propose a finite element fitting model, which treats the vocal folds as a viscoelastic deformable body comprised of three layers. Using the glottal area waveforms generated by self-oscillating silicone vocal folds we directly estimate the elastic moduli, density, and other material properties of the silicone folds using a Bayesian importance sampling approach. Estimated material properties agree with the "ground truth" experimental values to within 3% for most parameters. By considering cases with varying subglottal pressure and medial compression we demonstrate that the finite element model coupled with Bayesian estimation is sufficiently sensitive to distinguish between experimental configurations. Additional information not available experimentally, namely, contact pressures, are extracted from the developed finite element models. The contact pressures are found to increase with medial compression and subglottal pressure, in agreement with expectation.
Indexed as
Identifiers
What OpenQuestion holds
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.