ArticleBioImpacts : BI2023
Towards modeling of phonation and its recovery in unilateral vocal fold paralysis by fluid-structure interaction.
Article in BioImpacts : BI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed, 1 citations in OpenAlex.
- AI-Driven Lumped-Element Modeling of Human Respiratory System for Studying Voice Mechanics.Research square · 2026Article
- Comprehensive Numerical and Experimental Investigations on Endolaryngeal Mobilization.Laryngoscope investigative otolaryngology · 2026Article
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Vocal folds are responsible for sound generation. In unilateral vocal fold paralysis (UVFP), the recurrent laryngeal nerve, which controls the vocal folds, is paralyzed. Medialization laryngoplasty is a surgery in which an implant is inserted to push the paralyzed vocal fold to the centerline to recover phonation. Methods: Here, a numerical simulation is used to calculate flow-related parameters to give insight into what happens in healthy and treated(implanted) vocal folds and their enhancement. In the present work, airflow over vocal folds is modeled considering fluid-structure interaction (FSI) and varying inlet pressure. The governing equations are discretized for fluid and solid domains and solved using the Galerkin finite element method. The boundary conditions for healthy and unilaterally paralyzed vocal folds were imposed to agree with real cases behavior. Results: The results showed the effectiveness of medialization laryngoplasty in treating unilateral vocal fold paralysis concerning healthy vocal folds. Conclusion: This simulation provided a better insight into treatment results for patient-specific cases.
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