Leveraging large language models for automated detection of velopharyngeal dysfunction in patients with cleft palate

Leveraging large language models for automated detection of velopharyngeal dysfunction in patients with cleft palate

BackgroundHypernasality, a hallmark of velopharyngeal insufficiency (VPI), is a speech disorder with significant psychosocial and functional implications. Conventional diagnostic methods rely heavily on specialized expertise and equipment, posing challenges in resource-limited settings. This study e...

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Bibliographic Details
Main Authors: Myranda Uselton Shirk, Catherine Dang, Jaewoo Cho, Hanlin Chen, Lily Hofstetter, Jack Bijur, Claiborne Lucas, Andrew James, Ricardo-Torres Guzman, Andrea Hiller, Noah Alter, Amy Stone, Maria Powell, Matthew E. Pontell
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-03-01
Series:Frontiers in Digital Health
Subjects:
velopharyngeal dysfunction (VPD)
hypernasality detection
artificial intelligence (AI)
cleft palate
machine learning (ML)
speech diagnostics
Online Access:https://www.frontiersin.org/articles/10.3389/fdgth.2025.1552746/full
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