360 Using machine learning to analyze voice and detect aspiration

360 Using machine learning to analyze voice and detect aspiration

Objectives/Goals: Aspiration causes or aggravates lung diseases. While bedside swallow evaluations are not sensitive/specific, gold standard tests for aspiration are invasive, uncomfortable, expose patients to radiation, and are resource intensive. We propose the development and validation of an AI...

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Main Authors: Cyril Varghese, Jianwei Zhang, Sara A. Charney, Abdelmohaymin Abdalla, Stacy Holyfield, Adam Brown, Hunter Stearns, Michelle Higgins, Julie Liss, Nan Zhang, David G. Lott, Victor E. Ortega, Visar Berisha
Format: Article
Language:English
Published: Cambridge University Press 2025-04-01
Series:Journal of Clinical and Translational Science
Online Access:https://www.cambridge.org/core/product/identifier/S2059866124009865/type/journal_article
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https://www.cambridge.org/core/product/identifier/S2059866124009865/type/journal_article

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