Identifying bias in models that detect vocal fold paralysis from audio recordings using explainable machine learning and clinician ratings

INTRODUCTION. Detecting voice disorders from voice recordings could allow for frequent, remote, and low-cost screening before costly clinical visits and a more invasive laryngoscopy examination. Our goals were to detect unilateral vocal fold paralysis (UVFP) from voice recordings using machine learn...

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Detalles Bibliográficos
Autores principales: Low, Daniel M., Rao, Vishwanatha, Randolph, Gregory, Song, Phillip C., Ghosh, Satrajit S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7836138/
https://www.ncbi.nlm.nih.gov/pubmed/33501466
http://dx.doi.org/10.1101/2020.11.23.20235945

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7836138/
https://www.ncbi.nlm.nih.gov/pubmed/33501466
http://dx.doi.org/10.1101/2020.11.23.20235945

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