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Digital Omicron detection using unscripted voice samples from social media

The success of artificial intelligence in clinical environments relies upon the diversity and availability of training data. In some cases, social media data may be used to counterbalance the limited amount of accessible, well-curated clinical data, but this possibility remains largely unexplored. I...

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Detalles Bibliográficos
Autores principales: Anibal, James T., Landa, Adam J., Nguyen, Hang T., Peltekian, Alec K., Shin, Andrew D., Song, Miranda J., Christou, Anna S., Hazen, Lindsey A., Rivera, Jocelyne, Morhard, Robert A., Bagci, Ulas, Li, Ming, Clifton, David A., Wood, Bradford J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9516853/
https://www.ncbi.nlm.nih.gov/pubmed/36172131
http://dx.doi.org/10.1101/2022.09.13.22279673
Descripción
Sumario:The success of artificial intelligence in clinical environments relies upon the diversity and availability of training data. In some cases, social media data may be used to counterbalance the limited amount of accessible, well-curated clinical data, but this possibility remains largely unexplored. In this study, we mined YouTube to collect voice data from individuals with self-declared positive COVID-19 tests during time periods in which Omicron was the predominant variant(1,2,3), while also sampling non-Omicron COVID-19 variants, other upper respiratory infections (URI), and healthy subjects. The resulting dataset was used to train a DenseNet model to detect the Omicron variant from voice changes. Our model achieved 0.85/0.80 specificity/sensitivity in separating Omicron samples from healthy samples and 0.76/0.70 specificity/sensitivity in separating Omicron samples from symptomatic non-COVID samples. In comparison with past studies, which used scripted voice samples, we showed that leveraging the intra-sample variance inherent to unscripted speech enhanced generalization. Our work introduced novel design paradigms for audio-based diagnostic tools and established the potential of social media data to train digital diagnostic models suitable for real-world deployment.