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dc.contributor.author Zimmer, Alexandra J.
dc.contributor.author Ugarte Gil, Cesar Augusto
dc.contributor.author Pathri, Rahul
dc.contributor.author Dewan, Puneet
dc.contributor.author Jaganath, Devan
dc.contributor.author Cattamanchi, Adithya
dc.contributor.author Pai, Madhukar
dc.contributor.author Grandjean Lapierre, Simon
dc.date.accessioned 2022-08-15T20:11:09Z
dc.date.available 2022-08-15T20:11:09Z
dc.date.issued 2022
dc.identifier.uri https://hdl.handle.net/20.500.12866/12037
dc.description.abstract Cough assessment is central to the clinical management of respiratory diseases, including tuberculosis (TB), but strategies to objectively and unobtrusively measure cough are lacking. Acoustic epidemiology is an emerging field that uses technology to detect cough sounds and analyze cough patterns to improve health outcomes among people with respiratory conditions linked to cough. This field is increasingly exploring the potential of artificial intelligence (AI) for more advanced applications, such as analyzing cough sounds as a biomarker for disease screening. While much of the data are preliminary, objective cough assessment could potentially transform disease control programs, including TB, and support individual patient management. Here, we present an overview of recent advances in this field and describe how cough assessment, if validated, could support public health programs at various stages of the TB care cascade. en_US
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartofseries Communications Medicine
dc.rights info:eu-repo/semantics/restrictedAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject Tuberculosis en_US
dc.subject Diagnostic markers en_US
dc.subject Prognostic markers en_US
dc.title Making cough count in tuberculosis care. en_US
dc.type info:eu-repo/semantics/article
dc.identifier.doi https://doi.org/10.1038/s43856-022-00149-w
dc.subject.ocde https://purl.org/pe-repo/ocde/ford#3.02.07
dc.relation.issn 2730-664X


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