Registro completo de metadatos
Campo DC | Valor | Lengua/Idioma |
---|---|---|
dc.creator | Gabaldón-Figueira, J.C. (Juan C.) | - |
dc.creator | Keen, E. (Erik) | - |
dc.creator | Giménez, G. (Gérard) | - |
dc.creator | Orrillo, V. (Virginia) | - |
dc.creator | Blavia, I. (Isabel) | - |
dc.creator | Doré, D.H. (Dominique Hélène) | - |
dc.creator | Armendáriz, N. (Nuria) | - |
dc.creator | Chaccour, J. (Juliane) | - |
dc.creator | Fernandez-Montero, A. (Alejandro) | - |
dc.creator | Bartolome, J. (Javier) | - |
dc.creator | Forouhi, N.G. (Nita G.) | - |
dc.creator | Small, P. (Peter) | - |
dc.creator | Grandjean-Lapierre, S. (Simon) | - |
dc.creator | Chaccour, C.J. (Carlos J.) | - |
dc.date.accessioned | 2022-08-11T09:46:22Z | - |
dc.date.available | 2022-08-11T09:46:22Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Gabaldón-Figueira, J. C.; Keen, E.; Giménez, G.; et al. "Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence". ERJ open research. 8 (2), 2022, 053 | es |
dc.identifier.issn | 2312-0541 | - |
dc.identifier.uri | https://hdl.handle.net/10171/63903 | - |
dc.description.abstract | Research question Can smartphones be used to detect individual and population-level changes in cough frequency that correlate with the incidence of coronavirus disease 2019 (COVID-19) and other respiratory infections? Methods This was a prospective cohort study carried out in Pamplona (Spain) between 2020 and 2021 using artificial intelligence cough detection software. Changes in cough frequency around the time of medical consultation were evaluated using a randomisation routine; significance was tested by comparing the distribution of cough frequencies to that obtained from a model of no difference. The correlation between changes of cough frequency and COVID-19 incidence was studied using an autoregressive moving average analysis, and its strength determined by calculating its autocorrelation function (ACF). Predictors for the regular use of the system were studied using a linear regression. Overall user experience was evaluated using a satisfaction questionnaire and through focused group discussions. Results We followed-up 616 participants and collected >62 000 coughs. Coughs per hour surged around the time cohort subjects sought medical care (difference +0.77 coughs.h(-1); p=0.00001). There was a weak temporal correlation between aggregated coughs and the incidence of COVID-19 in the local population (ACF 0.43). Technical issues affected uptake and regular use of the system. Interpretation Artificial intelligence systems can detect changes in cough frequency that temporarily correlate with the onset of clinical disease at the individual level. A clearer correlation with population-level COVID-19 incidence, or other respiratory conditions, could be achieved with better penetration and compliance with cough monitoring. | - |
dc.description.sponsorship | This study was funded by the Patrick J. McGovern Foundation (grant name: “Early diagnosis of COVID-19 by utilising Artificial Intelligence and Acoustic Monitoring”). S. Grandjean Lapierre received salary support from the Fonds de Recherche en Santé Québec. ISGlobal acknowledges support from the Spanish Ministry of Science and Innovation through the “Centro de Excelencia Severo Ochoa 2019–2023” Programme (grant number: CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA programme. Funding information for this article has been deposited with the Crossref Funder Registry. | - |
dc.language.iso | en | - |
dc.rights | info:eu-repo/semantics/openAccess | - |
dc.subject | Respiratory infections | - |
dc.subject | Respiratory disease | - |
dc.subject | Artificial intelligence | - |
dc.subject | Smartphones | - |
dc.subject | Coronavirus | - |
dc.subject | COVID-19 | - |
dc.subject | Cough frequency | - |
dc.title | Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence | - |
dc.type | info:eu-repo/semantics/article | - |
dc.relation.publisherversion | https://pubmed.ncbi.nlm.nih.gov/35651361/ | - |
dc.description.note | This version is distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0. | - |
dc.identifier.doi | 10.1183/23120541.00053-2022 | - |
dadun.citation.endingPage | 9 | - |
dadun.citation.number | 00053 | - |
dadun.citation.publicationName | ERJ open research | - |
dadun.citation.startingPage | 1 | - |
dadun.citation.volume | 8 | - |
Ficheros en este ítem:
Estadísticas e impacto
Los ítems de Dadun están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.