RT Journal Article
SR Electronic
T1 Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence
JF ERJ Open Research
JO erjor
FD European Respiratory Society
SP 00053-2022
DO 10.1183/23120541.00053-2022
VO 8
IS 2
A1 Gabaldón-Figueira, Juan C.
A1 Keen, Eric
A1 Giménez, Gerard
A1 Orrillo, Virginia
A1 Blavia, Isabel
A1 Doré, Dominique Hélène
A1 Armendáriz, Nuria
A1 Chaccour, Juliane
A1 Fernandez-Montero, Alejandro
A1 Bartolomé, Javier
A1 Umashankar, Nita
A1 Small, Peter
A1 Grandjean Lapierre, Simon
A1 Chaccour, Carlos
YR 2022
UL http://openres.ersjournals.com/content/8/2/00053-2022.abstract
AB 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.Artificial intelligence software installed in smartphones can detect changes in cough frequency associated with medical consultations. With adequate uptake and use, these tools could help detect the onset of respiratory disease in a population. https://bit.ly/3qSuaIV