TY - JOUR T1 - Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence JF - ERJ Open Research JO - erjor DO - 10.1183/23120541.00053-2022 VL - 8 IS - 2 SP - 00053-2022 AU - Juan C. Gabaldón-Figueira AU - Eric Keen AU - Gerard Giménez AU - Virginia Orrillo AU - Isabel Blavia AU - Dominique Hélène Doré AU - Nuria Armendáriz AU - Juliane Chaccour AU - Alejandro Fernandez-Montero AU - Javier Bartolomé AU - Nita Umashankar AU - Peter Small AU - Simon Grandjean Lapierre AU - Carlos Chaccour Y1 - 2022/04/01 UR - http://openres.ersjournals.com/content/8/2/00053-2022.abstract N2 - 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 ER -