Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence


This study followed-up 616 participants and collected >62 000 coughs showed that artificial intelligence systems can detect changes in cough frequency that temporarily correlate with the onset of clinical disease at the individual level.

This was a prospective cohort study carried out in Pamplona (Spain) between 2020 and 2021 using artificial intelligence cough detection software (followed 616 participants and collected >62 000 coughs). 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.