Grant: Op. Gr.:Emer. COVID Res. Gaps/Priorities-Core Public Health and Control Measures

Cough is a key symptom of respiratory diseases, including COVID-19. The ongoing COVID-19 pandemic has accelerated advancements in the field of digital cough monitoring using artificial intelligence (AI). Prior studies and AI models have shown that AI can identify human coughs from ambient sounds (cough detection) and can potentially differentiate coughs caused by different diseases (cough classification). For example, there is a promising smartphone application named Hyfe Research that uses AI to detect human cough, with more than 97% accuracy. Such AI models can be used on smartphones, allowing for non-invasive, easy to use tools. In this study, we will develop and evaluate a cough classification AI model which can be used on smartphones to differentiate COVID-19 coughs from coughs caused by other diseases (e.g., influenza). In a case-control study design, coughing patients with confirmed COVID-19 infection and negative controls will be recruited at the Centre Hospitalier de l'Université de Montréal (CHUM) in Montreal, Canada (close to 500 participants in total). Clinical and demographic information will be collected, and ten coughs will be recorded using the Hyfe Research app. Using these cough sounds, we will train algorithms to differentially identify COVID-19 coughs from non-COVID-19 coughs and compare how well the algorithm performs against the laboratory reference standard. We will also conduct in-depth interviews with patients and healthcare providers to understand the feasibility and acceptability of smartphone-based cough recording in a clinical setting. This study will contribute to a global database of COVID-19 cough sounds. The development of a reliable AI application for cough detection could improve COVID-19 screening strategies, and thus mitigate future infections and outbreaks in Canada, and around the world.