AI-Based App for COVID-19 Screening Using Coughing
In the wake of the COVID-19 pandemic, mass coronavirus testing has proven essential to governments in monitoring the spread of the disease, isolating infected individuals, and effectively “flattening the curve” of infections over time . However, this oropharyngeal swab test is physically invasive and must be performed by a trained clinician. This requires patients to travel to a laboratory facility to get tested, thereby potentially infecting others along the way . Ideally, testing would be performed noninvasively at no cost and administered at the homes of potential patients to minimize contamination risk.
The World Health Organization (WHO) has reported that 67.7% of COVID-19 patients exhibit a “dry cough,” meaning that no mucus is produced, unlike the typical “wet cough” that occurs during a cold or allergies . Dry coughs can be distinguished from wet coughs by the sound they produce, which raises the question of whether COVID-19 can be diagnosed by analyzing patients’ cough sounds. Such cough sounds analysis has proven successful in diagnosing respiratory conditions like pertussis , asthma, and pneumonia .
At the Embedded Systems Laboratory (ESL) at EPFL, we propose to leverage signal processing, pervasive computing, and machine learning to develop an Android application and website to automatically screen COVID-19 from the comfort of people’s homes. Test subjects will be able to simply download a mobile application, enter their symptoms, record an audio clip of their cough, and upload the data anonymously to our servers. We will then use state-of-the-art machine learning techniques to classify between cough sounds produced by COVID-19 patients, as opposed to healthy subjects or those with other respiratory conditions.
The application will be finalized at the coming LauzHack Against COVID-19, but in the meantime, we kindly ask that you spread the word about our website, on which people who have been diagnosed with COVID-19 can record the sounds of their coughs: