Researchers at Cambridge University have launched a new app to collect data and sounds of people with COVID-19 to then explore the potential to use machine learning and algorithms to detect the condition.
The aim of the research is to inform the diagnosis of COVID-19, by developing machine learning algorithms, based primarily on sounds; breathing and coughing.
The app will collect some basic demographics and medical history data, as well as some voice samples.
Professor Cecilia Mascolo from Cambridge’s Department of Computer Science and Technology said “There’s still so much we don’t know about this virus and the illness it causes, and in a pandemic situation like the one we’re currently in, the more reliable information you can get, the better.”
As COVID-19 is a respiratory condition, the sounds made by people with the condition – including voice, breathing and cough sounds – are very specific. The researchers said a large, crowdsourced data set will be useful in developing machine learning algorithms that could be used for automatic detection of the condition.
Once they have completed their initial analysis of the data collected by the app, the team will release the dataset to other researchers. The dataset could help shed light on disease progression, further relationship of the respiratory complication with medical history, for example.
Professor Mascolo continued “Having spoken to doctors, one of the most common things they have noticed about patients with the virus is the way they catch their breath when they’re speaking, as well as a dry cough, and the intervals of their breathing patterns.”
“There are very few large datasets of respiratory sounds, so to make better algorithms that could be used for early detection, we need as many samples from as many participants as we can get. Even if we don’t get many positive cases of coronavirus, we could find links with other health conditions.”
