AI Model Detects Covid-19 from Sound of Coughing

TECH DIGEST – MIT researchers have developed a model which can distinguish people with asymptomatic Covid-19 from healthy people, using the sound of their coughs. These differences are impossible to detect with the human ear.

Previously, researchers have trained algorithms to detect conditions such as pneumonia and asthma based on basic phone recordings of coughs.

The MIT team had been developing models to detect signs of Alzheimer’s using these recordings but as the corona virus pandemic emerged, the researchers decided to investigate whether a similar tool could be applicable to Covid-19.

In April, they collected as many recordings of coughs as possible, setting up a website where anyone could submit recordings of forced coughs, along with information about their symptoms and test results.

The researchers collected more than 200,000 forced-cough samples including 2,500 from people confirmed to have Covid-19.

These Covid-19-positive recordings and 2,500 recordings from healthy participants formed a dataset.

The scientists used 4,000 to train the model and the remaining 1,000 to test it.

When fed new recordings of coughs, the model was able to identify people diagnosed with Covid-19 with 98.5 per cent accuracy, and 100 per cent accuracy for people with asymptomatic Covid-19.

The team is partnering with several hospitals to collect a larger and more diverse set of cough recordings to boost the accuracy of the model.

They are also working with a company to incorporate the AI model into a user-friendly pre-screening app, which could provide a free, quick and non-invasive tool for identifying people with asymptomatic Covid-19.

“The effective implementation of this group diagnostic tool could diminish the spread of the pandemic if everyone uses it before going to a classroom, a factory, or a restaurant,” said Professor Brian Subirana, director of the Auto-ID Laboratory.