AI Recognizes COVID-19 in the Sound of a Cough

When the pandemic hit, Subirana’s team at MIT had been working on a set of machine learning algorithms to detect Alzheimer’s disease in audio recordings using biomarkers such as vocal cord strength, sentiment, lung performance, and muscular degradation. When it became clear that coughing was key feature of COVID-19, they quickly pivoted to seeing if it was possible for AI to detect coronavirus infections.

In a crowd-sourcing effort, the team gathered forced-cough recordings via a website between April and May, developing what the team claims is the largest audio COVID-19 dataset to date, with 70,000 recordings, of which 2,680 were submitted by people confirmed to have COVID-19.

Originally, the MIT team developed AI models for the project from scratch, but reached an accuracy ceiling of about 70%. As a test one weekend, the researchers trained their existing Alzheimer’s disease AI model with the COVID-19 cough data, and it worked, says Subirana. The model was accurate 98.5 percent of the time at detecting people who had received a positive test result. In detecting individuals with no symptoms at all, that accuracy climbed to 100 percent, with 83.2 percent success identifying negative cases. “It was a bit counterintuitive” that detecting asymptomatic patients was easier than symptomatic patients, says Subirana, but it makes sense that confounding factors of other infections would make it harder to pinpoint COVID-19 cough features.

Back in June, Imran and colleagues were able to develop an AI model to identify asymptomatic coughs and sift through those confounding factors to distinguish COVID-19 coughs from the cough sounds of bronchitis, whooping cough, and asthma with overall 90 percent accuracy. “Our goal was to make sure someone who simply has asthma would not be mis-diagnosed as having COVID,” says Imran.

Most teams pursuing this work are currently collecting more cough recordings: at workplaces, hospitals, online, and elsewhere. Researchers hope that cough apps will someday be used for daily screenings, such as students or factory workers coughing into their phones before heading to school or work. Eventually, says Subirana, the tool could be part of a true COVID-19 diagnostic, perhaps when used in combination with other biomarkers, such as fever.

Sound-based tools could also be used as an early warning system, in which coughs across a population are detected via hospital recordings or home smart speakers to pick up early signs of infection of a new disease. “This kind of solution can be used to identify unique cough signatures which will not be in the database already,” says Imran. “It can become an alarm system.”

And it’s not the only push to use AI to detect the sounds of COVID-19: A team of researchers from Saudi Arabia, India and the UK are developing an app to screen for COVID-19 symptoms in an individual’s speech.