Again and again, experts have pleaded that we need more and faster testing to control the coronavirus pandemic—and many have suggested that artificial intelligence (AI) can help. Numerous COVID-19 diagnostics in development use AI to quickly analyze X-ray or CT scans, but these techniques require a chest scan at a medical facility.
Since the spring, research teams have been working toward anytime, anywhere apps that could detect coronavirus in the bark of a cough. In June, a team at the University of Oklahoma showed it was possible to distinguish a COVID-19 cough from coughs due to other infections, and now a paper out of MIT, using the largest cough dataset yet, identifies asymptomatic people with a remarkable 100 percent detection rate.
If approved by the FDA and other regulators, COVID-19 cough apps, in which a person records themselves coughing on command, could eventually be used for free, large-scale screening of the population.
With potential like that, the field is rapidly growing: Teams pursuing similar projects include a Bill and Melinda Gates Foundation-funded initiative, Cough Against Covid, at the Wadhwani Institute for Artificial Intelligence in Mumbai; the Coughvid project out of the Embedded Systems Laboratory of the École Polytechnique Fédérale de Lausanne in Switzerland; and the University of Cambridge’s COVID-19 Sounds project.
The fact that multiple models can detect COVID in a cough suggests that there is no such thing as truly asymptomatic coronavirus infection—physical changes always occur that change the way a person produces sound. “There aren’t many conditions that don’t give you any symptoms,” says Brian Subirana, director of the MIT Auto-ID lab and co-author on the recent study, published in the IEEE Open Journal of Engineering in Medicine and Biology.
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