Asymptomatic people who are infected with Covid-19 exhibit, by definition, no discernible physical symptoms of the disease. They are thus less likely to seek out testing for the virus, and could unknowingly spread the infection to others. But it seems those who are asymptomatic may not be entirely free of changes wrought by the virus. MIT researchers have now found that people who are asymptomatic may differ from healthy individuals in the way that they cough. These differences are not decipherable to the human ear.
An algorithm can detect the coronavirus in people who are asymptomatic, just from listening to the way they cough. Coronavirus patients who don't have symptoms still exhibit subtle changes not always detectable by the naked eye - or ear. Researchers at MIT developed an AI-powered model that distinguishes asymptomatic people from uninfected individuals by analyzing recordings of coughs submitted by tens of thousands of volunteers online. The algorithm accurately identified 98.5 percent of coughs from people who tested positive for the virus, including 100 percent of coughs from asymptomatic patients. The team is gathering more samples, with the goal of producing an app that could be a convenient and free pre-screening tool. Researchers at MIT used AI to analyze thousands of coughs and detect differences in those of people with coronavirus.
As millions of people worldwide battle the symptoms of COVID-19, a group of "silent patients" may not even know they're sick and spreading the virus. Asymptomatic people, by definition, have no physical symptoms of the illnesses they carry. Researchers at the Massachusetts Institute of Technology (MIT) however, say they may be showing symptoms after all -- in the sound of their cough. Their study has created an artificial intelligence program that can identify if someone has coronavirus by the way their coughing sounds. Researchers programmed their AI model with thousands of different recorded coughs from both healthy and sick volunteers.
The Massachusetts Institute of Technology (MIT) has developed an algorithm to identify people infected with COVID-19 based only on their cough. The algorithm was trained using "tens of thousands" of recordings -- both coughs and spoken words -- and was able to correctly identity 98.5% of those who were displaying symptoms and had confirmed cases of COVID-19. In addition, the algorithm identified 100% of COVID-19 carriers confirmed to have the virus but who were not displaying any symptoms. The recordings used to train the artificial intelligence (AI) model were submitted by volunteers online and included forced-coughs from healthy volunteers as well as COVID-19 sufferers. Over 70,000 samples have been collected so far and roughly 2,500 were submitted by individuals confirmed to have COVID-19.
Asymptomatic spread of COVID-19 is a huge contributor to the pandemic, but of course if there are no symptoms, how can anyone tell they should isolate or get a test? MIT research has found that hidden in the sound of coughs is a pattern that subtly, but reliably, marks a person as likely to be in the early stages of infection. It could make for a much-needed early warning system for the virus. The sound of one's cough can be very revealing, as doctors have known for many years. AI models have been built to detect conditions like pneumonia, asthma and even neuromuscular diseases, all of which alter how a person coughs in different ways.