The difference between a healthy person’s cough and the cough of someone infected with [SARS-CoV-2] is so slight that it’s imperceptible to the human ear. So the team developed an AI to detect these minute differences using tens of thousands of recorded samples of coughs and spoken words. And it’s been ridiculously accurate in early tests, recognizing 98.5% of coughs from people with confirmed covid-19 cases, and 100% of coughs from asymptomatic people.
Here’s how it works. One neural network gauges sounds associated with vocal cord strength, while another detects cues related to a person’s emotional state, such as frustration, which can produce a “flat affect.” A third network listens for subtle changes in lung and respiratory performance. The team then combined all three models and overlaid them with an algorithm to detect muscular degradation.
The MIT scientists warned that, even with the level of accuracy achieved so far, people shouldn’t use this AI as a substitution for getting tested for covid-19… However, the technology could still play a vital role as a screening tool for the virus. The team is reportedly developing a free “user-friendly” app that can be used as a convenient prescreening tool for individuals who aren’t showing any symptoms but worry they might be infected.