When you hear a person coughing, you will probably run away, but for this machine sound of coughing can be helpful. published last year, cough is one of the most common medical complaints, accounting for as many as 30 million clinical visits per year. Up to 40% of these complaints result in a referral to a pulmonologist.
In the study, a team led by Google scientists presented Health Acoustic Representations , a scalable self-supervised learning-based deep learning system using masked autoencoders. This system was trained on a large dataset of 313 million two-second-long audio clips. HeAR was adapted to detect COVID-19 and tuberculosis and to include characteristics such as if the person smokes. The HeAR model achieved scores of 0.645 and 0.710 for COVID-19 detection, depending on the dataset used, indicating its performance on a scale where 0.5 represents random prediction and 1 represents perfect accuracy. The model scored 0.739 for tuberculosis detection.
Ali Imran, one of the study’s authors and an engineer at the University of Oklahoma in Tulsa who also leads the development of AI4COVID-19, told that his team plans to obtain approval from the US Food and Drug Administration so that the app can eventually move to market.