MIT News October 29, 2020
A team of researchers in the US (MIT, Harvard University) developed an AI speech processing framework that leverages acoustic biomarker feature extractors to pre-screen for COVID-19 from cough recordings. CNN-based models have been trained on 4256 subjects and tested on the remaining 1064 subjects of the dataset. When validated with subjects diagnosed using an official test, the model achieved COVID-19 sensitivity of 98.5% with a specificity of 94.2% . For asymptomatic subjects it achieved sensitivity of 100% with a specificity of 83.2%. AI techniques can produce a free, non-invasive, real-time, any-time, instantly distributable, large-scale COVID-19 asymptomatic screening tool to augment current approaches in containing the spread of COVID-19. Practical use cases could be for daily screening of students, workers, and public as schools, jobs, and transport reopen, or for pool testing to quickly alert of outbreaks in groups…read more. Open Access TECHNICAL ARTICLEÂ