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Learn MoreCOVID-19 is a global pandemic disease and the best way to stop it is controlling its spread and treating the infected individuals. Detailed measures of clinical characteristics and outcomes in patients with COVID-19 like Reverse transcription-polymerase chain reaction (RT-PCR) are not accessible to a large population and require patients to spend hours waiting at the hospitals. As well, it is not yet known that the lung is the only host of this virus; an inflammation of the heart has been recently reported in patients with COVID-19. Most recent studies imply that COVID-19 might directly impact on the heart. Therefore, relying on one method for detecting COVID-19 is not sufficient, multi-modal sensors indicating different physiological activities are required. There is no unique solution to capture different but relevant physiological signals underlying COVID-19. Given medical imaging techniques (chest CT scans), pulmonary function tests (PFTs), and electrophysiological recordings (e.g., ECG and blood pressure) of patients with COVID-19, we, in collaboration with Dena Corporation and University of Toronto, aim to develop machine learning and signal processing algorithms for detecting biomarkers underlying COVID19 and inferring their correlational patterns.
Milad Lankarany
Behnaz Poursartip;Idir Mellal;Vaibhav Bachuwar
Dena Corporation
Information and cultural industries
University of Toronto
Accelerate
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