Detection and classification of SARS-CoV-2 through phase imaging with computational specificity (PICS)

The COVID pandemic prompted the need for rapid detection of the SARS-CoV-2 virus and potentially other pathogens. In this study, we report a rapid, label-free optical detection method for SARS-CoV-2 that is aimed at detecting the virus in the patient’s breath condensates. We show in the published pre-clinical study that, through phase imaging with computational specificity (PICS), we can detect and classify SARS-CoV-2 versus other viruses (H1N1, HAdV and ZIKV) with 96% accuracy, within a minute after sample collection. PICS combines ultrasensitive quantitative phase imaging (QPI) with advanced deep-learning algorithms to detect and classify viral particles. The second stage of our project, currently under development, involves clinical validation of our proposed testing technique. Breath samples collected from patients in the clinic will be imaged with QPI and a U-Net model trained on the breath samples will identify the SARS-CoV-2 in the sample within a minute.