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Feature | April 14, 2020

Pre-Symptomatic Pathogen Detection Using Computer Vision and Machine Learning

By Marc Raphael (LUCI Fellow)

The current COVID-19 pandemic has highlighted the need for early-stage pathogen detection. LUCI fellow, Marc Raphael (c/o 2016) is researching on methods that detect whether or not eukaryotic cells are infected with a virus such as COVID-19 based on the analysis of their in-vitro behavior under traditional modes of light microscopy. Raphael and his team plan to leverage their group’s previous novel computer vision algorithms to quantitatively characterize in vitro cellular phenotype with the aid of computer vision and in doing so detect phenotypic deviations or anomalies due to the onset of viral infection with the aid of machine learning. The technology will be the first to quantify the time lag between viral infection and significant deviations in cellular behavior with high temporal resolution (minutes). The technique is compatible with hand-held microscopes, and thus readily deployable, and can be coupled with fluorescently-tagged antibody assays for specific pathogen determination, such as COVID-19. Recently, Raphael submitted a white paper on this technology and plans to update the Basic Research Office as this research project continues.

*Researcher cited using VBFF or LUCI funds.