Automated processing of thermal imaging to detect COVID-19

Brzezinski, R. Y.; Rabin, N.; Lewis, N.; Peled, R.; Kerpel, A.; Tsur, A. M.; Gendelman, O.; Naftali-Shani, N.; Gringauz, I.; Amital, H.; Leibowitz, A.; Mayan, H.; Ben-Zvi, I.; Heler, E.; Shechtman, L.; Rogovski, O.; Shenhar-Tsarfaty, S.; Konen, E.; Marom, E. M.; Ironi, A.; Rahav, G.; Zimmer, Y.; Grossman, E.; Ovadia-Blechman, Z.; Leor, J.; Hoffer, O.

Rapid and sensitive screening tools for SARS-CoV-2 infection are essential to limit the spread of COVID-19 and to properly allocate national resources. Here, we developed a new point-of-care, non-contact thermal imaging tool to detect COVID-19, based on image-processing algorithms and machine learning analysis. We captured thermal images of the back of individuals with and without COVID-19 using a portable thermal camera that connects directly to smartphones. Our novel image processing algorithms automatically extracted multiple texture and shape features of the thermal images and achieved an area under the curve (AUC) of 0.85 in detecting COVID-19 with up to 92% sensitivity. Thermal imaging scores were inversely correlated with clinical variables associated with COVID-19 disease progression. We show, for the first time, that a hand-held thermal imaging device can be used to detect COVID-19. Non-invasive thermal imaging could be used to screen for COVID-19 in out-of-hospital settings, especially in low-income regions with limited imaging resources.