Automated processing of thermal imaging to detect COVID-19

Rafael Y. Brzezinski, Neta Rabin, Nir Lewis, Racheli Peled, Ariel Kerpel, Avishai M. Tsur, Omer Gendelman, Nili Naftali-Shani, Irina Gringauz, Howard Amital, Avshalom Leibowitz, Haim Mayan, Ilan Ben-Zvi, Eyal Heller, Liran Shechtman, Ori Rogowski, Shani Shenhar-Tsarfaty, Eli Konen, Edith M. Marom, Avinoah IroniGalia Rahav, Yair Zimmer, Ehud Grossman, Zehava Ovadia-Blechman, Jonathan Leor, Oshrit Hoffer

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


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 advanced image processing algorithms. We captured thermal images of the backs 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 COVID-19 detection with up to 92% sensitivity. Thermal imaging scores were inversely correlated with clinical variables associated with COVID-19 disease progression. In summary, 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.

Original languageEnglish
Article number17489
JournalScientific Reports
Issue number1
StatePublished - Dec 2021
Externally publishedYes


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