Oshrit Hoffer, Rafael Y Brzezinski, Adam Ganim, Perry Shalom, Zehava Ovadia-Blechman, Lital Ben-Baruch, Nir Lewis, Racheli Peled, Carmi Shimon, Nili Naftali-Shani, Eyal Katz, Yair Zimmer, Neta Rabin
{"title":"Smartphone-based detection of COVID-19 and associated pneumonia using thermal imaging and a transfer learning algorithm.","authors":"Oshrit Hoffer, Rafael Y Brzezinski, Adam Ganim, Perry Shalom, Zehava Ovadia-Blechman, Lital Ben-Baruch, Nir Lewis, Racheli Peled, Carmi Shimon, Nili Naftali-Shani, Eyal Katz, Yair Zimmer, Neta Rabin","doi":"10.1002/jbio.202300486","DOIUrl":null,"url":null,"abstract":"<p><p>COVID-19-related pneumonia is typically diagnosed using chest x-ray or computed tomography images. However, these techniques can only be used in hospitals. In contrast, thermal cameras are portable, inexpensive devices that can be connected to smartphones. Thus, they can be used to detect and monitor medical conditions outside hospitals. Herein, a smartphone-based application using thermal images of a human back was developed for COVID-19 detection. Image analysis using a deep learning algorithm revealed a sensitivity and specificity of 88.7% and 92.3%, respectively. The findings support the future use of noninvasive thermal imaging in primary screening for COVID-19 and associated pneumonia.</p>","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of biophotonics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/jbio.202300486","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
COVID-19-related pneumonia is typically diagnosed using chest x-ray or computed tomography images. However, these techniques can only be used in hospitals. In contrast, thermal cameras are portable, inexpensive devices that can be connected to smartphones. Thus, they can be used to detect and monitor medical conditions outside hospitals. Herein, a smartphone-based application using thermal images of a human back was developed for COVID-19 detection. Image analysis using a deep learning algorithm revealed a sensitivity and specificity of 88.7% and 92.3%, respectively. The findings support the future use of noninvasive thermal imaging in primary screening for COVID-19 and associated pneumonia.
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