Download This PaperInfrared Image Method for Possible COVID-19 Detection Through Febrile and Subfebrile People Screening
51 Pages Posted: 23 Feb 2022
Abstract
This study proposed an infrared image-based method for febrile and subfebrile people screening to comply with the society need for alternative, quick response, and effective methods for COVID-19 contagious people screening. The methodology consisted of: (i) Developing a method based on facial infrared imaging for possible COVID-19 early detection in people with and without fever (subfebrile state); (ii) Using 1206 emergency room (ER) patients to develop an algorithm for general application of the method, and (iii) Testing the method and algorithm effectiveness in 2558 cases (RT-qPCR tested for COVID-19) from 227,261 workers evaluations in five different countries. Artificial intelligence was used through a convolutional neural network (CNN) to develop the algorithm that took facial infrared images as input and classified the tested individuals in three groups: fever (high risk), subfebrile (medium risk), and no fever (low risk). The results showed that suspicious and confirmed COVID-19 (+) cases characterized by temperatures below the 37.5ºC fever threshold were identified. Also, average forehead and eye temperatures greater than 37.5 oC were not enough to detect fever similarly to the proposed CNN algorithm. Most RT-qPCR confirmed COVID-19 (+) cases found in the 2558 cases sample (17 cases/89.5%) belonged to the CNN selected subfebrile group. The COVID-19 (+) main risk factor was to be in the subfebrile group, in comparison to age, diabetes, high blood pressure, smoking and others. In sum, the proposed method was shown to be a potentially important new tool for COVID-19 (+) people screening for air travel and public places in general.
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Funding: The authors acknowledge with gratitude the support of the Brazilian National Council of Scientific and Technological Development, CNPq, project 313646/2020-1; the cooperation of the Hospital of Clinics, Faculty of Medicine, University of São Paulo, Brazil.
Declaration of Interests: We declare that we have no conflict of interests.
Ethics Approval Statement: This retrospective cross-sectional analysis of the IT database of cases admitted to an emergency unit (Hospital of Clinics, Faculty of Medicine, University of São Paulo, Brazil) was reviewed and approved by an Ethics Committee and did not require informed consent. br>
Keywords: infrared imaging, artificial intelligence, convolutional neural network
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