Non-fever COVID-19 Detection by Infrared Imaging

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Citações na Scopus
2
Tipo de produção
conferenceObject
Data de publicação
2022
DOI
Scopus
Web of Science
Título da Revista
ISSN da Revista
Título do Volume
Editora
SPRINGER INTERNATIONAL PUBLISHING AG
Autores
BRIOSCHI, Marcos Leal
MOREIRA, Mayco Anderson Guedes Maciel
CIVIERO, Nicolas
NEVES, Eduardo Borba
VARGAS, Jose Viriato Coelho
Citação
ARTIFICIAL INTELLIGENCE OVER INFRARED IMAGES FOR MEDICAL APPLICATIONS AND MEDICAL IMAGE ASSISTED BIOMARKER DISCOVERY, v.13602, p.57-72, 2022
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
This study proposed an infrared image-based method for febrile and non-febrile people screening to comply with the society needs for alternative, quick response, and effective methods for COVID-19 contagious people screening. The methodology consisted of: (i) Developing a method based on the face infrared imaging for early COVID-19 detection in people with and without fever; (ii) Recruiting 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 (RTqPCR tested for COVID-19) from 227,261 workers evaluations in five different countries. Artificial intelligence was used with a convolutional neural network (CNN) to develop the algorithm that took face infrared images as input and classified the tested individuals into three groups: fever (high risk), non-febrile (medium risk), and without fever (low risk). The results showed that suspicious and confirmed COVID-19 (+) cases characterized by temperatures below the 37.5 degrees C fever threshold were identified. Also, average forehead and eye temperatures greater than 37.5 C 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 non-febrile COVID group. The COVID-19 (+) main risk factor was to be in the non-febrile medium-risk group, compared with 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.
Palavras-chave
Infrared imaging, Artificial intelligence, Convolutional neural network, Thermography
URI
https://observatorio.fm.usp.br/handle/OPI/52638
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Coleções
Comunicações em Eventos - FM/MNE
Comunicações em Eventos - COVID-19
Comunicações em Eventos - HC/ICHC
Comunicações em Eventos - LIM/62
Comunicações em Eventos - ODS/03