Medical Thermology and Thermography Specialization, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, HCFMUSP, São Paulo, SP, 01246-903, Brazil.
Medical Thermology and Thermography Specialization, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, HCFMUSP, São Paulo, SP, 01246-903, Brazil; Mechanical Engineering Post-Graduation Program, Mechanical Engineering Department, Universidade Federal do Paraná, UFPR, Curitiba, PR, 81531-980, Brazil.
J Therm Biol. 2023 Feb;112:103444. doi: 10.1016/j.jtherbio.2022.103444. Epub 2022 Dec 28.
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 37.5 °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 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.
本研究提出了一种基于红外图像的发热和低热人群筛查方法，以满足社会对 COVID-19 传染性人群筛查替代、快速响应和有效方法的需求。该方法包括：（i）开发一种基于面部红外成像的方法，用于在发热和无热（低热状态）人群中早期检测 COVID-19；（ii）使用 1206 名急诊（ER）患者开发一种适用于该方法的通用算法；（iii）在来自五个不同国家的 227261 名工人评估的 2558 例（经 RT-qPCR 检测 COVID-19）中测试该方法和算法的有效性。该算法通过卷积神经网络（CNN）开发，采用面部红外图像作为输入，将受测个体分为三组：发热（高风险）、低热（中风险）和无热（低风险）。结果表明，通过温度低于 37.5°C 的发热阈值来识别可疑和确诊的 COVID-19（+）病例。同样，平均额温和眼温高于 37.5°C 也不足以像所提出的 CNN 算法那样检测发热。在 2558 例样本中，大多数经 RT-qPCR 确诊的 COVID-19（+）病例（17 例/89.5%）属于 CNN 选择的低热组。与年龄、糖尿病、高血压、吸烟等相比，COVID-19（+）的主要危险因素是处于低热组。总之，所提出的方法被证明是一种用于航空旅行和公共场所 COVID-19（+）人群筛查的潜在重要新工具。
