The screening of flu-like syndrome is difficult due to nonspecific symptoms or even oligosymptomatic presentation and became even more complex during the Covid-19 pandemic. However, an efficient screening tool plays an important role in the control of highly contagious diseases, allowing more efficient medical-epidemiological approaches and rational management of global health resources. Infrared thermography is a technique sensitive to small alterations in the skin temperature which may be related to early signs of inflammation and thus being relevant in the detection of infectious diseases. Thus, the objective of this study was to evaluate the potential of facial thermal profiles as a risk evaluator of symptoms and signs of SARs diseases, using COVID-19 as background disease. A total of 136 patients were inquired about the most common symptoms of COVID-19 infection and were submitted to an infrared image scanning, where the temperatures of 10 parameters from different regions of the face were captured. We used RT-qPCR as the ground truth to compare with the thermal parameters, in order to evaluate the performance of infrared imaging in COVID-19 screening. Only 16% of infected patients had fever at the hospital admission, and most infrared thermal variables presented values of temperature significantly higher in infected patients. The maximum eye temperature (MaxE) showed the highest predictive value at a cut-off of >35.9°C (sn = 71.87%, sp = 86.11%, LR+ = 5.18, LR- = 0.33, AUC = 0.850, p < 0.001). Our predictive model reached an accuracy of 86% for disease detection, indicating that facial infrared thermal scanning, based on the combination of different facial regions and the thermal profile of the face, has potential to act as a more accurate diagnostic support method for early COVID-19 screening, when compared to classical infrared methods, based on a single spot with the maximum skin temperature of the face.
Copyright: © 2023 Makino Antunes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.