Potential of using facial thermal imaging in patient triage of flu-like syndrome during the COVID-19 pandemic crisis

Abstract

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.

Fully Automated Unsupervised Learning Approach for Thermal Camera Calibration and An Accurate COVID-19 Human Temperature Tracking

“…For the objective of recognizing faces and masks and allowing an efficient automated Covid-19 screening strategy in public setting using NTIC dataset. Makino et al [17] proposed to assess the abilities of infrared imaging, also known as thermography, to evaluate symptoms and early indications using thermal video frames of different face regions of people, and then analyze their body temperature as a diagnostic tool to aid screening. 136 patient samples were gathered and examined at the emergency room of the aforementioned hospital from June to September 2022 in accordance with the inclusion and exclusion criteria.…”

Applications of Infrared Thermography in Ophthalmology

“…This suggests the need to assess additional physiological parameters to increase the sensitivity and specificity of non-invasive biosensors [ 14 ]. However, more recently, Makino Antunes et al reported a predictive model that reached an accuracy of 86% for disease detection [ 15 ]. In addition to the traditional measurement of temperature by infrared methods, there have been reports of algorithms based on artificial intelligence and the use of drones with thermal cameras for temperature measurement and algorithms to recognize human actions such as coughing and sneezing, which are paramount symptoms of respiratory infections [ 16 ].…”

Best Practices for Body Temperature Measurement with Infrared Thermography: External Factors Affecting Accuracy

“…NCITs measure temperature at a single point using a sensor with one or a few pixels. On the other hand, IRTs provide a temperature map of a large area using a sensor with thousands of pixels, which allows for greater flexibility in assessing different regions of the face and improves the effectiveness of temperature estimation [ 10 , 11 ]. Recent studies and evidence in the literature suggest that qualified IRTs, when used properly, can offer superior accuracy in estimating EBT compared to NCITs [ 12 , 13 , 14 ].…”