Characterization of tungiasis infection and morbidity using thermography in Kenya revealed higher disease burden during COVID-19 school closures.

BACKGROUND: Tungiasis is a neglected tropical skin disease caused by the sand flea Tunga penetrans. Female fleas penetrate the skin, particularly at the feet, and cause severe inflammation. This study aimed to characterize disease burden in two highly affected regions in Kenya, to test the use of thermography to detect tungiasis-associated inflammation and to create a new two-level classification of disease severity suitable for mapping, targeting, and monitoring interventions. METHODS: From February 2020 to April 2021, 3532 pupils age 8-14 years were quasi-randomly selected in 35 public primary schools and examined for tungiasis and associated symptoms. Of the infected pupils, 266 were quasi-randomly selected and their households visited, where an additional 1138 family members were examined. Inflammation was assessed using infra-red thermography. A Clinical score was created combining the number of locations on the feet with acute and chronic symptoms and infra-red hotspots. RESULTS: The overall prevalence of tungiasis among all the school pupils who were randomly selected during survey rounds 1 and 3 was 9.3% [95% confidence interval (CI): 8.4-10.3]. Based on mixed effects logistic models, the odds of infection with tungiasis among school pupils was three times higher in Kwale (coastal Kenya) than in Siaya [western Kenya; adjusted odds ratio (aOR) = 0.36, 95% CI: 0.18-0.74]; three times higher in males than in females (aOR = 3.0, 95% CI: 2.32-3.91) and three times lower among pupils sleeping in a house with a concrete floor (aOR = 0.32, 95% CI: 0.24-0.44). The odds of finding an infected person among the household population during surveys before the COVID-19 pandemic was a third (aOR = 0.32, 95% CI: 0.19-0.53) of that when schools were closed due to COVID-19 restrictions and approximately half (aOR = 0.44, 95% CI: 0.29-0.68) in surveys done after school re-opening (round 3). Infection intensity was positively correlated with inflammation as measured by thermography (Spearman's rho = 0.68, P < 0.001) and with the clinical score (rho = 0.86, P < 0.001). Based on the two-level classification, severe cases were associated with a threefold higher level of pain (OR = 2.99, 95% CI: 2.02-4.43) and itching (OR = 3.31, 95% CI: 2.24-4.89) than mild cases. CONCLUSIONS: Thermography was a valuable addition for assessing morbidity and the proposed two-level classification of disease severity clearly separated patients with mild and severe impacts. The burden of tungiasis was considerably higher in households surveyed during COVID-19 restrictions suggesting underlying risks are found in the home environment more than in school.

Measurement and Processing of Thermographic Data of Passing Persons for Epidemiological Purposes.

Non-contact temperature measurement of persons during an epidemic is the most preferred measurement option because of the safety of personnel and minimal possibility of spreading infection. The use of infrared (IR) sensors to monitor building entrances for infected persons has seen a major boom between 2020 and 2022 due to the COVID-19 epidemic, but with questionable results. This article does not deal with the precise determination of the temperature of an individual person but focuses on the possibility of using infrared cameras for monitoring the health of the population. The aim is to use large amounts of infrared data from many locations to provide information to epidemiologists so they can have better information about potential outbreaks. This paper focuses on the long-term monitoring of the temperature of passing persons inside public buildings and the search for the most appropriate tools for this purpose and is intended as the first step towards creating a useful tool for epidemiologists. As a classical approach, the identification of persons based on their characteristic temperature values over time throughout the day is used. These results are compared with the results of a method using artificial intelligence (AI) to evaluate temperature from simultaneously acquired infrared images. The advantages and disadvantages of both methods are discussed.

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

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.

Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding.

As obesity is a serious problem in the human population, overloading of the horse's thoracolumbar region often affects sport and school horses. The advances in using infrared thermography (IRT) to assess the horse's back overload will shortly integrate the IRT-based rider-horse fit into everyday equine practice. This study aimed to evaluate the applicability of entropy measures to select the most informative measures and color components, and the accuracy of rider:horse bodyweight ratio detection. Twelve horses were ridden by each of the six riders assigned to the light, moderate, and heavy groups. Thermal images were taken pre- and post-exercise. For each thermal image, two-dimensional sample (SampEn), fuzzy (FuzzEn), permutation (PermEn), dispersion (DispEn), and distribution (DistEn) entropies were measured in the withers and the thoracic spine areas. Among 40 returned measures, 30 entropy measures were exercise-dependent, whereas 8 entropy measures were bodyweight ratio-dependent. Moreover, three entropy measures demonstrated similarities to entropy-related gray level co-occurrence matrix (GLCM) texture features, confirming the higher irregularity and complexity of thermal image texture when horses worked under heavy riders. An application of DispEn to red color components enables identification of the light and heavy rider groups with higher accuracy than the previously used entropy-related GLCM texture features.

Increased temperature at the healed area detected by thermography predicts recurrent pressure ulcers.

Preventing recurrent pressure ulcers is an important challenge in healthcare. One of the reasons for the high rate of recurrent pressure ulcers is the lack of assessment methods for their early detection. Therefore, this study aimed to determine the thermographic characteristics of the healed area and to consider the predictive validity of thermographic images for recurrent pressure ulcers within a 2-week period. This observational study was conducted at a long-term care facility in Japan between July 2017 and February 2019 among patients whose pressure ulcers had healed. Thermographic images of the healed area were recorded once a week until recurrence or until the end of the study. We enrolled 30 participants, among whom 8 developed recurrent pressure ulcers. The generalised estimation equation revealed that the thermographic finding of increased temperature at the healed area compared to that of the surrounding skin was significantly associated with recurrent pressure ulcers (odds ratio: 101.13, 95% confidence interval: 3.60-2840.77, p = .007); the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio for recurrent pressure ulcers within 2 weeks were 0.80, 0.94, 0.62, 0.97, 12.9 and 0.2, respectively. Our thermographic findings revealed that the temperature of the healed area was higher than that of the surrounding skin; this could be a useful predictor of pressure ulcer recurrence within 2 weeks, even in the absence of macroscopic changes.

Use of infrared thermography in medical diagnostics: a scoping review protocol.

INTRODUCTION: Thermography offers a non-invasive radiation-free methodology for diagnostic imaging and temperature measurement, but the extent of the current application is unclear, as is the level of evidence for each use case. Moreover, population-based thermographic reference values for diagnostic purposes are nearly unknown. The aim of this scoping review is to identify patient populations and diseases in which thermography is applied, cataloguing of technical and environmental modalities, investigation of the existence of specific reference data and finally exploration of gaps and future tasks. METHODS AND ANALYSIS: PubMed, Cochrane Database of Systematic Reviews and CENTRAL, Embase, Web of Science and OpenGrey are to be searched using pretested suitable search strategies, with no language restriction, but abstracts should be available in English or German and articles should not have been published before 2000. This limited time frame is due to the rapid technological progress, which makes it necessary to exclude reports based on outdated technology. The literature found will be selected on the basis of previously defined inclusion and exclusion criteria. Subsequently, relevant data will be extracted from the included references into a predesigned table. The selection and extraction process will be conducted by two researchers independently. The report of the results will be according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. The entire review process will follow the Joanna Briggs Institute approach. The scoping review protocol is registered at the Open Science Framework. ETHICS AND DISSEMINATION: Ethical approval is not required for this work, but ethical medicine also obliges us to carefully consider diagnostic alternatives and compare them with current standards. The dissemination of the results will take place in a variety of ways. First and foremost through publication in an open access journal, but also through conference proceedings. In addition, this scoping review will serve to open up new research foci with regard to thermography.

Infrared Thermography for Measuring Elevated Body Temperature: Clinical Accuracy, Calibration, and Evaluation.

Infrared thermographs (IRTs) implemented according to standardized best practices have shown strong potential for detecting elevated body temperatures (EBT), which may be useful in clinical settings and during infectious disease epidemics. However, optimal IRT calibration methods have not been established and the clinical performance of these devices relative to the more common non-contact infrared thermometers (NCITs) remains unclear. In addition to confirming the findings of our preliminary analysis of clinical study results, the primary intent of this study was to compare methods for IRT calibration and identify best practices for assessing the performance of IRTs intended to detect EBT. A key secondary aim was to compare IRT clinical accuracy to that of NCITs. We performed a clinical thermographic imaging study of more than 1000 subjects, acquiring temperature data from several facial locations that, along with reference oral temperatures, were used to calibrate two IRT systems based on seven different regression methods. Oral temperatures imputed from facial data were used to evaluate IRT clinical accuracy based on metrics such as clinical bias (Δcb), repeatability, root-mean-square difference, and sensitivity/specificity. We proposed several calibration approaches designed to account for the non-uniform data density across the temperature range and a constant offset approach tended to show better ability to detect EBT. As in our prior study, inner canthi or full-face maximum temperatures provided the highest clinical accuracy. With an optimal calibration approach, these methods achieved a Δcb between ±0.03 °C with standard deviation (σΔcb) less than 0.3 °C, and sensitivity/specificity between 84% and 94%. Results of forehead-center measurements with NCITs or IRTs indicated reduced performance. An analysis of the complete clinical data set confirms the essential findings of our preliminary evaluation, with minor differences. Our findings provide novel insights into methods and metrics for the clinical accuracy assessment of IRTs. Furthermore, our results indicate that calibration approaches providing the highest clinical accuracy in the 37-38.5 °C range may be most effective for measuring EBT. While device performance depends on many factors, IRTs can provide superior performance to NCITs.

Utility of thermal image scanning in screening for febrile patients in cold climates.

BACKGROUND: Infrared thermography (IRT) for fever screening systems was introduced in not only general hospitals, but also orthopedic hospitals as a countermeasure against the spread of coronavirus disease 2019 (COVID-19). Despite the widespread use of IRT, various results have shown low and high efficacies, so the utility of IRT is controversial, especially in cold climates. The aims of this study were to investigate the utility of IRT in screening for fever in a cold climate and to devise suitable fever screening in orthopedic surgery for COVID-19. METHODS: A total of 390 orthopedic surgery patients were enrolled to the outdoor group and 210 hospital staff members were enrolled to the indoor group. Thermographic temperature at the front of the face in the outdoor group was immediately measured after entering our hospital from a cold outdoor environment. Measurements for the indoor group were made after staying in the hospital (environmental temperature, 28 °C) for at least 5 h. Body temperature was then measured using an axillary thermometer >15 min later in both groups. RESULTS: In the outdoor group, mean thermographic temperature was significantly lower than axillary temperature and IRT could not detect febrile patients with axillary temperatures >37.0 °C. Mean thermographic temperature was significantly lower in the outdoor group than in the indoor group. Sensitivity was 11.5% for the outdoor group, lower than that for the indoor group. CONCLUSIONS: We verified that IRT was not accurate in a cold climate. IRT is inadequate as a screening method to accurately detect febrile individuals, so we believe that stricter countermeasures for second screening need to be employed to prevent nosocomial infections and disease clusters of COVID-19, even in orthopedic hospitals.

Estimation of Respiratory Rate from Thermography Using Respiratory Likelihood Index.

Respiration is a key vital sign used to monitor human health status. Monitoring respiratory rate (RR) under non-contact is particularly important for providing appropriate pre-hospital care in emergencies. We propose an RR estimation system using thermal imaging cameras, which are increasingly being used in the medical field, such as recently during the COVID-19 pandemic. By measuring temperature changes during exhalation and inhalation, we aim to track the respiration of the subject in a supine or seated position in real-time without any physical contact. The proposed method automatically selects the respiration-related regions from the detected facial regions and estimates the respiration rate. Most existing methods rely on signals from nostrils and require close-up or high-resolution images, while our method only requires the facial region to be captured. Facial region is detected using YOLO v3, an object detection model based on deep learning. The detected facial region is divided into subregions. By calculating the respiratory likelihood of each segmented region using the newly proposed index, called the Respiratory Quality Index, the respiratory region is automatically selected and the RR is estimated. An evaluation of the proposed RR estimation method was conducted on seven subjects in their early twenties, with four 15 s measurements being taken. The results showed a mean absolute error of 0.66 bpm. The proposed method can be useful as an RR estimation method.

Globally deployed COVID-19 fever screening devices using infrared thermographs consistently normalize high readings to afebrile range.

SIGNIFICANCE: The need for regulatory review of infrared thermographs (IRTs) used on humans was removed in response to the unique circumstances of the SARS-CoV-2 pandemic (a.k.a., COVID-19). The market for these devices has since expanded considerably. This evaluation of IRT performance may have significant implications for febrility screening worldwide. AIM: Perform controlled nonhuman trials of IRT devices to identify and quantify deviations in the human temperature range. APPROACH: We compared IRT readings of a temperature-controlled non-human subject with one FDA-cleared IRT and one FDA-cleared handheld NCIT. In individual trials for each device, the subject was measured between 35°C and 40°C at 0.25°C increments. RESULTS: The IRT device measurements were consistently normalized around the human mean (∼37 ° C). Temperatures were decremented as they approached the febrile range, and systematically reported as normal across all seven devices. This effect does not appear to be explained by a fixed offset or any known approach to estimating body temperature, or by random error. CONCLUSION: The IRTs in this study generated human temperature measurements that were systematically biased to the mean human temperature. Given that these devices are utilized for sentinel detection of possible infectious disease transmission, and are now globally employed, the implications for reduced detection of febrility are a widespread false sense of security. This vulnerability must be considered with respect to facility access control, clinical applications, and travel screening in the context of the ongoing COVID-19 pandemic response.

A Deep Learning-Based Camera Approach for Vital Sign Monitoring Using Thermography Images for ICU Patients.

Infrared thermography for camera-based skin temperature measurement is increasingly used in medical practice, e.g., to detect fevers and infections, such as recently in the COVID-19 pandemic. This contactless method is a promising technology to continuously monitor the vital signs of patients in clinical environments. In this study, we investigated both skin temperature trend measurement and the extraction of respiration-related chest movements to determine the respiratory rate using low-cost hardware in combination with advanced algorithms. In addition, the frequency of medical examinations or visits to the patients was extracted. We implemented a deep learning-based algorithm for real-time vital sign extraction from thermography images. A clinical trial was conducted to record data from patients on an intensive care unit. The YOLOv4-Tiny object detector was applied to extract image regions containing vital signs (head and chest). The infrared frames were manually labeled for evaluation. Validation was performed on a hold-out test dataset of 6 patients and revealed good detector performance (0.75 intersection over union, 0.94 mean average precision). An optical flow algorithm was used to extract the respiratory rate from the chest region. The results show a mean absolute error of 2.69 bpm. We observed a computational performance of 47 fps on an NVIDIA Jetson Xavier NX module for YOLOv4-Tiny, which proves real-time capability on an embedded GPU system. In conclusion, the proposed method can perform real-time vital sign extraction on a low-cost system-on-module and may thus be a useful method for future contactless vital sign measurements.

Noncontact Body Temperature Measurement: Uncertainty Evaluation and Screening Decision Rule to Prevent the Spread of COVID-19.

The need to measure body temperature contactless and quickly during the COVID-19 pandemic emergency has led to the widespread use of infrared thermometers, thermal imaging cameras and thermal scanners as an alternative to the traditional contact clinical thermometers. However, limits and issues of noncontact temperature measurement devices are not well known and technical-scientific literature itself sometimes provides conflicting reference values on the body and skin temperature of healthy subjects. To limit the risk of contagion, national authorities have set the obligation to measure body temperature of workers at the entrance to the workplace. In this paper, the authors analyze noncontact body temperature measurement issues from both clinical and metrological points of view with the aim to (i) improve body temperature measurements accuracy; (ii) estimate the uncertainty of body temperature measurement on the field; (iii) propose a screening decision rule for the prevention of the spread of COVID-19. The approach adopted in this paper takes into account both the traditional instrumental uncertainty sources and clinical-medical ones related to the subjectivity of the measurand. A proper screening protocol for body temperature measurement considering the role of uncertainty is essential to correctly choose the threshold temperature value and measurement method to access critical places during COVID-19 pandemic emergency.

Diagnostic accuracy of infrared thermal imaging for detecting COVID-19 infection in minimally symptomatic patients.

INTRODUCTION: Despite being widely used as a screening tool, a rigorous scientific evaluation of infrared thermography for the diagnosis of minimally symptomatic patients suspected of having COVID-19 infection has not been performed. METHODS: A consecutive sample of 60 adult individuals with a history of close contact with COVID-19 infected individuals and mild respiratory symptoms for less than 7 days and 20 confirmed COVID-19 negative healthy volunteers were enrolled in the study. Infrared thermograms of the face were obtained with a mobile camera, and RT-PCR was used as the reference standard test to diagnose COVID-19 infection. Temperature values and distribution of the face of healthy volunteers and patients with and without COVID-19 infection were then compared. RESULTS: Thirty-four patients had an RT-PCR confirmed diagnosis of COVID-19 and 26 had negative test results. The temperature asymmetry between the lacrimal caruncles and the forehead was significantly higher in COVID-19 positive individuals. Through a random forest analysis, a cut-off value of 0.55°C was found to discriminate with an 82% accuracy between patients with and without COVID-19 confirmed infection. CONCLUSIONS: Among adults with a history of COVID-19 exposure and mild respiratory symptoms, a temperature asymmetry of ≥ 0.55°C between the lacrimal caruncle and the forehead is highly suggestive of COVID-19 infection. This finding questions the widespread use of the measurement of absolute temperature values of the forehead as a COVID-19 screening tool.

The effect of constitutive pigmentation on the measured emissivity of human skin.

BACKGROUND: The measurement of body temperature has become commonplace in the current COVID-19 pandemic. Body temperature can be measured using thermal infrared imaging, a safe, non-contact method that relies on the emissivity of the skin being known to provide accurate readings. Skin pigmentation affects the absorption of visible light and enables us to see variations in skin colour. Pigmentation may also affect the absorption of infrared radiation and thus affect thermal imaging. Human skin has an accepted emissivity of 0.98 but the effect of different skin pigmentation on this value is not known. In this study, we investigated the influence of different skin pigmentation on thermal emissivity in 65 adult volunteers. METHODS: A reference object of known emissivity (electrical tape) was applied to participant's skin on the inner upper arm. Tape and arm were imaged simultaneously using a thermal infrared camera. The emissivity was set on the camera to the known value for electrical tape. The emissivity was altered manually until the skin temperature using thermal imaging software was equal to the initial tape temperature. This provided the calculated emissivity value of the skin. Participants were grouped according to skin pigmentation, quantified using the Fitzpatrick skin phototyping scale and reflectance spectrophotometry. Differences in emissivity values between skin pigmentation groups were assessed by one-way ANOVA. RESULTS: The mean calculated emissivity for the 65 participants was 0.972 (range 0.96-0.99). No significant differences in emissivity were observed between participants when grouped by skin pigmentation according to the Fitzpatrick scale (p = 0.859) or reflectance spectrophotometry (p = 0.346). CONCLUSION: These data suggest that skin pigmentation does not affect thermal emissivity measurement of skin temperature using thermal infrared imaging. This study will aid further research into the application of thermal infrared imaging as a screening or bedside diagnostic tool in clinical practice.

Temperature Screening for SARS-CoV-2 in Nursing Homes: Evidence from Two National Cohorts.

BACKGROUND/OBJECTIVES: Infection screening tools classically define fever as 38.0°C (100.4°F). Frail older adults may not mount the same febrile response to systemic infection as younger or healthier individuals. We evaluate temperature trends among nursing home (NH) residents undergoing diagnostic SARS-CoV-2 testing and describe the diagnostic accuracy of temperature measurements for predicting test-confirmed SARS-CoV-2 infection. DESIGN: Retrospective cohort study evaluating diagnostic accuracy of pre-SARS-CoV-2 testing temperature changes. SETTING: Two separate NH cohorts tested diagnostically (e.g., for symptoms) for SARS-CoV-2. PARTICIPANTS Veterans residing in Veterans Affairs (VA) managed NHs and residents in a private national chain of community NHs. MEASUREMENTS: For both cohorts, we determined the sensitivity, specificity, and Youden's index with different temperature cutoffs for SARS-CoV-2 polymerase chain reaction results. RESULTS: The VA cohort consisted of 1,301 residents in 134 facilities from March 1, 2020, to May 14, 2020, with 25% confirmed for SARS-CoV-2. The community cohort included 3,368 residents spread across 282 facilities from February 18, 2020, to June 9, 2020, and 42% were confirmed for SARS-CoV-2. The VA cohort was younger, less White, and mostly male. A temperature testing threshold of 37.2°C has better sensitivity for SARS-CoV-2, 76% and 34% in the VA and community NH, respectively, versus 38.0°C with 43% and 12% sensitivity, respectively. CONCLUSION: A definition of 38.0°C for fever in NH screening tools should be lowered to improve predictive accuracy for SARS-CoV-2 infection. Stakeholders should carefully consider the impact of adopting lower testing thresholds on testing availability, cost, and burden on staff and residents. Temperatures alone have relatively low sensitivity/specificity, and we advocate any threshold be used as part of a screening tool, along with other signs and symptoms of infection.

Contactless Vital Signs Measurement System Using RGB-Thermal Image Sensors and Its Clinical Screening Test on Patients with Seasonal Influenza.

Background: In the last two decades, infrared thermography (IRT) has been applied in quarantine stations for the screening of patients with suspected infectious disease. However, the fever-based screening procedure employing IRT suffers from low sensitivity, because monitoring body temperature alone is insufficient for detecting infected patients. To overcome the drawbacks of fever-based screening, this study aims to develop and evaluate a multiple vital sign (i.e., body temperature, heart rate and respiration rate) measurement system using RGB-thermal image sensors. Methods: The RGB camera measures blood volume pulse (BVP) through variations in the light absorption from human facial areas. IRT is used to estimate the respiration rate by measuring the change in temperature near the nostrils or mouth accompanying respiration. To enable a stable and reliable system, the following image and signal processing methods were proposed and implemented: (1) an RGB-thermal image fusion approach to achieve highly reliable facial region-of-interest tracking, (2) a heart rate estimation method including a tapered window for reducing noise caused by the face tracker, reconstruction of a BVP signal with three RGB channels to optimize a linear function, thereby improving the signal-to-noise ratio and multiple signal classification (MUSIC) algorithm for estimating the pseudo-spectrum from limited time-domain BVP signals within 15 s and (3) a respiration rate estimation method implementing nasal or oral breathing signal selection based on signal quality index for stable measurement and MUSIC algorithm for rapid measurement. We tested the system on 22 healthy subjects and 28 patients with seasonal influenza, using the support vector machine (SVM) classification method. Results: The body temperature, heart rate and respiration rate measured in a non-contact manner were highly similarity to those measured via contact-type reference devices (i.e., thermometer, ECG and respiration belt), with Pearson correlation coefficients of 0.71, 0.87 and 0.87, respectively. Moreover, the optimized SVM model with three vital signs yielded sensitivity and specificity values of 85.7% and 90.1%, respectively. Conclusion: For contactless vital sign measurement, the system achieved a performance similar to that of the reference devices. The multiple vital sign-based screening achieved higher sensitivity than fever-based screening. Thus, this system represents a promising alternative for further quarantine procedures to prevent the spread of infectious diseases.

Return to school in the COVID-19 era: considerations for temperature measurement.

COVID-19 pandemics required a reorganisation of social spaces to prevent the spread of the virus. Due to the common presence of fever in the symptomatic patients, temperature measurement is one of the most common screening protocols. Indeed, regulations in many countries require temperature measurements before entering shops, workplaces, and public buildings. Due to the necessity of providing rapid non-contact and non-invasive protocols to measure body temperature, infra-red thermometry is mostly used. Many countries are now facing the need to organise the return to school and universities in the COVID-19 era, which require solutions to prevent the risk of contagion between students and/or teachers and technical/administrative staff. This paper highlights and discusses some of the strengths and limitations of infra-red cameras, including the site of measurements and the influence of the environment, and recommends to be careful to consider such measurements as a single "safety rule" for a good return to normality.

Clinical evaluation of fever-screening thermography: impact of consensus guidelines and facial measurement location.

SIGNIFICANCE: Infrared thermographs (IRTs) have been used for fever screening during infectious disease epidemics, including severe acute respiratory syndrome, Ebola virus disease, and coronavirus disease 2019 (COVID-19). Although IRTs have significant potential for human body temperature measurement, the literature indicates inconsistent diagnostic performance, possibly due to wide variations in implemented methodology. A standardized method for IRT fever screening was recently published, but there is a lack of clinical data demonstrating its impact on IRT performance. AIM: Perform a clinical study to assess the diagnostic effectiveness of standardized IRT-based fever screening and evaluate the effect of facial measurement location. APPROACH: We performed a clinical study of 596 subjects. Temperatures from 17 facial locations were extracted from thermal images and compared with oral thermometry. Statistical analyses included calculation of receiver operating characteristic (ROC) curves and area under the curve (AUC) values for detection of febrile subjects. RESULTS: Pearson correlation coefficients for IRT-based and reference (oral) temperatures were found to vary strongly with measurement location. Approaches based on maximum temperatures in either inner canthi or full-face regions indicated stronger discrimination ability than maximum forehead temperature (AUC values of 0.95 to 0.97 versus 0.86 to 0.87, respectively) and other specific facial locations. These values are markedly better than the vast majority of results found in prior human studies of IRT-based fever screening. CONCLUSION: Our findings provide clinical confirmation of the utility of consensus approaches for fever screening, including the use of inner canthi temperatures, while also indicating that full-face maximum temperatures may provide an effective alternate approach.