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.