Rapid, Non-Contact Screening Tool for COVID-19 Using Mobile Thermal Imaging and Deep Learning

Recent pandemics (SARS, MERS, COVID-19) have demonstrated the important need for rapid non-contact screening tools for respiratory infections. While forehead "spot check" thermometers and thermal imaging have been widely used in recent years, their utility has been limited to identification of people with a fever (T>100.4 degrees F), which misses a large number of infectious cases. In this paper, we present several results that demonstrate how deep learning can be used to greatly enhance thermal imaging tools. In our first experiment, we present results from a CNN ResNet50 transfer learning model trained on thermal images collected from (N=45) patients who had mild respiratory infections (COVID-19, tuberculosis, LRI) who did not present with a fever. When compared with (N=85) healthy controls, the deep learning model demonstrated a median accuracy of AUC=0.80 for a binary classifier (infected vs non-infected). In our second experiment, we tested the ability of thermal imaging with deep learning to distinguish between (N=25) COVID-POSITIVE patients from (N=49) COVID-NEGATIVE patients who had other symptomatic infections. Using a similar deep learning model, we discovered that thermal imaging alone had a limited ability (AUC accuracy = 0.61) to detect COVID-POSITIVE patients from among other infected patients; however, by adding a clinical questionnaire, this accuracy was increased to AUC=0.77, and the overall screening accuracy (thermal+questionnaire) was increased to AUC=0.90. These two experimental results suggest that current COVID-19 screening could be significantly improved by first employing rapid non-contact thermal imaging to quickly detect individuals with some type of respiratory infection, and then following up each positive thermal test with a simple questionnaire to determine if the infection is likely to be COVID-19. To the best of our knowledge, this is the first reported result demonstrating thermal imaging and deep learning among patients with various respiratory infections. Clinical Relevance - This work further establishes the use of thermal imaging as an effective screening tool for identifying early-stage respiratory infections and screening for COVID-19.