The Use of Thermal Imaging and Deep Learning for Pulmonary Diagnostics and Infection Detection

Abstract

Pulmonary diseases are a leading cause of mortality and disability, but lack of simple low-cost tools to help diagnose and screen for such diseases. In this paper, we present results from a preliminary study exploring the use of thermal imaging as a possible diagnostic tool for several common pulmonary diseases including Asthma, COPD, ILD, Allergic Rhinitis, and Respiratory Infection. As part of a global health study, thermal images of the face were collected from 125 pulmonary disease patients as well as 11 healthy controls. All subjects were evaluated using a full pulmonary function test (PFT) and diagnosed by an experienced chest physician. For each pulmonary disease, we developed a separate naïve 2-layer CNN model as well as a transfer learning CNN model, using a more complex pre-trained ResNet50 model. The naïve CNN models demonstrated an accuracy of AUC = 0.75 for respiratory infection and an AUC=0.76 for COPD, but lacked any significant predictive value for other pulmonary diseases. The transfer learning CNN models demonstrated an accuracy of AUC = 0.82 for respiratory infection and AUC=0.81 for COPD, but exhibited poor performance for other pulmonary diseases. From these results, we conclude that a facial thermal image can be a useful tool to help identify respiratory infections as well as COPD. It is also important to note that none of the patients in our study had a significant fever (T >100.4 °F) that would be predictive of infection, and our CNN models were also able to distinguish Respiratory Infection from other pulmonary diseases including COPD. Given that thermal imaging is a non-contact measurement, such a tool could be of tremendous value in low resource settings or global health.