Low-cost thermal imaging with machine learning for non-invasive diagnosis and therapeutic monitoring of pneumonia

Yingjie Qu; Yuquan Meng; Hua Fan; Ronald X Xu

doi:10.1016/j.infrared.2022.104201

Low-cost thermal imaging with machine learning for non-invasive diagnosis and therapeutic monitoring of pneumonia

Infrared Phys Technol. 2022 Jun:123:104201. doi: 10.1016/j.infrared.2022.104201. Epub 2022 May 14.

Authors

Yingjie Qu¹, Yuquan Meng², Hua Fan³, Ronald X Xu⁴

Affiliations

¹ Department of Intelligence Science and Technology, Anhui Polytechnic University, Wuhu, Anhui 241000, China.
² Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
³ Department of Hepatobiliary Surgery, The First Affiliated Hospital of University of Science and Technology of China, Hefei, Anhui 230036, China.
⁴ Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangshu 215009, China.

Abstract

Rapid screening and early treatment of lung infection are essential for effective control of many epidemics such as Coronavirus Disease 2019 (COVID-19). Recent studies have demonstrated the potential correlation between lung infection and the change of back skin temperature distribution. Based on these findings, we propose to use low-cost, portable and rapid thermal imaging in combination with image-processing algorithms and machine learning analysis for non-invasive and safe detection of pneumonia. The proposed method was tested in 69 subjects (30 normal adults, 11 cases of fever without pneumonia, 19 cases of general pneumonia and 9 cases of COVID-19) where both RGB and thermal images were acquired from the back of each subject. The acquired images were processed automatically in order to extract multiple location and shape features that distinguish normal subjects from pneumonia patients at a high accuracy of 93 $%$ . Furthermore, daily assessment of two pneumonia patients by the proposed method accurately predicted the clinical outcomes, coincident with those of laboratory tests. Our pilot study demonstrated the technical feasibility of portable and intelligent thermal imaging for screening and therapeutic assessment of pneumonia. The method can be potentially implemented in under-resourced regions for more effective control of respiratory epidemics.

Keywords: Diagnosis; Machine learning; Pneumonia; Therapeutic monitoring; Thermal imaging.