Qu Yingjie, Meng Yuquan, Fan Hua, Xu Ronald X
Department of Intelligence Science and Technology, Anhui Polytechnic University, Wuhu, Anhui 241000, China.
Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Infrared Phys Technol. 2022 Jun;123:104201. doi: 10.1016/j.infrared.2022.104201. Epub 2022 May 14.
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.
对肺部感染进行快速筛查和早期治疗对于有效控制许多流行病至关重要，如2019冠状病毒病（COVID-19）。最近的研究表明肺部感染与背部皮肤温度分布变化之间存在潜在关联。基于这些发现，我们建议结合低成本、便携式和快速热成像技术，运用图像处理算法和机器学习分析来对肺炎进行无创且安全的检测。所提出的方法在69名受试者（30名正常成年人、11名无肺炎的发热患者、19名普通肺炎患者和9名COVID-19患者）中进行了测试，从每个受试者的背部采集了RGB图像和热图像。对采集到的图像进行自动处理，以便提取多个位置和形状特征，从而以93%的高精度区分正常受试者和肺炎患者。此外，通过所提出的方法对两名肺炎患者进行的每日评估准确预测了临床结果，与实验室检测结果一致。我们的初步研究证明了便携式智能热成像技术用于肺炎筛查和治疗评估的技术可行性。该方法有可能在资源匮乏地区实施，以更有效地控制呼吸道流行病。
