Limpabandhu Chayabhan, Hooper Frances Sophie Woodley, Li Rui, Tse Zion
Queen Mary University of London, Mile End Road, London, E1 4NS.
Tandon School of Engineering, New York University, Brooklyn, USA.
IPEM Transl. 2022 Nov-Dec;3:100006. doi: 10.1016/j.ipemt.2022.100006. Epub 2022 Jul 15.
With fever being one of the most prominent symptoms of COVID-19, the implementation of fever screening has become commonplace around the world to help mitigate the spread of the virus. Non-contact methods of temperature screening, such as infrared (IR) forehead thermometers and thermal cameras, benefit by minimizing infection risk. However, the IR temperature measurements may not be reliably correlated with actual core body temperatures. This study proposed a trained model prediction using IR-measured facial feature temperatures to predict core body temperatures comparable to an FDA-approved product. The reference core body temperatures were measured by a commercially available temperature monitoring system. Optimal inputs and training models were selected by the correlation between predicted and reference core body temperature. Five regression models were tested during the study. The linear regression model showed the lowest minimum-root-mean-square error (RSME) compared with reference temperatures. The temple and nose region of interest (ROI) were identified as optimal inputs. This study suggests that IR temperature data could provide comparatively accurate core body temperature prediction for rapid mass screening of potential COVID cases using the linear regression model. Using linear regression modeling, the non-contact temperature measurement could be comparable to the SpotOn system with a mean SD of ± 0.285 °C and MAE of 0.240 °C.
发热是新冠病毒肺炎（COVID-19）最显著的症状之一，实施发热筛查已在全球变得司空见惯，以帮助减轻病毒传播。非接触式体温筛查方法，如红外（IR）额头温度计和热成像摄像机，通过将感染风险降至最低而具有优势。然而，红外温度测量可能与实际核心体温没有可靠的相关性。本研究提出了一种经过训练的模型预测方法，利用红外测量的面部特征温度来预测与美国食品药品监督管理局（FDA）批准的产品相当的核心体温。参考核心体温由市售温度监测系统测量。通过预测核心体温与参考核心体温之间的相关性选择最佳输入和训练模型。研究期间测试了五个回归模型。与参考温度相比，线性回归模型显示出最低的最小均方根误差（RSME）。颞部和鼻部感兴趣区域（ROI）被确定为最佳输入。本研究表明，红外温度数据可以为使用线性回归模型对潜在新冠病例进行快速大规模筛查提供相对准确的核心体温预测。使用线性回归建模，非接触式体温测量可以与SpotOn系统相媲美，平均标准差为±0.285°C，平均绝对误差为0.240°C。
