PDF(Pubmed)
Abstract：
BACKGROUND: Current approaches for initial coronary artery disease (CAD) assessment rely on pretest probability (PTP) based on risk factors and presentations, with limited performance. Infrared thermography (IRT), a non-contact technology that detects surface temperature, has shown potential in assessing atherosclerosis-related conditions, particularly when measured from body regions such as faces. We aim to assess the feasibility of using facial IRT temperature information with machine learning for the prediction of CAD.
METHODS: Individuals referred for invasive coronary angiography or coronary CT angiography (CCTA) were enrolled. Facial IRT images captured before confirmatory CAD examinations were used to develop and validate a deep-learning IRT image model for detecting CAD. We compared the performance of the IRT image model with the guideline-recommended PTP model on the area under the curve (AUC). In addition, interpretable IRT tabular features were extracted from IRT images to further validate the predictive value of IRT information.
RESULTS: A total of 460 eligible participants (mean (SD) age, 58.4 (10.4) years; 126 (27.4%) female) were included. The IRT image model demonstrated outstanding performance (AUC 0.804, 95% CI 0.785 to 0.823) compared with the PTP models (AUC 0.713, 95% CI 0.691 to 0.734). A consistent level of superior performance (AUC 0.796, 95% CI 0.782 to 0.811), achieved with comprehensive interpretable IRT features, further validated the predictive value of IRT information. Notably, even with only traditional temperature features, a satisfactory performance (AUC 0.786, 95% CI 0.769 to 0.803) was still upheld.
CONCLUSIONS: In this prospective study, we demonstrated the feasibility of using non-contact facial IRT information for CAD prediction.
METHODS: Individuals referred for invasive coronary angiography or coronary CT angiography (CCTA) were enrolled. Facial IRT images captured before confirmatory CAD examinations were used to develop and validate a deep-learning IRT image model for detecting CAD. We compared the performance of the IRT image model with the guideline-recommended PTP model on the area under the curve (AUC). In addition, interpretable IRT tabular features were extracted from IRT images to further validate the predictive value of IRT information.
RESULTS: A total of 460 eligible participants (mean (SD) age, 58.4 (10.4) years; 126 (27.4%) female) were included. The IRT image model demonstrated outstanding performance (AUC 0.804, 95% CI 0.785 to 0.823) compared with the PTP models (AUC 0.713, 95% CI 0.691 to 0.734). A consistent level of superior performance (AUC 0.796, 95% CI 0.782 to 0.811), achieved with comprehensive interpretable IRT features, further validated the predictive value of IRT information. Notably, even with only traditional temperature features, a satisfactory performance (AUC 0.786, 95% CI 0.769 to 0.803) was still upheld.
CONCLUSIONS: In this prospective study, we demonstrated the feasibility of using non-contact facial IRT information for CAD prediction.
摘要：
背景：目前用于初始冠状动脉疾病（CAD）评估的方法依赖于基于风险因素和表现的预测试概率（PTP），有限的性能。红外热成像(IRT),一种检测表面温度的非接触式技术，已经显示出评估动脉粥样硬化相关疾病的潜力，特别是从身体区域如面部测量时。我们旨在评估将面部IRT温度信息与机器学习一起用于CAD预测的可行性。
方法：纳入有创冠状动脉血管造影术或冠状动脉CT血管造影术（CCTA）的患者。在验证性CAD检查之前捕获的面部IRT图像用于开发和验证用于检测CAD的深度学习IRT图像模型。我们在曲线下面积（AUC）上比较了IRT图像模型与指南推荐的PTP模型的性能。此外,从IRT图像中提取可解释的IRT表格特征,进一步验证IRT信息的预测价值。
结果：总共460名符合条件的参与者（平均（SD）年龄，包括58.4（10.4）岁；126（27.4％）女性。与PTP模型（AUC0.713，95％CI0.691至0.734）相比，IRT图像模型表现出出色的性能（AUC0.804，95％CI0.785至0.823）。一致的卓越表现水平(AUC0.796，95%CI0.782至0.811)，通过全面的可解释的IRT功能实现，进一步验证了IRT信息的预测价值。值得注意的是,即使只有传统的温度特征，仍维持令人满意的表现(AUC0.786,95%CI0.769~0.803).
结论：在这项前瞻性研究中，我们证明了使用非接触面部IRT信息进行CAD预测的可行性。
方法：纳入有创冠状动脉血管造影术或冠状动脉CT血管造影术（CCTA）的患者。在验证性CAD检查之前捕获的面部IRT图像用于开发和验证用于检测CAD的深度学习IRT图像模型。我们在曲线下面积（AUC）上比较了IRT图像模型与指南推荐的PTP模型的性能。此外,从IRT图像中提取可解释的IRT表格特征,进一步验证IRT信息的预测价值。
结果：总共460名符合条件的参与者（平均（SD）年龄，包括58.4（10.4）岁；126（27.4％）女性。与PTP模型（AUC0.713，95％CI0.691至0.734）相比，IRT图像模型表现出出色的性能（AUC0.804，95％CI0.785至0.823）。一致的卓越表现水平(AUC0.796，95%CI0.782至0.811)，通过全面的可解释的IRT功能实现，进一步验证了IRT信息的预测价值。值得注意的是,即使只有传统的温度特征，仍维持令人满意的表现(AUC0.786,95%CI0.769~0.803).
结论：在这项前瞻性研究中，我们证明了使用非接触面部IRT信息进行CAD预测的可行性。
