根据非接触式红外热成像捕捉到的面部温度信息预测冠状动脉疾病。

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES
Minghui Kung, Juntong Zeng, Shen Lin, Xuexin Yu, Chang Liu, Mengnan Shi, Runchen Sun, Shangyuan Yuan, Xiaocong Lian, Xiaoting Su, Yan Zhao, Zhe Zheng, Xiangyang Ji
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引用次数: 0

摘要

背景:目前的冠状动脉疾病(CAD)初步评估方法依赖于基于风险因素和表现的预检概率(PTP),但效果有限。红外热成像(IRT)是一种检测表面温度的非接触式技术,已显示出评估动脉粥样硬化相关疾病的潜力,尤其是从面部等身体部位测量时。我们旨在评估利用面部 IRT 温度信息和机器学习预测 CAD 的可行性:方法:我们招募了接受有创冠状动脉造影术或冠状动脉 CT 血管造影术(CCTA)的患者。在进行确诊冠状动脉粥样硬化检查之前拍摄的面部 IRT 图像被用于开发和验证用于检测冠状动脉粥样硬化的深度学习 IRT 图像模型。我们比较了 IRT 图像模型与指南推荐的 PTP 模型在曲线下面积 (AUC) 方面的性能。此外,我们还从IRT图像中提取了可解释的IRT表格特征,以进一步验证IRT信息的预测价值:共纳入了 460 名符合条件的参与者(平均(标清)年龄 58.4(10.4)岁;女性 126(27.4%)人)。与 PTP 模型(AUC 0.713,95% CI 0.691 至 0.734)相比,IRT 图像模型表现出色(AUC 0.804,95% CI 0.785 至 0.823)。综合可解释的 IRT 特征实现了一致的卓越性能水平(AUC 0.796,95% CI 0.782 至 0.811),进一步验证了 IRT 信息的预测价值。值得注意的是,即使仅使用传统的体温特征,仍能保持令人满意的性能(AUC 0.786,95% CI 0.769 至 0.803):在这项前瞻性研究中,我们证明了使用非接触式面部 IRT 信息预测 CAD 的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of coronary artery disease based on facial temperature information captured by non-contact infrared thermography.

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.

Conclusion: In this prospective study, we demonstrated the feasibility of using non-contact facial IRT information for CAD prediction.

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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
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