Liang Hsin-Yueh, Hsu Kai-Cheng, Chien Shang-Yu, Yeh Chen-Yu, Sun Ting-Hsuan, Liu Meng-Hsuan, Ng Kee Koon
Division of Cardiology, Department of Medicine, China Medical University Hospital, Taichung, Taiwan.
Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan.
Front Artif Intell. 2025 Mar 17;8:1496109. doi: 10.3389/frai.2025.1496109. eCollection 2025.
The diagnostic power of exercise stress electrocardiography (ExECG) remains limited. We aimed to construct an artificial intelligence (AI)-based method to enhance ExECG performance to identify patients with significant coronary artery disease (CAD).
We retrospectively collected 818 patients who underwent both ExECG and coronary angiography (CAG) within 6 months. The mean age was 57.0 ± 10.1 years, and 614 (75%) were male patients. Significant coronary artery disease was seen in 369 (43.8%) CAG reports. We also included 197 individuals with normal ExECG and low risk of CAD. A convolutional recurrent neural network algorithm, integrating electrocardiographic (ECG) signals and features from ExECG reports, was developed to predict the risk of significant CAD. We also investigated the optimal number of inputted ECG signal slices and features and the weighting of features for model performance.
Using the data of patients undergoing CAG for training and test sets, our algorithm had an area under the curve, sensitivity, and specificity of 0.74, 0.86, and 0.47, respectively, which increased to 0.83, 0.89, and 0.60, respectively, after enrolling 197 subjects with low risk of CAD. Three ECG signal slices and 12 features yielded optimal performance metrics. The principal predictive feature variables were sex, maximum heart rate, and ST/HR index. Our model generated results within one minute after completing ExECG.
The multimodal AI algorithm, leveraging deep learning techniques, efficiently and accurately identifies patients with significant CAD using ExECG data, aiding clinical screening in both symptomatic and asymptomatic patients. Nevertheless, the specificity remains moderate (0.60), suggesting a potential for false positives and highlighting the need for further investigation.
运动应激心电图（ExECG）的诊断能力仍然有限。我们旨在构建一种基于人工智能（AI）的方法，以提高ExECG识别严重冠状动脉疾病（CAD）患者的性能。
我们回顾性收集了818例在6个月内同时接受ExECG和冠状动脉造影（CAG）的患者。平均年龄为57.0±10.1岁，其中614例（75%）为男性患者。369份（43.8%）CAG报告显示存在严重冠状动脉疾病。我们还纳入了197例ExECG正常且CAD风险较低的个体。开发了一种卷积循环神经网络算法，整合心电图（ECG）信号和ExECG报告中的特征，以预测严重CAD的风险。我们还研究了输入ECG信号切片和特征的最佳数量以及特征对模型性能的权重。
使用接受CAG检查患者的数据作为训练集和测试集，我们的算法曲线下面积、敏感性和特异性分别为0.74、0.86和0.47，在纳入197例CAD风险较低的受试者后，分别提高到0.83、0.89和0.60。三个ECG信号切片和12个特征产生了最佳性能指标。主要预测特征变量为性别、最大心率和ST/HR指数。我们的模型在完成ExECG后一分钟内生成结果。
利用深度学习技术的多模态AI算法，使用ExECG数据高效准确地识别严重CAD患者，有助于有症状和无症状患者的临床筛查。然而，特异性仍然适中（0.60），表明存在假阳性的可能性，并突出了进一步研究的必要性。
