Tao Rong, Zhang Shunlin, Zhang Rui, Shen Chengxing, Ma Jian, Cui Jianguo, Chen Yundai, Wang Bo, Li Hailing, Xie Xiaoming, Zheng Guoyan
Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.
Sci Rep. 2025 Feb 19;15(1):6094. doi: 10.1038/s41598-025-90615-x.
Early diagnosis and localization of myocardial ischemia (MS) and coronary artery stenosis (CAS) play a crucial role in the effective prevention and management of ischemic heart disease (IHD). Magnetocardiography (MCG) has emerged as a promising approach for non-invasive, non-contact, and high-sensitivity assessment of cardiac dysfunction. This study presents a multi-center, AI-enabled diagnosis and localization of myocardial ischemia and coronary artery stenosis from MCG data. To this end, we collected a large-scale dataset consisting of 2,158 MCG recordings from eight clinical centers. We then proposed a multiscale vision transformer-based network for extracting spatio-temporal information from multichannel MCG recordings. Anatomical prior knowledge of the coronary artery and the irrigated left ventricular regions was incorporated by a carefully designed graph convolutional network (GCN)-based feature fusion module. The proposed approach achieved an accuracy of 84.7%, a sensitivity of 83.8%, and a specificity of 85.6% in diagnosing IHD, an average accuracy of 78.4% in localization of five MS regions, and an average accuracy of 65.3% in localization of stenosis in three coronary arteries. Subsequent validation on an independent validation dataset consisting of 268 MCG recordings collected from four clinical centers demonstrated an accuracy of 82.3%, a sensitivity of 83.8%, and a specificity of 81.3% in diagnosing IHD, an average accuracy of 77.3% in localization of five myocardial ischemic regions, and an average accuracy of 65.6% in localization of stenosis in three coronary arteries. The proposed approach can be used as a fast and accurate diagnosis tool, boosting the integration of MCG examination into clinical routine.
心肌缺血（MS）和冠状动脉狭窄（CAS）的早期诊断与定位在缺血性心脏病（IHD）的有效预防和管理中起着至关重要的作用。心磁图（MCG）已成为一种用于心脏功能障碍的非侵入性、非接触式和高灵敏度评估的有前景的方法。本研究提出了一种基于多中心、人工智能的心磁图数据心肌缺血和冠状动脉狭窄诊断与定位方法。为此，我们收集了一个大规模数据集，该数据集由来自八个临床中心的2158份心磁图记录组成。然后，我们提出了一种基于多尺度视觉Transformer的网络，用于从多通道心磁图记录中提取时空信息。通过精心设计的基于图卷积网络（GCN）的特征融合模块，纳入了冠状动脉和灌注左心室区域的解剖学先验知识。所提出的方法在诊断IHD方面的准确率为84.7%，灵敏度为83.8%，特异性为85.6%；在五个MS区域定位方面的平均准确率为78.4%；在三条冠状动脉狭窄定位方面的平均准确率为65.3%。随后在一个独立验证数据集上进行验证，该数据集由从四个临床中心收集的268份心磁图记录组成，结果显示在诊断IHD方面的准确率为82.3%，灵敏度为83.8%，特异性为81.3%；在五个心肌缺血区域定位方面的平均准确率为77.3%；在三条冠状动脉狭窄定位方面的平均准确率为65.6%。所提出的方法可作为一种快速准确的诊断工具，促进心磁图检查融入临床常规。
