用心电图预测严重狭窄，首次实现高精度诊断。
Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification
- 通过跨模态对比学习融合心电图与血管造影特征
- 在多个严重程度阈值下均显著提升分类性能
- 适合心血管早期筛查与无症状人群检测
冠状动脉狭窄是常见心血管疾病，未及时治疗可能引发心肌梗死。尽管冠状动脉（X射线）造影仍是诊断标准，但其具有侵入性、耗时且资源密集，仅对高风险患者进行。部分无症状患者因此漏诊。心电图（ECG）快速、廉价、非侵入，常用于无症状者，但目前尚未发现可靠的狭窄特异性信号，无法用于风险分层。为此，我们提出StenCE预训练框架，直接从心电图中提取特征进行患者分层。在不同狭窄严重度阈值及额外的ECG疾病分类任务中，该方法在多种ECG编码器上均表现优异，优于先前工作。所获模型成功捕捉到心电图中的狭窄诊断信号，是首个在严重狭窄分类中达到高精度的方案。代码已开源：https://github.com/NikolaCenic/ecg-stenosis-cls。
原文摘要 · Abstract (English)
Coronary artery stenosis is a common cardiovascular disease, with severe, untreated cases posing significant risks of heart attack. Although coronary (X-ray) angiograms remain the standard for stenosis diagnosis, they are invasive, time- and resource-intensive, and therefore only performed on patients with a high probability of disease based on symptoms and prior clinical tests. However, a subset of patients, especially those without symptoms, may remain undiagnosed. Detecting indications of stenosis from ECGs, which are fast, cheap, non-invasive, and thus routinely acquired even in asymptomatic patients, would support early diagnosis. However, as no reliable stenosis-specific signal has been identified in ECGs, they can not currently be used for stenosis risk stratification. To address this, we introduce StenCE, a pretraining framework, allowing stratification of patients based on features derived directly from ECGs. Evaluations across varying stenosis severity thresholds and additional ECG disease classification tasks demonstrate consistent performance improvements across different ECG encoders, outperforming previous work. The obtained models successfully detect signals for stenosis diagnosis in ECGs and are the first to achieve high performance in severe stenosis classification. The source code is available at https://github.com/NikolaCenic/ecg-stenosis-cls.
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