用心脏影像知识增强心电图，提升资源匮乏地区查加斯病检测能力
Leveraging Cardiac Imaging to Improve ECG-Based Detection of Chagas Disease in Resource-Constrained Settings
- 通过对比学习将心电图与心脏磁共振结构特征对齐
- 在多个数据集上实现0.851的AUROC和42.7%的高风险人群检出率
- 无需接触查加斯病病例即可提升诊断性能，适合临床部署
查加斯病是拉丁美洲心肌病的主要原因。心脏磁共振（CMR）可精准刻画其结构异常，但扫描仪和专业解读人员在流行区仍严重不足。心电图（ECG）成本低、普及广，但需从电活动间接推断结构病变。本文提出通过对比预训练，将CMR-derived结构知识迁移到ECG中。利用英国生物银行63,193对配对的ECG-CMR检查数据，采用非对称InfoNCE目标函数，将ECG编码器与具有临床意义的CMR嵌入空间对齐。尽管预训练过程中未接触任何查加斯病病例，所获表示仍显著提升基于ECG的查加斯病检测性能。在CODE-15%和SaMi-Trop数据集上，冻结线性探测器在五折交叉验证中达到0.851的AUROC和0.427的Top5%-TPR，优于未对齐的ECG-FM基线（0.827和0.377）。在PhysioNet/CinC 2025挑战赛测试集上，模型在SaMi-Trop-3上取得最高AUROC，且在ELSA-Brasil挑战赛中得分位居前三方法之首，表明影像监督的ECG表示具备跨人群与场景的泛化能力。
原文摘要 · Abstract (English)
Chagas disease is a major cause of cardiomyopathy in Latin America. Cardiac magnetic resonance (CMR) imaging can characterize its structural abnormalities, but scanners and expert readers remain scarce in endemic regions. Electrocardiography (ECG) is inexpensive and widely available, yet structural disease must be inferred indirectly from electrical signals. We propose to transfer CMR-derived structural knowledge to ECG through contrastive pre-training. Using 63,193 paired ECG-CMR examinations from the UK Biobank, we align an ECG encoder with a clinically grounded CMR embedding space using an asymmetric InfoNCE objective. Despite seeing no Chagas cases during pre-training, the resulting representation improves ECG-based Chagas detection. Across CODE-15% and SaMi-Trop, a frozen linear probe achieves an AUROC of 0.851 and sensitivity at the top 5% of predicted risk (Top5%-TPR) of 0.427 in five-fold cross-validation, compared with 0.827 and 0.377 for an unaligned ECG-FM baseline. On the PhysioNet/CinC 2025 Challenge test set, our model obtains the highest AUROC on SaMi-Trop-3 and the best ELSA-Brasil challenge score among the three top-performing methods, indicating that imaging-supervised ECG representations can generalize to populations and settings beyond the pre-training distribution.
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