The contrastive learning model combined with ECG data and 3D CMR metrics achieved an average R2 of 0.605 for predicting CMR features, enhancing ECG-based risk stratification for CVD.
Does enriching ECG representations with CMR features using contrastive learning improve the prediction of structural cardiac features and cardiovascular disease?
Contrastive learning can effectively enrich ECG representations with 3D volume CMR embeddings to predict structural cardiac features, offering a scalable approach for risk stratification.
Abstract Background Cardiovascular disease (CVD) remains the leading cause of mortality. Early detection of CVD requires diagnostic tools that are scalable, accessible, and low-cost. While cardiac magnetic resonance imaging (CMR) provides detailed structural and functional cardiac information, its limited availability and high costs restrict widespread use. In contrast, the electrocardiogram (ECG) is widely available but lacks the rich anatomical and mechanical information of the CMR. We hypothesize that ECG-based latent representations can be enriched with CMR features by leveraging contrastive learning (CL). Purpose We aim to develop a CL framework that fuses CMR metrics to the ECG signal representation and use it to predict CMR features and CVD outcomes. Methods We used 63,448 subjects from the UK Biobank with same-day short-axis cine CMR and 12-lead resting ECG recordings. A pretrained segmentation model was used to crop the CMR images around the heart. As proposed by previous work, the model learns cross-modal latent representations by minimizing the distance between data from the same subject, and maximizing the distance between pairs from different participants, described in Figure 1. The model learns cross-modal latent representations by minimizing the distance between data from the same subject, and maximizing the distance between pairs from different participants, described in Figure 1. We further enhance the CMR representation with 3D cardiac volumes from ES and ED timepoints. We evaluated the ECG encoder's ability to predict: (1) CMR metrics, including left ventricular ejection fraction (LVEF), right ventricular ejection fraction (RVEF) and cardiac output; and (2) the most prevalent cardiovascular diseases, including coronary artery disease (CAD), atrial fibrillation (AF), sudden cardiac death (SCD), heart failure (HF), myocardial infarction (MI) and cardiomyopathy (CMP). We used 47,527/10,185/10,185 subjects for the training/validation/held-out test cohorts. We compared five models: (1) ECG only, (2) ECG with CL trained on one mid-ventricular end-diastolic slice from the CMR image, (3) ECG with CL trained on 2D+time data of one middle slice over time, (4) ECG with CL using 3D CMR data with multiple slices over the volume, and (5) ECG with triplet contrastive loss (TCL) with 4D CMR data combining the end-diastolic and end-systolic volumes over time. Results The model with TCL showed the highest performance for the prediction of CMR features, with an average R2 of 0.605 (Figure 2). Adding temporal dynamics and 3D volume improved the model performance compared to using a single image. However, it did not improve the performance of the clinical endpoints. Conclusion We design a CL pre-training strategy that proves effective in enriching ECG representations with 3D volume CMR derived embeddings, enabling a low cost and non-invasive ECG-based risk stratification for cardiac pathology in the general population.
Gomez et al. (2026) studied this question. The contrastive learning model combined with ECG data and 3D CMR metrics achieved an average R2 of 0.605 for predicting CMR features, enhancing ECG-based risk stratification for CVD.
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