Leveraging Cardiac Imaging to Improve ECG-Based Detection of Chagas Disease in Resource-Constrained Settings

TL;DR - A contrastively pretrained ECG model transfers structural knowledge from cardiac MRI to improve Chagas disease detection using widely available ECG data. This could support cardiac screening in endemic regions where MRI scanners and expert readers are scarce.

• Pretraining aligned an ECG encoder with clinically grounded MRI embeddings using asymmetric InfoNCE on 63,193 paired UK Biobank examinations.
• Although pretraining included no Chagas cases, a frozen linear probe improved cross-validated AUROC from 0.827 to 0.851 across CODE-15% and SaMi-Trop.
• Sensitivity among the top 5% of predicted-risk patients increased from 0.377 to 0.427 over an unaligned ECG foundation-model baseline.
• On the PhysioNet/CinC 2025 test set, the model achieved the highest SaMi-Trop-3 AUROC and best ELSA-Brasil challenge score among the three leading methods.