Key result
Auxiliary-conditioned cross-attention ECG framework achieves a 0.218 score for Chagas disease detection.
Why the study?
Automated 12-lead ECG screening for Chagas disease remains challenging due to dataset heterogeneity and label uncertainty when combining strongly labeled cohorts with weakly labeled repositories.
Conditioning deep learning models on interpretable QRS and conduction descriptors provides a physiologically informed framework for Chagas disease screening from ECGs, though generalization across heterogeneous cohorts remains challenging.
Answers are grounded in this paper and cite their sources.
Model performance was modest in the 2025 PhysioNet Challenge; leaves open the role of deep learning for Chagas ECG screening pending external validation.
OBJECTIVE: Chagas disease remains a major public-health concern in endemic regions, and chronic cardiac involvement often manifests as conduction abnormalities detectable on standard 12-lead electrocardiograms (ECGs). Reliable automated screening remains challenging because of dataset heterogeneity and label uncertainty, particularly when combining strongly labeled cohorts with large weakly labeled repositories. APPROACH: We propose a hybrid architecture that integrates a 1D ResNet encoder for local ECG morphology, a bidirectional GRU for long-range temporal context, and handcrafted physiological features and demographics through an auxiliary-conditioned cross-attention module. The auxiliary vector, comprising age, sex, and QRS/conduction descriptors, is projected into a query token that selectively attends to deep sequential embeddings for feature-aware temporal aggregation. To exploit heterogeneous sources while reflecting source reliability, we further adopt a source-aware weighted binary cross-entropy objective. MAIN RESULTS: As team CAUETUMN in the PhysioNet/Computing in Cardiology Challenge 2025, the framework achieved a score of 0.347 on the organizer-held REDS-II leaderboard-validation set during the official phase and 0.218 on the final hidden test set, ranking 17th among 41 eligible teams. SIGNIFICANCE: These results suggest that conditioning detection on interpretable QRS and conduction descriptors supports a transparent and physiologically informed screening framework, while highlighting the difficulty of generalizing across heterogeneous cohorts.
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Im et al. (2026) studied Chagas disease. Auxiliary-conditioned cross-attention hybrid architecture was evaluated on Challenge score on the final hidden test set. An auxiliary-conditioned cross-attention framework for Chagas disease detection from ECGs achieved a score of 0.218 on the final hidden test set, ranking 17th in the PhysioNet Challenge 2025.
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