Biomarker-Based Pretraining for Chagas Disease Screening in Electrocardiograms
For researchers and clinicians needing automated Chagas disease screening from ECGs, this work offers a pretraining strategy to overcome label scarcity, though the improvement is incremental.
The authors tackle Chagas disease screening from ECGs, which is limited by scarce/noisy labels. Their biomarker-based pretraining approach achieved a challenge score of 0.269 on the hidden test set, ranking 5th in the PhysioNet Challenge 2025.
Chagas disease screening via ECGs is limited by scarce and noisy labels in existing datasets. We propose a biomarker-based pretraining approach, where an ECG feature extractor is first trained to predict percentile-binned blood biomarkers from the MIMIC-IV-ECG dataset. The pretrained model is then fine-tuned on Brazilian datasets for Chagas detection. Our 5-model ensemble, developed by the Ahus AIM team, achieved a challenge score of 0.269 on the hidden test set, ranking 5th in Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025. Source code and the model are shared on GitHub: github.com/Ahus-AIM/physionet-challenge-2025