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Biomarker-Based Pretraining for Chagas Disease Screening in Electrocardiograms

arXiv cs.CV / 4/14/2026

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Key Points

  • The paper proposes biomarker-based pretraining to improve ECG-based Chagas disease screening when existing datasets have scarce and noisy labels.
  • An ECG feature extractor is first pretrained on MIMIC-IV-ECG to predict percentile-binned blood biomarkers, and then fine-tuned on Brazilian datasets for Chagas detection.
  • Using a 5-model ensemble, the authors report a challenge score of 0.269 on the hidden test set, placing 5th in the 2025 George B. Moody PhysioNet Challenge for Chagas detection.
  • The work includes released code and a model on GitHub, enabling replication and further experimentation by other researchers.

Abstract

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

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