# Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025

arXiv: https://arxiv.org/abs/2510.02202 · HTML: https://mlsanity.com/paper/2510.02202/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 33 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Builds on (most cited first, 2 of 2)

- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [On the Geometry of Receiver Operating Characteristic and Precision-Recall Curves](https://mlsanity.com/paper/2504.02169/) (2025)

## Built on by (most cited first, 2 of 2)

- [Biomarker-Based Pretraining for Chagas Disease Screening in Electrocardiograms](https://mlsanity.com/paper/2604.09782/) (2026)
- [Leveraging Cardiac Imaging to Improve ECG-Based Detection of Chagas Disease in Resource-Constrained Settings](https://mlsanity.com/paper/2609.08582/) (2026)

## Abstract (arXiv)

Objective: Chagas disease is a parasitic infection that is endemic to South America, Central America, and, more recently, the U.S., primarily transmitted by insects. Chronic Chagas disease can cause cardiovascular diseases and digestive problems. Serological testing capacities for Chagas disease are limited, but Chagas cardiomyopathy often manifests in ECGs, providing an opportunity to prioritize patients for testing and treatment. Approach: The George B. Moody PhysioNet Challenge 2025 invites teams to develop algorithmic approaches for identifying Chagas disease from electrocardiograms (ECGs). Main results: This Challenge provides multiple innovations. First, we leveraged several datasets with labels from patient reports and serological testing, provided a large dataset with weak labels and smaller datasets with strong labels. Second, we augmented the data to support model robustness and generalizability to unseen data sources. Third, we applied an evaluation metric that captured the local serological testing capacity for Chagas disease to frame the machine learning problem as a triage task. Significance: Over 630 participants from 111 teams submitted over 1300 entries during the Challenge, representing diverse approaches from academia and industry worldwide.