Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025
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arXiv
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| Natura: | Preprint |
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2025
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| _version_ | 1866915530898669568 |
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| author | Reyna, Matthew A. Koscova, Zuzana Pavlus, Jan Saghafi, Soheil Weigle, James Elola, Andoni Seyedi, Salman Campbell, Kiersten Li, Qiao Rad, Ali Bahrami Ribeiro, Antônio H. Ribeiro, Antonio Luiz P. Sameni, Reza Clifford, Gari D. |
| author_facet | Reyna, Matthew A. Koscova, Zuzana Pavlus, Jan Saghafi, Soheil Weigle, James Elola, Andoni Seyedi, Salman Campbell, Kiersten Li, Qiao Rad, Ali Bahrami Ribeiro, Antônio H. Ribeiro, Antonio Luiz P. Sameni, Reza Clifford, Gari D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02202 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025 Reyna, Matthew A. Koscova, Zuzana Pavlus, Jan Saghafi, Soheil Weigle, James Elola, Andoni Seyedi, Salman Campbell, Kiersten Li, Qiao Rad, Ali Bahrami Ribeiro, Antônio H. Ribeiro, Antonio Luiz P. Sameni, Reza Clifford, Gari D. Machine Learning Artificial Intelligence 68Txx 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. |
| title | Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025 |
| topic | Machine Learning Artificial Intelligence 68Txx |
| url | https://arxiv.org/abs/2510.02202 |