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Meas."],"published-print":{"date-parts":[[2026,9,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Chagas disease, a neglected tropical disease (NTD) with significant cardiovascular impact, remains underdiagnosed in resource-limited regions. Electrocardiogram (ECG) screening offers a low-cost tool for detecting cardiac involvement, yet algorithm development is challenged by label noise, data scarcity, and the latent nature of infection. This study proposes a robust ECG-based screening framework that explicitly addresses these constraints.\n                    <jats:italic>Approach.<\/jats:italic>\n                    We introduce a\n                    <jats:italic>Reliability-Aware Hierarchical Learning<\/jats:italic>\n                    strategy that calibrates supervision according to data provenance, prioritizing serology-confirmed labels over noisy self-reports. To mitigate data scarcity, we compare a specialized convolutional neural network (CNN) trained from scratch with a transfer learning approach based on a Spatio-Temporal ECG foundation Model (FM). Performance is evaluated across varying data scales, and the representation structure is analyzed to interpret model behavior.\n                    <jats:italic>Main results.<\/jats:italic>\n                    On the official hidden test set of the George B. Moody PhysioNet\/Computing in Cardiology Challenge 2025, our approach achieved a Challenge Score of 0.163. We observe that while the specialized CNN performs competitively in data-rich regimes, the FM exhibits superior robustness in extreme low-resource settings. Furthermore, performance reaches a plateau imposed by underlying disease physiology. Bimodal score distributions suggest that models distinguish established cardiomyopathy from indeterminate infection, which remains electrophysiologically indistinguishable from healthy controls.\n                    <jats:italic>Significance.<\/jats:italic>\n                    These findings clarify both the potential and intrinsic limits of ECG-based AI screening for NTD-associated cardiac involvement. Reliability-aware supervision and data-efficient transfer learning provide a practical framework toward scalable and clinically meaningful ECG screening systems in resource-constrained environments.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/ae98e0","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T22:55:22Z","timestamp":1786575322000},"page":"095003","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Reliability-aware hierarchical learning for Chagas disease screening from 12-lead ECGs: tackling label uncertainty and class imbalance"],"prefix":"10.1088","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8856-0883","authenticated-orcid":true,"given":"Hao","family":"Wen","sequence":"first","affiliation":[{"name":"College of Science, China Agricultural University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4263-1495","authenticated-orcid":false,"given":"Jingsu","family":"Kang","sequence":"additional","affiliation":[{"name":"Tianjin Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"key":"pmeaae98e0bib1","first-page":"pp 229","type":"book","article-title":"Electrocardiogram in neglected tropical diseases","author":"\u00c1lvarez Garc\u00eda","year":"2022"},{"key":"pmeaae98e0bib2","first-page":"pp 929","type":"conference-proceedings","article-title":"PyTorch 2: faster machine learning through dynamic python bytecode transformation and graph compilation","author":"Ansel","year":"2024"},{"key":"pmeaae98e0bib3","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2024.1393992","type":"journal-article","article-title":"In vitro diagnostic methods of chagas disease in the clinical laboratory: a scoping review","volume":"15","author":"Ascanio","year":"2024","journal-title":"Front. 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