{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T01:51:44Z","timestamp":1792547504530,"version":"4.5.0"},"reference-count":44,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"10","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Circulation"],"published-print":{"date-parts":[[2026,9,8]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>Chagas disease (ChD), a neglected cardiovascular condition, affects 7.5 to 10.5 million people worldwide. Opportunistic screening during routine electrocardiography may allow earlier detection of unrecognized infections, enabling timely antiparasitic therapy or optimized cardiac care. This study prospectively evaluated the feasibility and diagnostic accuracy of an artificial intelligence (AI)\u2013enabled ECG model enriched with 3 epidemiological questions (AI-ECG-EPI model) as an opportunistic screening for ChD in endemic regions of Brazil.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>We conducted a prospective study embedded in the Telehealth Network of Minas Gerais, Brazil, across 107 municipalities in hyperendemic and endemic regions. From October 2023 to September 2024, adults undergoing routine tele-ECGs were eligible. The primary exposure was the output of the AI-ECG-EPI model: all positive individuals and a 3:1 sample of negative individuals were invited for serology (index test\u2013dependent sampling), the reference standard. Weighted analyses (inverse probability) accounted for this sampling design. Diagnostic metrics included sensitivity, specificity, predictive values, likelihood ratios, area under the receiver operating characteristics curve, and area under the precision recall curve. The AI-ECG-EPI model was compared with both an epidemiological questions model and an AI ECG\u2013only model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>\n                      Among 75\u2009779 eligible ECGs, the AI-ECG-EPI model was available for 59\u2009154 individuals; 6812 (11.5%) screened positive. A total of 7804 selected were invited for serology, and 3509 completed testing (2517 positive; 992 negative). ChD was confirmed in 36.8% of positive and 9.3% of negative individuals. The weighted prevalence of ChD was 12.2%. Sensitivity was 34.3%, specificity 91.6%, harmonic mean of precision and recall score 0.52, and diagnostic odds ratio 5.69. The AI-ECG-EPI model showed higher discrimination than both the epidemiological questions model and AI ECG\u2013only model (area under the receiver operating characteristics curve, 77.6% versus 70.0% and 64.5%, respectively;\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt;0.001). Precision recall curves showed higher precision across most recall levels. Exploratory analysis suggested improved risk reclassification (net reclassification improvement, 0.503). Performance was higher in women and in individuals with Chagas cardiomyopathy.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>In this community-based study, an AI-ECG-EPI model integrated into a public telecardiology network enabled feasible opportunistic screening for ChD in primary care. It showed acceptable diagnostic accuracy, with higher discrimination than epidemiological questions and AI ECG\u2013only models, and identified many previously undiagnosed patients in endemic regions, supporting its potential role in scalable screening strategies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>REGISTRATION:<\/jats:title>\n                    <jats:p>\n                      URL:\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/www.clinicaltrials.gov\">https:\/\/www.clinicaltrials.gov<\/jats:ext-link>\n                      ; Unique identifier: NCT02646943.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/circulationaha.125.078949","type":"journal-article","created":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T17:03:22Z","timestamp":1792429402000},"page":"879-891","update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["Opportunistic Screening for Chagas Disease Using an Artificial Intelligence\u2013Enabled ECG: Prospective Evaluation of Feasibility and Diagnostic Accuracy"],"prefix":"10.1161","volume":"154","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2740-0042","authenticated-orcid":true,"given":"Antonio Luiz Pinho","family":"Ribeiro","sequence":"first","affiliation":[{"name":"Department of Internal Medicine, School of Medicine, Telehealth Center, and Cardiology Service (A.L.P.R., M.C.P.N.), Hospital das Cl\u00ednicas, Universidade Federal de Minas Gerais, Belo Horizonte, 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