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Recently, an artificial intelligence (AI)-enabled ECG algorithm showed excellent accuracy to detect LVSD in a general population, but its accuracy in ChD has not been tested.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Objective<\/jats:title>\n<jats:p>To analyze the ability of AI to recognize LVSD in patients with ChD, defined as a left ventricular ejection fraction determined by the Echocardiogram \u2264 40%.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Methodology\/principal findings<\/jats:title>\n<jats:p>This is a cross-sectional study of ECG obtained from a large cohort of patients with ChD named S\u00e3o Paulo-Minas Gerais Tropical Medicine Research Center (SaMi-Trop) Study. The digital ECGs of the participants were submitted to the analysis of the trained machine to detect LVSD. The diagnostic performance of the AI-enabled ECG to detect LVSD was tested using an echocardiogram as the gold standard to detect LVSD, defined as an ejection fraction &lt;40%. The model was enriched with NT-proBNP plasma levels, male sex, and QRS \u2265 120ms.<\/jats:p>\n<jats:p>Among the 1,304 participants of this study, 67% were women, median age of 60; there were 93 (7.1%) individuals with LVSD. Most patients had major ECG abnormalities (59.5%). The AI algorithm identified LVSD among ChD patients with an odds ratio of 63.3 (95% CI 32.3\u2013128.9), a sensitivity of 73%, a specificity of 83%, an overall accuracy of 83%, and a negative predictive value of 97%; the AUC was 0.839. The model adjusted for the male sex and QRS \u2265 120ms improved the AUC to 0.859. The model adjusted for the male sex and elevated NT-proBNP had a higher accuracy of 0.89 and an AUC of 0.874.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusion<\/jats:title>\n<jats:p>The AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pntd.0009974","type":"journal-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T18:33:20Z","timestamp":1638815600000},"page":"e0009974","update-policy":"https:\/\/doi.org\/10.1371\/journal.pntd.corrections_policy","source":"Crossref","is-referenced-by-count":27,"title":["Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients\u2013The SaMi-Trop cohort"],"prefix":"10.1371","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3710-006X","authenticated-orcid":true,"given":"Bruno Oliveira de 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