biostudies-literatureBrant LCCNIA NIH HHSNHLBI NIH HHSNIH HHSe013734https://www.ebi.ac.uk/biostudies/studies/S-EPMC12569998biostudies-literatureUnknown18(10)<h4>Background</h4>We aimed to refine and validate a deep neural network model from the ECG to predict atrial fibrillation (AF) risk, using samples from diverse backgrounds: the Framingham Heart Study (FHS), UK Biobank, and Estudo Longitudinal da Saúde do Adulto (ELSA-Brasil). We compared the model's performance to the clinical Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (CHARGE-AF) risk score and evaluated the association with other cardiovascular outcomes.<h4>Methods</h4>The ECG-derived deep-learning prediction of AF (ECG-AF) model was refined using 60% of FHS samples free of AF. Its performance was then tested in the remaining FHS samples, UK Biobank, and ELSA-Brasil, with discrimination assessed by the area under the receiver operating characteristic curve. Circulation. Arrhythmia and electrophysiologyPrediction of Atrial Fibrillation From the ECG in the Community Using Deep Learning: A Multinational Study.PMC12569998R01 AG028321R01 AG083735HHSN268201500001C75N92023D00012R01 HL092577R21 HL17558475N98025D00012N01 HC025195U01 AG068221R01 AG06601075N92019D00031HHSN268201500001IU54 HL120163Duncan BBLarson MGBarreto SMPinto-Filho MMEromosele OBLin HBrant LCCRibeiro AHRibeiro ALPBenjamin EJfalsePrediction of Atrial Fibrillation From the ECG in the Community Using Deep Learning: A Multinational Study.<h4>Background</h4>We aimed to refine and validate a deep neural network model from the ECG to predict atrial fibrillation (AF) risk, using samples from diverse backgrounds: the Framingham Heart Study (FHS), UK Biobank, and Estudo Longitudinal da Saúde do Adulto (ELSA-Brasil). We compared the model's performance to the clinical Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (CHARGE-AF) risk score and evaluated the association with other cardiovascular outcomes.<h4>Methods</h4>The ECG-derived deep-learning prediction of AF (ECG-AF) model was refined using 60% of FHS samples free of AF. Its performance was then tested in the remaining FHS samples, UK Biobank, and ELSA-Brasil, with discrimination assessed by the area under the receiver operating characteristic curve. 2025-01-01T00:00:00Z2025 Oct2026-06-10T05:19:06.396Z2026-06-10T03:07:44.846ZS-EPMC125699984102525210.1161/CIRCEP.125.01373410.1161/circep.125.013734