{"doi":"10.1161/circep.125.013734","title":"Prediction of Atrial Fibrillation From the ECG in the Community Using Deep Learning: A Multinational Study","abstract":"BACKGROUND: 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. METHODS: 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. The association of ECG-AF with cardiovascular outcomes was assessed using Cox proportional hazards models. RESULTS: The study sample included 10 097 FHS participants (mean age 53±12 years; 54.9% women), 49 280 participants from the UK Biobank (mean age 64±8 years, 47.9% women), and 12 284 participants from ELSA-Brasil (mean age 53±8 years, 54.7% women). The ECG-AF model showed moderate discrimination for incident AF (area under the curve, 0.82 [95% CI, 0.80-0.84]) in the FHS, comparable to the CHARGE-AF score (area under the curve, 0.83 [95% CI, 0.81-0.85]), and incremental when combined (area under the curve, 0.85 [95% CI, 0.83-0.87]). In UK Biobank and ELSA-Brasil, combining ECG-AF and CHARGE also improved prediction. Higher ECG-AF scores were associated with increased risks of heart failure, myocardial infarction, stroke, and all-cause mortality in all 3 cohorts. CONCLUSIONS: In multinational cohort studies, the single-input ECG-AF deep neural network model demonstrated good performance in predicting AF and other cardiovascular outcomes, comparable to a multivariable clinical risk score, with improved performance when combined.","journal":"Circulation Arrhythmia and Electrophysiology","year":2025,"id":514947,"datarank":0.31191623125197543,"base_score":2.0794415416798357,"endowment":2.0794415416798357,"self_citation_contribution":0.31191623125197543,"citation_network_contribution":0.0,"self_endowment_contribution":0.31191623125197543,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9558,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":621700,"name":"Antônio H. Ribeiro","orcid":"0000-0003-3632-8529","position":1,"is_corresponding":false},{"id":1320792,"name":"Oseiwe Eromosele","orcid":"0000-0002-3121-5704","position":2,"is_corresponding":false},{"id":360580,"name":"Marcelo Martins Pinto Filho","orcid":"0000-0002-6646-1163","position":3,"is_corresponding":false},{"id":621705,"name":"Sandhi Maria Barreto","orcid":"0000-0001-7383-7811","position":4,"is_corresponding":false},{"id":22471,"name":"Bruce Bartholow Duncan","orcid":"0000-0002-7491-2630","position":5,"is_corresponding":false},{"id":230820,"name":"Martin G. Larson","orcid":"0000-0002-9631-1254","position":6,"is_corresponding":false},{"id":1034,"name":"Emelia J. Benjamin","orcid":"0000-0003-4076-2336","position":7,"is_corresponding":false},{"id":61511,"name":"Antônio Luiz Pinho Ribeiro","orcid":"0000-0002-2740-0042","position":8,"is_corresponding":false},{"id":24806,"name":"Honghuang Lin","orcid":"0000-0003-3043-3942","position":9,"is_corresponding":false},{"id":61452,"name":"Luísa Campos Caldeira Brant","orcid":"0000-0002-7317-1367","position":0,"is_corresponding":true}],"reference_count":53,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:48:34.431522Z","pmid":"41025252","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}