Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study.
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| Title: | Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study. |
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| Authors: | Dhingra LS; Department of Internal Medicine, Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT 06510, USA.; Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT 06510, USA., Aminorroaya A; Department of Internal Medicine, Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT 06510, USA.; Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT 06510, USA., Sangha V; Department of Internal Medicine, Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT 06510, USA.; Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT 06510, USA.; Department of Engineering Science, University of Oxford, Oxford, UK., Pedroso AF; Department of Internal Medicine, Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT 06510, USA.; Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT 06510, USA., Asselbergs FW; Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands.; Institute of Health Informatics, University College London, London, UK.; The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK., Brant LCC; Faculdade de Medicina, Department of Internal Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.; Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil., Barreto SM; Faculdade de Medicina, Department of Preventive Medicine, School of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil., Ribeiro ALP; Faculdade de Medicina, Department of Internal Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.; Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil., Krumholz HM; Department of Internal Medicine, Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT 06510, USA.; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT 06510, USA.; Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA., Oikonomou EK; Department of Internal Medicine, Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT 06510, USA.; Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT 06510, USA., Khera R; Department of Internal Medicine, Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT 06510, USA.; Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT 06510, USA.; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT 06510, USA.; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT 06510, USA.; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA. |
| Source: | European heart journal [Eur Heart J] 2025 Mar 13; Vol. 46 (11), pp. 1044-1053. |
| Publication Type: | Journal Article; Multicenter Study |
| Journal Info: | Publisher: Oxford University Press Country of Publication: England NLM ID: 8006263 Publication Model: Print Cited Medium: Internet ISSN: 1522-9645 (Electronic) Linking ISSN: 0195668X NLM ISO Abbreviation: Eur Heart J Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1522-9645 |
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| DOI: | 10.1093/eurheartj/ehae914 |
