Toward advanced diagnosis and management of inherited arrhythmia syndromes: Harnessing the capabilities of artificial intelligence and machine learning.

Asatryan, Babken; Bleijendaal, Hidde; Wilde, Arthur A M

Asatryan, Babken; Bleijendaal, Hidde; Wilde, Arthur A M.

Afiliação

Asatryan B; Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland. Electronic address: basatryan@yahoo.com.
Bleijendaal H; University of Amsterdam, Heart Center; Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Amsterdam, The Netherlands; Department of Clinical Epidemiology, Biostatistics and Bioinformatics, University of Amsterdam, Amsterdam, The Netherlands.
Wilde AAM; University of Amsterdam, Heart Center; Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Amsterdam, The Netherlands; Department of Clinical Epidemiology, Biostatistics and Bioinformatics, University of Amsterdam, Amsterdam, The Netherlands; European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart (ERN GUARD-Heart).

Heart Rhythm ; 20(10): 1399-1407, 2023 Oct.

Article em En | MEDLINE | ID: mdl-37442407

Palavras-chave

Brugada syndrome; Cardiomyopathy; Deep learning; Genetic testing; Genetics; Inherited arrhythmia syndrome; Long QT syndrome; Neural network; Prediction; Sudden cardiac death

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

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