Artificial intelligence using electrocardiography: strengths and pitfalls
- PMID: 33748841
- PMCID: PMC8347448
- DOI: 10.1093/eurheartj/ehab090
Artificial intelligence using electrocardiography: strengths and pitfalls
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Electrocardiogram screening for aortic valve stenosis using artificial intelligence.Eur Heart J. 2021 Aug 7;42(30):2885-2896. doi: 10.1093/eurheartj/ehab153. Eur Heart J. 2021. PMID: 33748852
References
-
- LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436–444. - PubMed
-
- Cohen-Shelly M, Attia ZI, Friedman PA, Ito S, Essayagh BA, Ko WY, Murphree DH, Michelena HI, Enriquez-Sarano M, Carter RE, Johnson PW, Noseworthy PA, Lopez-Jimenez F, Oh JK. Electrocardiogram screening for aortic valve stenosis using artificial intelligence. Eur Heart J 2021;42:2885–2895. - PubMed
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- Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med 2019;25:70–74. - PubMed
-
- Kwon J myoung, Kim KH, Medina-Inojosa J, Jeon KH, Park J, Oh BH. Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography. J Heart Lung Transpl 2020;39:805–814. - PubMed
-
- Galloway CD, Valys A V., Shreibati JB, Treiman DL, Petterson FL, Gundotra VP,, Albert DE, Attia ZI, Carter RE, Asirvatham SJ, Ackerman MJ, Noseworthy PA, Dillon JJ, Friedman PA. Development and validation of a deep-learning model to screen for hyperkalemia from the electrocardiogram. JAMA Cardiol 2019;4:428–436. - PMC - PubMed
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