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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/127619
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dc.contributor.authorPlayford, D.-
dc.contributor.authorBordin, E.-
dc.contributor.authorMohamad, R.-
dc.contributor.authorStewart, S.-
dc.contributor.authorStrange, G.-
dc.date.issued2020-
dc.identifier.citationJACC: Cardiovascular Imaging, 2020; 13(4):1087-1090-
dc.identifier.issn1936-878X-
dc.identifier.issn1876-7591-
dc.identifier.urihttp://hdl.handle.net/2440/127619-
dc.description.abstractAbstract not available-
dc.description.statementofresponsibilityDavid Playford, Edward Bordin, Razali Mohamad, Simon Stewart, Geoff Strange-
dc.language.isoen-
dc.publisherElsevier-
dc.rights© 2020 by the American College of Cardiology Foundation. Published by Elsevier.-
dc.source.urihttp://dx.doi.org/10.1016/j.jcmg.2019.10.013-
dc.subjectAortic Valve-
dc.subjectHumans-
dc.subjectAortic Valve Stenosis-
dc.subjectDiagnosis, Computer-Assisted-
dc.subjectImage Interpretation, Computer-Assisted-
dc.subjectEchocardiography-
dc.subjectPrognosis-
dc.subjectSeverity of Illness Index-
dc.subjectPredictive Value of Tests-
dc.subjectVentricular Function, Left-
dc.subjectArtificial Intelligence-
dc.subjectDatabases, Factual-
dc.subjectAdult-
dc.subjectAged-
dc.subjectAged, 80 and over-
dc.subjectMiddle Aged-
dc.subjectFemale-
dc.subjectMale-
dc.subjectHemodynamics-
dc.subjectProof of Concept Study-
dc.titleEnhanced diagnosis of severe aortic stenosis using artificial intelligence: a proof-of-concept study of 530,871 echocardiograms-
dc.typeJournal article-
dc.identifier.doi10.1016/j.jcmg.2019.10.013-
pubs.publication-statusPublished-
dc.identifier.orcidStewart, S. [0000-0001-9032-8998]-
Appears in Collections:Aurora harvest 4
Medicine publications

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