Artificial Intelligence for Detection of Prognostically Significant Left Ventricular Dysfunction From Echocardiography.

David Playford¹, Simon Stewart², Andrew Watts³, Dean Kezurer³, Yih-Kai Chan⁴, Geoff Strange⁵

¹Institute for Health Research, The University of Notre Dame Australia, Fremantle, Western Australia, Australia. Electronic address: dplayford@neda.net.au.

²Institute for Health Research, The University of Notre Dame Australia, Fremantle, Western Australia, Australia; BHF Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom.

³Echo IQ Ltd, Sydney, New South Wales, Australia.

⁴Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria, Australia.

⁵Institute for Health Research, The University of Notre Dame Australia, Fremantle, Western Australia, Australia; Heart Research Institute, University of Sydney, Sydney, New South Wales, Australia.

PubMed PMC DOI PDF

Abstract

BACKGROUND: Identification of left ventricular (LV) dysfunction following echocardiographic investigations remains problematic, particularly when the ejection fraction (EF) is preserved. OBJECTIVES: The authors examined the operational characteristics of artificial intelligence LV dysfunction (AI-LVD) identification from routinely obtained echocardiographic measurements. METHODS: Following initi…

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