Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography.

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Abstract

Machine-learning models may aid cardiac phenotypic recognition by using features of cardiac tissue deformation.

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Journal

Journal ID (iso-abbrev): J. Am. Coll. Cardiol.

Title: Journal of the American College of Cardiology

Publisher: Elsevier BV

ISSN (Electronic): 1558-3597

ISSN (Print): 0735-1097

Publication date (Electronic): Nov 29 2016

Volume: 68

Issue: 21

Affiliations

[1 ] Zena and Michael A. Weiner Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York.

[2 ] Institute of Next Generation Healthcare, Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, New York.

[3 ] Zena and Michael A. Weiner Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Internal Medicine, Medical Division, National Research Center, Cairo, Egypt.

[4 ] Zena and Michael A. Weiner Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York. Electronic address: partho.sengupta@mountsinai.org.

Article

Publisher Item ID: S0735-1097(16)36250-7

DOI: 10.1016/j.jacc.2016.08.062

PubMed ID: 27884247

SO-VID: 4164f84f-5322-4214-b5be-762ec8c4193e

History

Keywords: cardiomyopathy,decision support systems,left ventricular hypertrophy,speckle-tracking echocardiography

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Keywords: cardiomyopathy, decision support systems, left ventricular hypertrophy, speckle-tracking echocardiography

Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography.

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