Assessment of ventricular tachyarrhythmia in patients with hypertrophic cardiomyopathy with machine learning-based texture analysis of late gadolinium enhancement cardiac MRI.

Journal: Diagnostic and interventional imaging

Published Date: Mar 1, 2020

Abstract

OBJECTIVE: To assess the diagnostic value of machine learning-based texture feature analysis of late gadolinium enhancement images on cardiac magnetic resonance imaging (MRI) for assessing the presence of ventricular tachyarrhythmia (VT) in patients with hypertrophic cardiomyopathy.

Authors

D Alis

Istanbul Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, Department of Radiology, Halkali/Istanbul, Turkey. Electronic address: drdenizalis@gmail.com.
A Guler

Istanbul Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, Department of Cardiology, Halkali/Istanbul, Turkey.
M Yergin

Bahcesehir University, Department of Software Engineering and applied sciences, Istanbul, Turkey.
O Asmakutlu

Istanbul Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, Department of Radiology, Halkali/Istanbul, Turkey.

Keywords

Adult Aged Cardiac Imaging Techniques Cardiomyopathy, Hypertrophic Contrast Media Female Gadolinium Humans Image Enhancement Machine Learning Magnetic Resonance Imaging Male Middle Aged Retrospective Studies Tachycardia, Ventricular Young Adult

External Resources

View on PubMed Access via DOI PubMed (31727603)

Assessment of ventricular tachyarrhythmia in patients with hypertrophic cardiomyopathy with machine learning-based texture analysis of late gadolinium enhancement cardiac MRI.

Abstract

Authors

Keywords

External Resources

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