Deep Learning–Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects

Evann Eisenberg,Priscilla McElhinney,Frederic Commandeur,Xi Chen,Sebastien Cadet,Markus Goeller,Aryabod Razipour,Heidi Gransar,Stephanie Cantu,Robert J. H. Miller,Piotr J. Slomka,Nathan D. Wong,Alan Rozanski,Stephan Achenbach,Balaji Tamarappoo,Daniel S. Berman,Damini Dey

Deep Learning–Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects

2020

Evann Eisenberg
Priscilla McElhinney
Frederic Commandeur
Xi Chen
Sebastien Cadet
Markus Goeller
Aryabod Razipour
Heidi Gransar
Stephanie Cantu
Robert J. H. Miller
Piotr J. Slomka
Nathan D. Wong
Alan Rozanski
Stephan Achenbach
Balaji Tamarappoo
Daniel S. Berman
Damini Dey

Background: Epicardial adipose tissue (EAT) volume (cm3) and attenuation (Hounsfield units) may predict major adverse cardiovascular events (MACE). We aimed to evaluate the prognostic value of full...

Keywords:

Adipose tissue
Mace
Tomography
Radiology
Epicardial adipose tissue
Internal medicine
Asymptomatic
Cardiology
Hounsfield scale
Medicine

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