作者
Shuo Wang, Hena Patel, Tamari Miller, Keith Ameyaw, Akhil Narang, Daksh Chauhan, Simran Anand, Emeka Anyanwu, Stephanie A Besser, Keigo Kawaji, Xing-Peng Liu, Roberto M Lang, Victor Mor-Avi, Amit R Patel
发表日期
2022/3/1
期刊
Cardiovascular Imaging
卷号
15
期号
3
页码范围
413-427
出版商
American College of Cardiology Foundation
简介
Objectives
The aim of this study was to determine whether left ventricular ejection fraction (LVEF) and right ventricular ejection fraction (RVEF) and left ventricular mass (LVM) measurements made using 3 fully automated deep learning (DL) algorithms are accurate and interchangeable and can be used to classify ventricular function and risk-stratify patients as accurately as an expert.
Background
Artificial intelligence is increasingly used to assess cardiac function and LVM from cardiac magnetic resonance images.
Methods
Two hundred patients were identified from a registry of individuals who underwent vasodilator stress cardiac magnetic resonance. LVEF, LVM, and RVEF were determined using 3 fully automated commercial DL algorithms and by a clinical expert (CLIN) using conventional methodology. Additionally, LVEF values were classified according to clinically important ranges: <35%, 35% to 50%, and …
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S Wang, H Patel, T Miller, K Ameyaw, A Narang… - Cardiovascular Imaging, 2022