Artificial Intelligence Aids Cardiac Image Quality Assessment for Improving Precision in Strain Measurements
Journal
JACC. Cardiovascular imaging
Journal Volume
14
Journal Issue
2
Pages
335-345
Date Issued
2021-02
Author(s)
Hung, Chung-Lieh
Ethan Tu, Yi-Chin
Abstract
The aim of this study was to develop an artificial intelligence tool to assess echocardiographic image quality objectively.
Subjects
artificial intelligence; automated strain analysis; cancer therapeutics−related cardiac dysfunction; left ventricular global longitudinal strain
SDGs
Other Subjects
accuracy; adult; Article; artificial intelligence; body mass; breast cancer; cardiovascular magnetic resonance; convolutional neural network; echocardiography; female; follow up; human; image quality; interrater reliability; limit of agreement; major clinical study; male; mastectomy; middle aged; prediction; predictive value; priority journal; test retest reliability; cine magnetic resonance imaging; heart left ventricle function; heart stroke volume; reproducibility; Artificial Intelligence; Humans; Magnetic Resonance Imaging, Cine; Predictive Value of Tests; Reproducibility of Results; Stroke Volume; Ventricular Function, Left
Type
journal article