KUAN-CHIH HUANGCHIUN-SHENG HUANGMAO-YUAN SUHung, Chung-LiehChung-LiehHungEthan Tu, Yi-ChinYi-ChinEthan TuLin, Lung-ChunLung-ChunLinJUEY-JEN HWANG2021-04-222021-04-222021-021936878Xhttps://scholars.lib.ntu.edu.tw/handle/123456789/557652The aim of this study was to develop an artificial intelligence tool to assess echocardiographic image quality objectively.enartificial intelligence; automated strain analysis; cancer therapeuticsârelated cardiac dysfunction; left ventricular global longitudinal strain[SDGs]SDG3accuracy; 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, LeftArtificial Intelligence Aids Cardiac Image Quality Assessment for Improving Precision in Strain Measurementsjournal article10.1016/j.jcmg.2020.08.034332212132-s2.0-85097246328