  Echocardiography image quality assessment is not a trivial issue in
transthoracic examination. As the in vivo examination of heart structures
gained prominence in cardiac diagnosis, it has been affirmed that accurate
diagnosis of the left ventricle functions is hugely dependent on the quality of
echo images. Up till now, visual assessment of echo images is highly subjective
and requires specific definition under clinical pathologies. While poor-quality
images impair quantifications and diagnosis, the inherent variations in
echocardiographic image quality standards indicates the complexity faced among
different observers and provides apparent evidence for incoherent assessment
under clinical trials, especially with less experienced cardiologists. In this
research, our aim was to analyse and define specific quality attributes mostly
discussed by experts and present a fully trained convolutional neural network
model for assessing such quality features objectively.
