  Standard views in two-dimensional echocardiography are well established but
the quality of acquired images are highly dependent on operator skills and are
assessed subjectively. This study is aimed at providing an objective assessment
pipeline for echocardiogram image quality by defining a new set of
domain-specific quality indicators. Consequently, image quality assessment can
thus be automated to enhance clinical measurements, interpretation, and
real-time optimization. We have developed deep neural networks for the
automated assessment of echocardiographic frame which were randomly sampled
from 11,262 adult patients. The private echocardiography dataset consists of
33,784 frames, previously acquired between 2010 and 2020. Deep learning
approaches were used to extract the spatiotemporal features and the image
quality indicators were evaluated against the mean absolute error. Our quality
indicators encapsulate both anatomical and pathological elements to provide
multivariate assessment scores for anatomical visibility, clarity, depth-gain
and foreshortedness, respectively.
