Automated Assessment of Transthoracic Echocardiogram Image Quality Using Deep Neural Networks

TL;DR

This paper presents a deep learning-based system for objective, automated assessment of transthoracic echocardiogram image quality, improving consistency and aiding clinical decision-making.

Contribution

It introduces a novel set of domain-specific quality indicators and deep neural networks trained on a large dataset for automated image quality evaluation.

Findings

High accuracy in quality indicator prediction

Effective extraction of anatomical and pathological features

Potential for real-time clinical application

Abstract

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.