Echocardiographic Image Quality Assessment Using Deep Neural Networks

TL;DR

This paper develops a deep neural network to objectively assess echocardiographic image quality, addressing the subjectivity and variability in traditional visual assessments crucial for accurate cardiac diagnosis.

Contribution

It introduces a fully trained convolutional neural network model that quantifies echocardiographic image quality attributes based on expert-defined features.

Findings

Neural network accurately predicts image quality attributes.

Automates quality assessment, reducing observer variability.

Potential to improve diagnostic reliability in echocardiography.

Abstract

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.