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Review
. 2018 Dec 1;5(4):R115-R125.
doi: 10.1530/ERP-18-0056.

Artificial intelligence and echocardiography

M Alsharqi  1 , W J Woodward  1 , J A Mumith  2 , D C Markham  2 , R Upton  2 , P Leeson  1
Affiliations
Review

Artificial intelligence and echocardiography

M Alsharqi et al. Echo Res Pract. .

Abstract

Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experience can lead to incorrect diagnoses. Artificial intelligence (AI) technologies provide new possibilities for echocardiography to generate accurate, consistent and automated interpretation of echocardiograms, thus potentially reducing the risk of human error. In this review, we discuss a subfield of AI relevant to image interpretation, called machine learning, and its potential to enhance the diagnostic performance of echocardiography. We discuss recent applications of these methods and future directions for AI-assisted interpretation of echocardiograms. The research suggests it is feasible to apply machine learning models to provide rapid, highly accurate and consistent assessment of echocardiograms, comparable to clinicians. These algorithms are capable of accurately quantifying a wide range of features, such as the severity of valvular heart disease or the ischaemic burden in patients with coronary artery disease. However, the applications and their use are still in their infancy within the field of echocardiography. Research to refine methods and validate their use for automation, quantification and diagnosis are in progress. Widespread adoption of robust AI tools in clinical echocardiography practice should follow and have the potential to deliver significant benefits for patient outcome.

Keywords: artificial intelligence; echocardiography; machine learning.

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Figures

Figure 1
Figure 1
Types of machine learning algorithms.
Figure 2
Figure 2
Advantages of machine learning assisted echocardiography interpretation.
Figure 3
Figure 3
An example of a Convolutional Neural Network model for image classification. A2C, apical two chamber; A3C, apical three chamber; A4C, apical four chamber; PLAX, parasternal long axis; PSAX, parasternal short axis.
Figure 4
Figure 4
Diagram of an example of machine learning model process.
See this image and copyright information in PMC

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