The Role of Artificial Intelligence in Echocardiography

Abstract

Echocardiography is an integral part of the diagnosis and management of cardiovascular disease. The use and application of artificial intelligence (AI) is a rapidly expanding field in medicine to improve consistency and reduce interobserver variability. AI can be successfully applied to echocardiography in addressing variance during image acquisition and interpretation. Furthermore, AI and machine learning can aid in the diagnosis and management of cardiovascular disease. In the realm of echocardiography, accurate interpretation is largely dependent on the subjective knowledge of the operator. Echocardiography is burdened by the high dependence on the level of experience of the operator, to a greater extent than other imaging modalities like computed tomography, nuclear imaging, and magnetic resonance imaging. AI technologies offer new opportunities for echocardiography to produce accurate, automated, and more consistent interpretations. This review discusses machine learning as a subfield within AI in relation to image interpretation and how machine learning can improve the diagnostic performance of echocardiography. This review also explores the published literature outlining the value of AI and its potential to improve patient care.

Keywords: artificial intelligence; echocardiography; machine learning.

Figure 1

Different applications of artificial intelligence (AI) in echocardiography. Panel (A). One of the main advantages of using echocardiography in machine learning models is that these algorithms can combine data derived from echocardiography with clinical information and/or other test results to develop predictive tools with high accuracy to enhance diagnosis, risk stratification, and therapeutic strategies. Panel (B). Artificial intelligence can use raw echocardiography images/videos to automatically provide structural or functional measurements but also to identify disease states. This ability is based on AI’s capacity to automatically analyze features from images that may be too subtle to be detected by the human eye. Following training, the machine learning algorithm should be able to recognize cardiac structural and functional patterns or specific diseases. (ROC) curves are usually used to show how well the risk prediction models discriminate between patients with and without a condition.

Figure 2

Potential clinical applications of artificial intelligence in echocardiography to identify disease states.