AI-echocardiography: Current status and future direction.

Abstract

Echocardiography, which provides detailed evaluations of cardiac structure and pathology, is central to cardiac imaging. Traditionally, the assessment of disease severity, treatment effectiveness, and prognosis prediction relied on detailed parameters obtained by trained sonographers and the expertise of specialists, which can limit access and availability. Recent advancements in deep learning and large-scale computing have enabled the automatic acquisition of parameters in a short time using vast amounts of historical training data. These technologies have been shown to predict the presence of diseases and future cardiovascular events with or without relying on quantitative parameters. Additionally, with the advent of large-scale language models, zero-shot prediction that does not require human labeling and automatic echocardiography report generation are also expected. The field of AI-enhanced echocardiography is poised for further development, with the potential for more widespread use in routine clinical practice. This review discusses the capabilities of deep learning models developed using echocardiography, their limitations, current applications, and research utilizing generative artificial intelligence technologies.