Artificial Intelligence-Enhanced Echocardiography for Systolic Function Assessment

Abstract

The accurate assessment of left ventricular systolic function is crucial in the diagnosis and treatment of cardiovascular diseases. Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are the most critical indexes of cardiac systolic function. Echocardiography has become the mainstay of cardiac imaging for measuring LVEF and GLS because it is non-invasive, radiation-free, and allows for bedside operation and real-time processing. However, the human assessment of cardiac function depends on the sonographer's experience, and despite their years of training, inter-observer variability exists. In addition, GLS requires post-processing, which is time consuming and shows variability across different devices. Researchers have turned to artificial intelligence (AI) to address these challenges. The powerful learning capabilities of AI enable feature extraction, which helps to achieve accurate identification of cardiac structures and reliable estimation of the ventricular volume and myocardial motion. Hence, the automatic output of systolic function indexes can be achieved based on echocardiographic images. This review attempts to thoroughly explain the latest progress of AI in assessing left ventricular systolic function and differential diagnosis of heart diseases by echocardiography and discusses the challenges and promises of this new field.

Keywords: artificial intelligence; deep learning; echocardiography; left ventricular systolic function; machine learning.

Figure 1

Logical diagram of AI, ML, and DL and the main characteristics of ML and DL. (AI: artificial intelligence; ML: machine learning; DL: deep learning).

Figure 2

The workflow of studies on AI in echocardiography, including four main steps: (1) clinical problem-oriented data collection; (2) data preprocessing operations based on task characteristics (classification tasks require explicit sample labels; segmentation tasks require the marking of regions of interest) and data splitting (training, validation, and testing datasets are mutually independent); (3) based on the type of tasks (regression, classification, or clustering), appropriate AI algorithms are selected for model development on the training datasets, and the performance of the model is validated on the validation datasets; (4) the reliability and generalization of the model are tested on the internal and external independent testing datasets.

Figure 3

Overview diagram of AI’s application in echocardiography. Current applications focus on view classification and image quality control (classification), cardiac phase detection and cardiac function assessment (regression), and disease diagnosis and prognosis analysis (clustering). Mainstream AI algorithms consist of the convolutional neural network (CNN), recurrent neural network (RNN), transformer, and traditional machine learning algorithms (RF and SVM).