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.

Cardiac Allograft Vasculopathy: Challenges and Advances in Invasive and Non-Invasive Diagnostic Modalities

“…The coronary artery calcium (CAC) score using CT is a well-established marker of coronary atherosclerosis, with a negative CAC score correlating with good clinical outcomes [ 34 ]. CAC estimates atherosclerotic plaque burden by assessing the calcified portion of coronary plaque; however, it may not detect soft plaque or intimal thickening without calcification.…”

“…CAC estimates atherosclerotic plaque burden by assessing the calcified portion of coronary plaque; however, it may not detect soft plaque or intimal thickening without calcification. Given the complex pathophysiology of CAV, it is unclear whether CAC score can predict CAV and long-term outcomes [ 34 , 35 ]. It was previously believed that CAC offered no prognostic value because calcification was absent even in severe disease and earlier studies failed to demonstrate any predictive value for CAC in CAV patients [ 36 , 37 , 38 ].…”

Artificial Intelligence in Cardiology: Why So Many Great Promises and Expectations, but Still a Limited Clinical Impact?

“…In the echocardiographic field, AI may improve imaging quality, guiding scanning, and assisting in segmentation, processing, and analysis [ 1 , 2 , 3 , 4 , 5 ]. AI can help in view interpretation and classification, in the quantification of both cardiovascular structure and function, and in detecting wall motion abnormalities [ 1 , 2 , 3 , 4 , 5 ].…”

“…In the echocardiographic field, AI may improve imaging quality, guiding scanning, and assisting in segmentation, processing, and analysis [ 1 , 2 , 3 , 4 , 5 ]. AI can help in view interpretation and classification, in the quantification of both cardiovascular structure and function, and in detecting wall motion abnormalities [ 1 , 2 , 3 , 4 , 5 ]. AI can also help differentiating physiological hypertrophy in athletes from hypertrophic cardiomyopathy, and in the identification and assessment of amyloidosis, pulmonary artery hypertension, and valvular heart disease, as mitral regurgitation and aortic stenosis [ 1 , 2 , 3 , 4 , 5 ].…”

Quantum Machine Learning and TensorNetworks as an Aid in the Clinical Diagnosis ofCoronary Artery Disease

“…There have been studies applying novel variants of Deep Learning to these cardiology problems [5][6][7][8][9][10], but there is still room to grow, in particular using advanced techniques that merge contributions from both classical and quantum machine learning algorithms on a set of clinical data providing a robust approach for patient assessment. Hybrid models, pairing classical and quantum Machine Learning, have been recently tested in several applications in cardiology with promising results [11,12].…”