Abstract Machine learning (ML) is making a dramatic impact on cardiovascular magnetic
resonance (CMR) in many ways. This review seeks to highlight the major areas in CMR …

Cardiovascular disease remains an integral field on which new research in both the
biomedical and technological fields is based, as it remains the leading cause of mortality …

Aims We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex
echocardiographic data and clinical parameters could be used to phenogroup a heart failure …

The aim of the presented review is to summarize the literature data on the accuracy and
clinical applicability of artificial intelligence (AI) models as a valuable alternative to the …

We present a novel multimodal deep learning framework for cardiac resynchronisation
therapy (CRT) response prediction from 2D echocardiography and cardiac magnetic …

Advances in deep learning (DL) have resulted in impressive accuracy in some medical
image classification tasks, but often deep models lack interpretability. The ability of these …

Background In recent years, cardiac magnetic resonance imaging (CMR) has become ever
more important in the diagnosis and risk stratification of patients with cardiac disease. The …

Abstract Purpose of Review The aim of this structured review is to summarize the current
research applications and opportunities arising from artificial intelligence (AI) and texture …

Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide.
The accurate prediction of mortality and readmission risk provides crucial information for …

Objective: The aim of this paper is to describe an automated diagnostic pipeline that uses as
input only ultrasound (US) data, but is at the same time informed by a training database of …