Healthcare traditionally relies on single-modality approaches, which limit the information
available for medical decisions. However, advancements in technology and the availability …

Cardiac imaging plays an important role in the diagnosis of cardiovascular disease (CVD).
Until now, its role has been limited to visual and quantitative assessment of cardiac structure …

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 …

Artificial Intelligence (AI) performs human intelligence-dependant tasks using tools such as
Machine Learning, and its subtype Deep Learning. AI has incorporated itself in the field of …

In recent times, there have been notable changes in cardiovascular medicine, propelled by
the swift advancements in artificial intelligence (AI). The present work provides an overview …

Quantification of anatomical shape changes currently relies on scalar global indexes which
are largely insensitive to regional or asymmetric modifications. Accurate assessment of …

Machine learning (ML) methods are pervading an increasing number of fields of application
because of their capacity to effectively solve a wide variety of challenging problems. The …

Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR)
imaging enables the assessment of regional myocardial function and is important for …

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

Gaussian process dynamical systems (GPDSs) have shown their effectiveness in many
tasks of machine learning. However, when they address multi-view data, current GPDSs do …