Atrial fibrillation (AF) is a complex condition requiring holistic management with multiple
treatment decisions about optimal thromboprophylaxis, symptom control (and prevention of …

Machine learning (ML), a branch of artificial intelligence, where machines learn from big
data, is at the crest of a technological wave of change sweeping society. Cardiovascular …

The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI)
methodologies for decades. Recent renewed interest in deep learning techniques has …

Background: Current ablation therapy for atrial fibrillation is suboptimal, and long-term
response is challenging to predict. Clinical trials identify bedside properties that provide only …

Despite significant advances in its detection, understanding and management, atrial
fibrillation (AF) remains a highly prevalent cardiac arrhythmia with a major impact on …

We aim to provide a critical appraisal of basic concepts underlying signal recording and
processing technologies applied for (i) atrial fibrillation (AF) mapping to unravel AF …

The complexity of cardiac electrophysiology, involving dynamic changes in numerous
components across multiple spatial (from ion channel to organ) and temporal (from …

Medical digital twins, which are potentially vital for personalized medicine, have become a
recent focus in medical research. Here we present an overview of the state of the art in …

There has been an exponential growth of artificial intelligence (AI) and machine learning
(ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has …

Background: Machine learning is a promising approach to personalize atrial fibrillation
management strategies for patients after catheter ablation. Prior atrial fibrillation ablation …