Machine learning applications in cardiology have rapidly evolved in the past decade. With
the availability of machine learning tools coupled with vast data sources, the management of …

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 In the context of atrial fibrillation (AF), traditional clinical practices have thus
fallen short in several domains, such as identifying patients at risk of incident AF or patients …

(1) Background: Atrial fibrillation (AF) is a major risk factor for stroke and is often
underdiagnosed, despite being present in 13–26% of ischemic stroke patients. Recently, a …

Background: Artificial intelligence (AI)–enabled analysis of 12-lead ECGs may facilitate
efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether …

Importance Early detection of atrial fibrillation (AF) may help prevent adverse cardiovascular
events such as stroke. Deep learning applied to electrocardiograms (ECGs) has been …

Abstract Machine learning has immense novel but also disruptive potential for medicine.
Numerous applications have already been suggested and evaluated concerning …

Background: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it
goes undetected. If new-onset AF could be predicted, targeted screening could be used to …

Aims Recent trial data demonstrate beneficial effects of active rhythm management in
patients with atrial fibrillation (AF) and support the concept that a low arrhythmia burden is …

The management of atrial fibrillation (AF) has seen marked changes in past years, with the
introduction of new oral anticoagulants, new antiarrhythmic drugs, and the emergence of …