Early identification of atrial fibrillation (AF) can reduce the risk of stroke, heart failure, and
other serious cardiovascular outcomes. However, paroxysmal AF may not be detected even …

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 …

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 …

Aims Atrial fibrillation (AF) is associated with significant morbidity but remains
underdiagnosed. A 24 h ambulatory electrocardiogram (ECG) is largely used as a tool to …

Background Paroxysmal atrial fibrillation (AF) often eludes early diagnosis, resulting in
significant morbidity and mortality. Artificial intelligence (AI) has been used to predict AF from …

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Implantable and
wearable cardiac devices have enabled the detection of asymptomatic AF episodes …

Background Atrial fibrillation is frequently asymptomatic and thus underdetected but is
associated with stroke, heart failure, and death. Existing screening methods require …

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

The most prevalent arrhythmia observed in clinical practice is atrial fibrillation (AF). AF is
associated with an irregular heartbeat pattern and a lack of a distinct P-waves signal. A low …

Background: Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising
interest to determine if AF severity or burden can further risk stratify these patients …