Objective Advanced machine learning technology provides an opportunity to improve
clinical electrocardiogram (ECG) interpretation, allowing non‐cardiology clinicians to initiate …

Objective An artificial intelligence (AI) algorithm has been developed to detect the
electrocardiographic signature of atrial fibrillation (AF) present on an electrocardiogram …

Background Emergency Room to Electrophysiology (ER2EP) Atrial Fibrillation (AF) referrals
may result in shorter times to ablation and optimal medical therapy. An effective way to …

AF is the most common clinically relevant cardiac arrhythmia associated with multiple
comorbidities, cardiovascular complications (eg stroke) and increased mortality. As artificial …

Background Atrial fibrillation (AF) increases risk of embolic stroke, and in postoperative
patients, increases cost of care. Consequently, ECG screening for AF in high‐risk patients is …

Importance Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, and its
early detection could lead to significant improvements in outcomes through the appropriate …

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 …

Atrial fibrillation (AF) affects more than 30 million individuals worldwide, making it the most
prevalent cardiac arrhythmia on a global scale. This systematic review summarizes recent …

Background Several smart devices are able to detect atrial fibrillation automatically by
recording a single-lead electrocardiogram, and have created a work overload at the hospital …

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 …