Summary Globally, up to 1· 5 million individuals with ischaemic stroke or transient ischaemic
attack can be newly diagnosed with atrial fibrillation per year. In the past decade, evidence …

Electrocardiography (ECG), improved by artificial intelligence (AI), has become a potential
technique for the precise diagnosis and treatment of cardiovascular disorders. The …

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 …

Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECG) has the
potential to transform diagnosis and estimate the prognosis of not only cardiac but …

Introduction Atrial fibrillation (AF) is the most common arrhythmia, contributing significantly to
morbidity and mortality. In a previous study, we developed a deep neural network for …

The long-term survivorship of patients diagnosed with cancer has improved due to
accelerated detection and rapidly evolving cancer treatment strategies. As such, the …

This study explores the practical applications of artificial intelligence (AI) in medical imaging,
focusing on machine learning classifiers and deep learning models. The aim is to improve …

Artificial Intelligence (AI) is redefining electrocardiogram (ECG) analysis in pre-participation
examination (PPE) of athletes, enhancing the detection and monitoring of cardiovascular …

Despite advances in ablative therapies, outcomes remain less favorable for persistent atrial
fibrillation often due to presence of non-pulmonary vein triggers and abnormal atrial …

Background: Outcomes of device-detected AF remain unclear in individuals without a prior
history of AF. Methods: A meta-analysis was conducted to evaluate outcomes in individuals …