The application of artificial intelligence (AI) to the electrocardiogram (ECG), a ubiquitous and
standardized test, is an example of the ongoing transformative effect of AI on cardiovascular …

MicroRNAs (miRNAs) are small regulatory molecules post-transcriptionally suppressing
mRNA activity. Many miRNAs in various organisms have been cloned but many unknown …

Technological innovations reach deeply into our daily lives and an emerging trend supports
the use of commercial smart wearable devices to manage health. In the era of remote …

This Perspective explores the application of machine learning toward improved diagnosis
and treatment. We outline a vision for how machine learning can transform three broad …

The purpose of this document is to update the “2014 AHA/ACC/HRS Guideline for the
Management of Patients With Atrial Fibrillation” S1. 3-1 (2014 AF Guideline) in areas for …

Cardiovascular diseases (CVDs) are the leading cause of death globally. An effective
strategy to mitigate the burden of CVDs has been to monitor patients' biomedical variables …

Background: Low detection and nonadherence are major problems in current management
approaches for patients with suspected atrial fibrillation (AF). Mobile health devices may …

Ambulatory monitoring is increasingly important for cardiovascular care but is often limited
by the unpredictability of cardiovascular events, the intermittent nature of ambulatory …

Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in
medicine, to assist in data classification, novel disease phenotyping and complex decision …

Apple launched a novel feature of the Apple Watch (Apple Inc.) series 4 that enables
consumers to record a rhythm strip and assist with self-diagnosis of atrial fibrillation (AF) …