Published Scientific Evidence
Category: (Atrial Fibrillation)
Anumana’s ECG-AI™ technology is supported by one of the most extensive evidence bases in cardiovascular AI. This library provides direct access to our peer-reviewed validation studies and broader body of clinical research.
September
27
2022
September 27, 2022
The Lancet
Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial
Background: Previous atrial fibrillation screening trials have highlighted the need for more targeted approaches. We did a pragmatic study to evaluate the effectiveness of an artificial intelligence (AI) algorithm-guided targeted…
June
08
2022
June 08, 2022
European Heart Journal
Migraine with aura associates with a higher artificial intelligence: ECG atrial fibrillation prediction model output compared to migraine without aura in both women and men
MwA is associated with an approximately twofold risk of ischemic stroke. Longitudinal cohort studies showed that patients with MwA have a higher incidence of developing AF compared to those with…
May
09
2022
May 09, 2022
European Heart Journal
Artificial intelligence-electrocardiography to detect atrial fibrillation: trend of probability before and after the first episode
Artificial intelligence (AI) enabled electrocardiography (ECG) can detect latent atrial fibrillation (AF) in patients with sinus rhythm (SR). However, the change of AI-ECG probability before and after the first AF…
April
30
2022
April 30, 2022
Mayo Clinic Proceedings
Artificial Intelligence-Enabled Electrocardiogram for Atrial Fibrillation Identifies Cognitive Decline Risk and Cerebral Infarcts
This study was designed to investigate whether artificial intelligence-enabled electrocardiograms (AI-ECG) assessment of Atrial Fibrillation (AF) risk predicts cognitive decline and cerebral infracts. Participants included patients who had a sinus-rhythm…
August
31
2021
August 31, 2021
American Heart Journal
Batch enrollment for an artificial intelligence-guided intervention to lower neurologic events in patients with undiagnosed atrial fibrillation: rationale and design of a digital clinical trial
BackgroundMost patients who have heart failure with a reduced ejection fraction, when left ventricular ejection fraction (LVEF) is 40% or lower, are diagnosed in hospital. This is despite previous presentations…
April
30
2021
April 30, 2021
Poster @ American College of Cardiology (ACC) 2021
Artificial Intelligence Helps Identify Patients With Graves’ Disease At Risk For Atrial Fibrillation
BackgroundGraves' disease (GD) is known to be associated with atrial fibrillation (AF). Artificial intelligence (AI)-enabled ECGs using a convolutional neural network can identify the signature of silent AF. Whether the…
March
10
2021
March 10, 2021
Poster Abstract @ International Stroke Conference 2021
Artificial Intelligence Enabled-Electrocardiography for the Detection of Cerebral Infarcts in Patients With Atrial Fibrillation
BackgroundAtrial fibrillation (AF) is an established risk factor for ischemic stroke but can be paroxysmal and go undiagnosed. An artificial intelligence (AI)-enabled ECG acquired during normal sinus rhythm was recently…
November
12
2020
November 12, 2020
Circulation: Arrhythmia and Electrophysiology
Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation
Background:An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI–enabled…
May
31
2020
May 31, 2020
Circulation Research
How Will Machine Learning Inform the Clinical Care of Atrial Fibrillation?
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 atrial fibrillation (AF),…
January
08
2020
January 08, 2020
HeartRhythm Case Reports
Recurrent cryptogenic stroke: A potential role for an artificial intelligence-enabled electrocardiogram?
Key Teaching PointsMany patients with cryptogenic stroke are suspected to have underlying paroxysmal atrial fibrillation (AF). However, in the absence of proven AF, anticoagulation of these patients has not been…