Published Scientific Evidence
Category: (Other Programs)
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.
May
01
2021
May 01, 2021
Poster @ American College of Cardiology (ACC) 2021
AI Enhanced ECG Enabled Rapid Non-invasive Exclusion Of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) Infection
BackgroundRapid identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is critical to management of the pandemic. We sought to investigate the use of artificial intelligence applied to the…
April
23
2021
April 23, 2021
European Heart Journal
The 12-lead electrocardiogram as a biomarker of biological age
In a previous study, we demonstrated that a neural network is able to predict a person’s age from the electrocardiogram (ECG). However, some discrepancies were observed between ECG-derived and chronological…
February
01
2021
February 01, 2021
Nature Medicine
Artificial intelligence-enhanced electrocardiography in cardiovascular disease management
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 medicine. Although the…
February
01
2021
February 01, 2021
Nature Medicine
Artificial intelligence-enhanced electrocardiography in cardiovascular disease management
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 medicine. Although the…
November
25
2020
November 25, 2020
European Heart Journal
The association of artificial intelligence-enabled electrocardiogram-derived age (physiologic age) with atherosclerotic cardiovascular events in the community
This study demonstrates that artificial intelligence interpretation of ECGs (AI-ECG) can estimate an individual's physiologic age and that the gap between AI-ECG and chronologic age (Age-Gap) is associated with increased…
September
01
2020
September 01, 2020
Cardiovascular Digital Health Journal
A comprehensive artificial intelligence-enabled electrocardiogram interpretation program
Automated computerized electrocardiogram (ECG) interpretation algorithms are designed to enhance physician ECG interpretation, minimize medical error, and expedite clinical workflow. However, the performance of current computer algorithms is notoriously inconsistent.…
March
24
2020
March 24, 2020
Poster @ American College of Cardiology (ACC) 2020
Machine Learning Algorithms to Predict 10-Year Atherosclerotic Cardiovascular Risk in a Contemporary, Community-Based Historical Cohort
Background:The ACC/AHA Pooled Cohort Equation (PCE) for Atherosclerotic cardiovascular disease (ASCVD) has shown modest accuracy. We assessed if machine learning algorithms (MLA) could improve PCE performance with traditional and selected…
August
27
2019
August 27, 2019
American Heart Journal
Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs
Sex and age have long been known to affect the ECG. Several biologic variables and anatomic factors may contribute to sex and age-related differences on the ECG. We hypothesized that…