Published Scientific Evidence
Category: (Low Ejection Fraction)
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.
January
02
2021
January 02, 2021
International Journal of Cardiology
External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction
ObjectiveTo validate a novel artificial-intelligence electrocardiogram algorithm (AI-ECG) to detect left ventricular systolic dysfunction (LVSD) in an external population.BackgroundLVSD, even when asymptomatic, confers increased morbidity and mortality. We recently derived…
November
01
2020
November 01, 2020
International Journal of Cardiology
Left ventricular systolic dysfunction identification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients
An artificial intelligence-augmented electrocardiogram (AI-ECG) can identify left ventricular systolic dysfunction (LVSD). We examined the accuracy of AI ECG for identification of LVSD (defined as LVEF ≤40% by transthoracic echocardiogram…
October
31
2020
October 31, 2020
Mayo Clinic Proceedings
Artificial Intelligence ECG to Detect Left Ventricular Dysfunction in COVID-19: A Case Series
COVID-19 can result in deterioration of cardiac function, which is associated with high mortality. A simple point-of-care diagnostic test to screen for ventricular dysfunction would be clinically useful to guide…
October
16
2020
October 16, 2020
European Heart Journal - Acute Cardiovascular Care
Mortality risk stratification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients
An artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm can identify left ventricular systolic dysfunction (LVSD). We sought to determine whether this AI-ECG algorithm could stratify mortality risk in cardiac intensive care unit…
August
04
2020
August 04, 2020
Circulation: Arrhythmia and Electrophysiology
Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea
Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and…
February
29
2020
February 29, 2020
Poster @ American College of Cardiology (ACC) 2020
Trastuzumab Cartiotoxicity Surveillance by Artificial Intelligence-Augmented Electrocardiography in a Multi Site Study
BackgroundTrastuzumab carries a black box warning for cardiotoxicity, and HER-2 positive breast cancer patients who are treated with trastuzumab are recommended to have an echocardiogram (TTE) every three month while…
February
16
2020
February 16, 2020
Circulation: Arrhythmia and Electrophysiology
Assessing and Mitigating Bias in Medical Artificial Intelligence – The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis
Deep learning algorithms derived in homogeneous populations may be poorly generalizable and have the potential to reflect, perpetuate, and even exacerbate racial/ethnic disparities in health and health care. In this…
January
31
2020
January 31, 2020
American Heart Journal
Clinical trial design data for electrocardiogram artificial intelligence-guided screening for low ejection fraction (EAGLE)
The article details the materials that will be used in a clinical trial - ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized…
November
11
2019
November 11, 2019
Abstract @ American Heart Association (AHA) 2019
Prospective Analysis of Utility of Signals From an Ecg-Enabled Stethoscope to Automatically Detect a Low Ejection Fraction Using Neural Network Techniques Trained From the Standard 12-Lead Ecg
BackgroundECG-enabled stethoscopes (ECG-steth) can acquire single lead ECGs during cardiac auscultation, and may facilitate real-time screening for pathologies not routinely identified during physical examination (eg, arrhythmias). We previously demonstrated an…
October
25
2019
October 25, 2019
American Heart Journal
ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial
BackgroundA deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead electrocardiogram (ECG) has recently been developed and validated. The algorithm was incorporated into the electronic health record…