Published Scientific Evidence
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.
February
29
2020
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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
25
2020
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Journal of the Americal College of Cardiology (JACC)
Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram
Hypertrophic cardiomyopathy (HCM) is an uncommon but important cause of sudden cardiac death. This study sought to develop an artificial intelligence approach for the detection of HCM based on 12-lead…
February
16
2020
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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
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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…
January
08
2020
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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…
November
11
2019
{post_date: F d, Y}
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
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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…
August
27
2019
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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…
August
01
2019
{post_date: F d, Y}
The Lancet
An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction
BackgroundAtrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low…
April
03
2019
{post_date: F d, Y}
JAMA Cardiology
Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram
For patients with chronic kidney disease (CKD), hyperkalemia is common, associated with fatal arrhythmias, and often asymptomatic, while guideline-directed monitoring of serum potassium is underused. A deep-learning model that enables…