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.
November
01
2020
{post_date: F d, Y}
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
{post_date: F d, Y}
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
31
2020
{post_date: F d, Y}
American Journal of Respiratory and Critical Care Medicine / American Thoracic Society (ATS) 2021 International Conference
An Automated Screening Algorithm Using Electrocardiograms for Pulmonary Hypertension
Pulmonary hypertension (PH) is a life-threatening disease that is typically detected after significantpulmonary vascular remodeling has occurred. Longer diagnostic delays are associated with higher mortality andthere is a need for…
October
31
2020
{post_date: F d, Y}
American Thoracic Society (ATS) 2021 International Conference
An Automated Screening Algorithm Using Electrocardiograms for Pulmonary Hypertension
Pulmonary hypertension (PH) is a life-threatening disease that is typically detected after significant pulmonary vascular remodeling has occurred. Longer diagnostic delays are associated with higher mortality and there is a…
October
16
2020
{post_date: F d, Y}
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…
September
01
2020
{post_date: F d, Y}
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.…
August
04
2020
{post_date: F d, Y}
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…
May
31
2020
{post_date: F d, Y}
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),…
March
24
2020
{post_date: F d, Y}
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…
February
29
2020
{post_date: F d, Y}
Poster @ American College of Cardiology (ACC) 2020
Detection Of Aortic Stenosis Using An Artificial Intelligence-Enabled Electrocardiogram
BackgroundPatients with moderate to severe aortic stenosis (AS) have increased mortality even when asymptomatic. We hypothesized, that artificial intelligence - (AI) enabled electrocardiogram (ECG) - an inexpensive, ubiquitous, 10 second…