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.
May
16
2021
May 16, 2021
Journal of the American College of Cardiology
Understanding Spectrum Bias In Algorithms Derived By Artificial Intelligence A Case Study In Detecting Aortic Stenosis Using Electrocardiograms
BackgroundThere are an increasing number of diagnostic tests derived from artificial intelligence (AI) and machine learning algorithms. Spectrum bias can arise when a diagnostic test is derived from study populations…
May
05
2021
May 05, 2021
Nature Medicine
Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial
We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a…
May
04
2021
May 04, 2021
Cardiovascular Digital Health Journal
An artificial intelligence-enabled ECG algorithm for comprehensive ECG interpretation: Can it pass the Turing test?
The objective of this study is to build infrastructure for digital trials to improve efficiency and generalizability and test it using a study to validate an artificial intelligence algorithm to…
May
03
2021
May 03, 2021
Poster @ American College of Cardiology (ACC) 2021
Deep Learning Enabled Electrocardiographic Prediction of Computer Tomography-Based High Coronary Calcium Score (CAC)
BackgroundCoronary artery calcium (CAC) scoring is recommended in adults with unclear cardiovascular (CV) risk to inform preventative strategies and has major limitations. We developed a deep learning (DL) algorithm to…
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
30
2021
April 30, 2021
Journal of the Americal College of Cardiology (JACC) / ACC.21
Artificial-Intelligence Enhanced Screening For Cardiac Amyloidosis By Electrocardiography
Cardiac amyloidosis (CA) is a life-threatening disease with poor outcomes often related to delayed diagnosis. We developed an artificial-intelligence (AI) based screening tool that identifies CA from the 12 lead…
April
30
2021
April 30, 2021
Poster @ Americal College of Cardiology (ACC) 2021
Detection Of Hypertrophic Cardiomyopathy By Artificial Intelligence-Enabled Electrocardiography In Children And Adolescents
BackgroundHypertrophic cardiomyopathy (HCM) is a cause of morbidity and sudden cardiac death in children and adolescents. There is currently no established screening approach for HCM. We recently developed an artificial…
April
30
2021
April 30, 2021
Poster @ American College of Cardiology (ACC) 2021
Validation Of An Artificial Intelligence Electrocardiogram Based Algorithm For The Detection Of Left Ventricular Systolic Dysfunction In Subjects With Chagas Disease
BackgroundChagas cardiomyopathy is a frequent and severe manifestation of Chagas disease (CD) and it is a leading cause of morbidity and death in South America. The dilated cardiomyopathy in CD…
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…
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…