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.
October
14
2024
October 14, 2024
European Heart Journal
Artificial Intelligence Evaluation of Electrocardiographic Characteristics and Interval Changes in Transgender Patients on Gender-Affirming Hormone Therapy
Background: Gender-affirming hormone therapy (GAHT) is used by some transgender individuals (TG), who comprise 1.4% of US population. However, the effects of GAHT on ECG remain unknown. Objective To assess…
June
12
2023
June 12, 2023
European Journal of Neurology
Machine-learning-derived heart and brain age are independently associated with cognition
Background and purpose: A heart age biomarker has been developed using deep neural networks applied to electrocardiograms. Whether this biomarker is associated with cognitive function was investigated. Methods: Using 12-lead…
February
13
2023
February 13, 2023
Transplantation
Physiological Age by Artificial Intelligence-Enhanced Electrocardiograms as a Novel Risk Factor of Mortality in Kidney Transplant Candidates
Mortality risk assessment before kidney transplantation (KT) is imperfect. An emerging risk factor for death in nontransplant populations is physiological age as determined by the application of artificial intelligence to…
September
01
2022
September 01, 2022
Current Cardiology Reports
Artificial Intelligence Applied to Cardiomyopathies: Is It Time for Clinical Application?
Many studies have been undertaken in recent years on cardiomyopathy screening especially using AI-enhanced electrocardiography (ECG). While the implementation of AI in the diagnosis and treatment of cardiomyopathies is still…
August
29
2022
August 29, 2022
Nature
Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes
Artificial Intelligence (AI) based approaches can now use electrocardiograms (ECGs) to provide expert-level performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models…
December
28
2021
December 28, 2021
The American Journal of Gastroenterology
Development of the AI-Cirrhosis-ECG (ACE) Score: An electrocardiogram-based deep learning model in cirrhosis
Cirrhosis is associated with cardiac dysfunction and distinct ECG abnormalities. This study aimed to develop a proof-of-concept deep learning-based artificial intelligence (AI) model that could detect cirrhosis-related signals on ECG…
August
01
2021
August 01, 2021
Mayo Clinic Proceedings
Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram
ObjectiveTo rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG).MethodsA global, volunteer consortium from 4 continents identified patients with ECGs obtained…
July
24
2021
July 24, 2021
The American Journal of Cardiology
Artificial Intelligence-Enabled Electrocardiography to Screen Patients with Dilated Cardiomyopathy
Undiagnosed dilated cardiomyopathy (DC) can be asymptomatic or present as sudden cardiac death, therefore pre-emptively identifying and treating patients may be beneficial. Screening for DC with echocardiography is expensive and…
June
18
2021
June 18, 2021
European Heart Journal
Application of artificial intelligence to the electrocardiogram
Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the…
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…