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.
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…
March
14
2023
March 14, 2023
Circulation
Abstract 42: Artificial Intelligence Electrocardiogram to Detect Coronary Calcification and to Predict Atherosclerotic Cardiovascular Events in the Community
Background: We developed a deep learning algorithm that detects coronary artery calcium (CAC) score using 12-lead electrocardiograms (CAC- ECG). We tested the hypothesis that the output from the CAC-ECG algorithm…
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…
November
14
2022
November 14, 2022
Nature Medicine
Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction
Although artificial intelligence (AI) algorithms have been shown to be capable of identifying cardiac dysfunction, defined as ejection fraction (EF) ≤ 40%, from 12-lead electrocardiograms (ECGs), identification of cardiac dysfunction…
November
13
2022
November 13, 2022
American Journal of Preventive Cardiology
Community-based participatory research application of an artificial intelligence-enhanced electrocardiogram for cardiovascular disease screening: A FAITH! Trial ancillary study
We conducted this study to evaluate the potential utility of an AI-based cardiovascular diseases (CVD) screening tool in an under-resourced African-American cohort, we reviewed the AI-enhanced electrocardiogram (ECG) data of…
November
01
2022
November 01, 2022
Mayo Clinic Proceedings
Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care.
In this study, we aimed to compare the clinicians’ characteristics of “high adopters” and “low adopters” of an artificial intelligence (AI)–enabled electrocardiogram (ECG) algorithm that alerted for possible low left…
October
21
2022
October 21, 2022
Cardiovascular Digital Health Journal
Tandem deep learning and logistic regression models to optimize hypertrophic cardiomyopathy detection in routine clinical practice
An electrocardiogram (ECG)-based artificial intelligence (AI) algorithm has shown good performance in detecting hypertrophic cardiomyopathy (HCM). However, its application in routine clinical practice may be challenging due to the low…
October
14
2022
October 14, 2022
JMIR AI
Provider Perspectives on Artificial Intelligence-Guided Screening for Low Ejection Fraction in Primary Care: Qualitative Study
In this study, we aimed to describe provider perspectives on the adoption of an AI-enabled screening tool in primary care to inform effective integration and sustained use. A qualitative study…
September
27
2022
September 27, 2022
The Lancet
Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial
Background: Previous atrial fibrillation screening trials have highlighted the need for more targeted approaches. We did a pragmatic study to evaluate the effectiveness of an artificial intelligence (AI) algorithm-guided targeted…
September
27
2022
September 27, 2022
Cardiovascular Digital Health Journal
Emerging role of artificial intelligence in cardiac electrophysiology
Key Findings: • Artificial Intelligence and machine learning have significantly impacted the field of cardiac electrophysiology. • Application of AI to EKG, data from wearables and smart devices can provide…