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.
October
02
2023
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JACC
Patient-Level Artificial Intelligence-Enhanced Electrocardiography in Hypertrophic Cardiomyopathy: Longitudinal Treatment and Clinical Biomarker Correlations
Background: Artificial intelligence (AI) applied to 12-lead electrocardiographs (ECGs) can detect hypertrophic cardiomyopathy (HCM). Objectives The purpose of this study was to determine if AI-enhanced ECG (AI-ECG) can track longitudinal…
June
12
2023
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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
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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
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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…