Published Scientific Evidence
Category: (Low Ejection Fraction)
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.
January
16
2026
January 16, 2026
JACC
Multisite, External Validation of an AI-Enabled ECG Algorithm for Detection of Low Ejection Fraction
Abstract Background: Low left ventricular ejection fraction (LEF) can progress undiagnosed. Artificial intelligence–based electrocardiogram (ECG-AI) screening may provide a scalable means to detect LEF.Objectives: The purpose of this study was…
November
12
2025
November 12, 2025
JACC
Predicting Heart Failure From 12-Lead ECGs Using AI: A HeartShare/AMP-HF Pooled Cohort Analysis
Abstract Background: Artificial intelligence applied to electrocardiograms (ECG-AI) offers a scalable approach to identify individuals at risk for heart failure (HF) and guide preventive interventions. Objective: The purpose of this study was…
October
25
2024
October 25, 2024
Mayo Clinic Proceedings: Digital Health
Cost-Effectiveness of AI-Enabled Electrocardiograms for Early Detection of Low Ejection Fraction: A Secondary Analysis of the EAGLE Trial
Objective: To investigate the cost-effectiveness of using artificial intelligence (AI) to screen for low ejection fraction (EF) in routine clinical practice using a pragmatic randomized controlled trial (RCT). Participants &…
September
27
2024
September 27, 2024
ScienceDirect
Artificial Intelligence-Enhanced Electrocardiography Identifies Patients With Normal Ejection Fraction at Risk of Worse Outcomes
Abstract: Background An artificial intelligence (AI)-based electrocardiogram (ECG) model identifies patients with a higher likelihood of low ejection fraction (EF). Patients with an abnormal AI-ECG score but normal EF (false…
September
01
2024
September 01, 2024
Nature Medicine
Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial
Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and postpartum women to usual care or artificial intelligence (AI)-guided screening to assess…
January
06
2024
January 06, 2024
Nature Medicine
Artificial intelligence-enabled ECG for left ventricular diastolic function and filling pressure
Assessment of left ventricular diastolic function plays a major role in the diagnosis and prognosis of cardiac diseases, including heart failure with preserved ejection fraction. We aimed to develop an…
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
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…