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.
April
30
2022
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Mayo Clinic Proceedings
Artificial Intelligence-Enabled Electrocardiogram for Atrial Fibrillation Identifies Cognitive Decline Risk and Cerebral Infarcts
This study was designed to investigate whether artificial intelligence-enabled electrocardiograms (AI-ECG) assessment of Atrial Fibrillation (AF) risk predicts cognitive decline and cerebral infracts. Participants included patients who had a sinus-rhythm…
February
28
2022
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Journal of the Americal College of Cardiology (JACC)
Assessment of Disease Status and Treatment Response With Artificial Intelligence−Enhanced Electrocardiography in Obstructive Hypertrophic Cardiomyopathy
Although hypertrophic cardiomyopathy (HCM) causes significant morbidity and is a leading cause of sudden death in adolescents, initial detection remains difficult. Echocardiography is an important diagnostic modality for HCM, but…
January
04
2022
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The Lancet Digital Health
Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study
Most patients who have heart failure with a reduced ejection fraction, when left ventricular ejection fraction (LVEF) is 40% or lower, are diagnosed in hospital. This is despite previous presentations…
December
28
2021
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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…
December
06
2021
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PLOS
Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients-The SaMi-Trop cohort
Left ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve symptoms and reduce mortality. Recently, an artificial intelligence (AI)-enabled ECG…
August
31
2021
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American Heart Journal
Batch enrollment for an artificial intelligence-guided intervention to lower neurologic events in patients with undiagnosed atrial fibrillation: rationale and design of a digital clinical trial
BackgroundMost patients who have heart failure with a reduced ejection fraction, when left ventricular ejection fraction (LVEF) is 40% or lower, are diagnosed in hospital. This is despite previous presentations…
August
01
2021
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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
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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…
July
02
2021
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Mayo Clinic Proceedings
Artificial Intelligence-Enhanced Electrocardiogram for the Early Detection of Cardiac Amyloidosis
The objective of this study was to develop an artificial intelligence (AI)–based tool to detect cardiac amyloidosis (CA) from a standard 12-lead electrocardiogram (ECG). 12-lead ECG data from 2541 patients…
June
18
2021
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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…