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WILLEM: AI to Reduce Cardiovascular Diseases | WILLEM: AI to Reduce Cardiovascular Diseases

Summary
WILLEM is the first 100% automated cloud platform for electrocardiogram (ECG) analysis, designed to comprehensively identify and diagnose all types of arrhythmias and predict Cardiovascular Diseases (CVDs) behaviour at 6 months since its detection. WILLEM communicates users and
hospitals-in real time through a unique Cloud Platform, integrable with any other eHealth platform as part of the clinical workflow. WILLEM provides the best prospective and labelled ECG database and, as a hardware-agnostic platform, it uses breakthrough Artificial Intelligence (AI) models
to transform raw ECG signals from any monitoring device into a medical grade ECG report. Today, WILLEM’s AI classifies 73 arrhythmias of the 288 known cardiac patterns, more than 90% of the cases, and it is the only solution that predicts Atrial Fibrillation. The goal is to classify every
arrythmia present in human biology and to predict the 6 most prevalent heart diseases in an automatic and non-supervised way to reduce CVDs.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/190173745
Start date: 01-11-2022
End date: 31-10-2024
Total budget - Public funding: 3 596 296,00 Euro - 2 500 000,00 Euro
Cordis data

Original description

WILLEM is the first 100% automated cloud platform for electrocardiogram (ECG) analysis, designed to comprehensively identify and diagnose all types of arrhythmias and predict Cardiovascular Diseases (CVDs) behaviour at 6 months since its detection. WILLEM communicates users and
hospitals-in real time through a unique Cloud Platform, integrable with any other eHealth platform as part of the clinical workflow. WILLEM provides the best prospective and labelled ECG database and, as a hardware-agnostic platform, it uses breakthrough Artificial Intelligence (AI) models
to transform raw ECG signals from any monitoring device into a medical grade ECG report. Today, WILLEM’s AI classifies 73 arrhythmias of the 288 known cardiac patterns, more than 90% of the cases, and it is the only solution that predicts Atrial Fibrillation. The goal is to classify every
arrythmia present in human biology and to predict the 6 most prevalent heart diseases in an automatic and non-supervised way to reduce CVDs.

Status

SIGNED

Call topic

HORIZON-EIC-2021-ACCELERATORCHALLENGES-01-01

Update Date

09-02-2023
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