Willem is a powerful and scalable AI platform for cardiology

How Willem works

01
An electrocardiogram (ECG) from any medical device hardware is recorded.
02
The ECG signal is analysed by AI algorithms in a secure cloud to triage or output a list of probable arrhythmias, risk scores and alerts.
03
The results are delivered to the clinician, via email or integration with Electronic Medical Record (EMR) or cardiology information system.

Our platform consists of 6 capabilities

State-of-the-art algorithms

Algorithms identify over 80 cardiac patterns automatically at cardiologist-level accuracy in a multi-class scenario.

Integrable with any EMR system

Our software is made available to customers via API and non-API tools, to ensure seamless bi-directional data exchange.

Deep science and clinical expertise in cardiology

The Idoven team has over 10 years building data pipelines in the field of cardiology that have led to state-of-the-art scientific publications, with over 30 published in high-impact factor journals.

Device neutral

Algorithms analyse ECG data from any-lead wearable, mobile and implantable device.

GDPR and ISO 27001 compliant for data privacy and security

Secure cloud infrastructure is compliant with internationally accepted standards to safeguard patient data.

Prediction capabilities

The Idoven team develops predictive ECG-based AI models as prognostic tools. Our first model identifies patients that are at increased risk of developing persistent atrial fibrillation within the next 6 months, with a paper published in Europace, one of the highest-impact factor journals in the arrhythmia field.

WillemTM is your Digital Cardiologist in the Cloud, in honour of the father of the electrocardiograph and Noble Prize winner, Willem Einthoven

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First-in-class collaborations with top research institutions and KOLs

Idoven is a member of the pan-European MAESTRIA (Machine Learning Artificial Intelligence Early Detection Stroke) consortium to build the largest and most diverse database ever built on atrial fibrillation, with the goal of creating multi-parametric digital tools based on a new generation of biomarkers integrating artificial intelligence and big data from imaging, electrocardiography and omics technologies.

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