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WILLEM AI-based ECG Platform for High-risk Cardiac Patients

This study is observing how well the WILLEM AI platform can identify heart patterns, irregular heartbeats (arrhythmias), or heart diseases from electrocardiograms (ECGs). ECGs are simple tests that help doctors detect heart problems early. The WILLEM AI platform automatically interprets these ECGs, which could make diagnosis faster and easier. Researchers want to see how well WILLEM performs in real-world situations. You might be able to join if you are over 18 and have had a standard ECG. The study aims to enroll 200,000 people. The study status is currently unclear.

Study design
This is a large, observational study involving 200,000 participants. It is a single-group registry study, meaning all participants will be assessed using the WILLEM AI platform.
What's involved
Not specified in the trial record.
Compensation
Not stated in the trial record.
Follow-up
Your ECGs will be assessed from enrollment up to 10 years after your ECG was taken, depending on whether you joined retrospectively or prospectively.

AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.

NCT07333547

Registry Study for the Evaluation of High-risk Cardiac Patients by WILLEM AI-based ECG Platform

Recruiting
Not specifiedAges 18+Observational
Idoven 1903 S.L.
~200,000 participants
Updated 2026-07-20 on ClinicalTrials.gov
What's tested:Willem AI ECG assessment

At a glance

Recruiting sites
3 of 5 listed sites are recruiting right now
RecruitingSuspended, closed, or not yet open
What they're measuring
Primary endpoint analysis: Willem performance
Measured over From enrollment to any standard of care timepoint when the patient underwent (retrospective) or will undergo within the next 10 years (prospective) an eligible electrocardiogram

Conditions

High-risk Cardiac Patients

Explore related trials

Heart Disorder

Where it's being run

5 sites across 3 states
Spain3
Tennessee1
Guayas1

Who to contact

Manuel Marina-Breysse, MD, PhD
[email protected]
+34669752391
Email the study team

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Do you actually qualify for this trial?

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Eligibility criteria

Inclusion

EC/IRB approval of ICF waiver prior to recruitment; otherwise, signed informed consent form by subject and investigator
Age \> 18 years-old, with no upper limit
Subjects undergoing standard of care electrocardiogram (ECG) of any duration from any hardware device
All available, but at least one, legible ECG tracings in raw data format (e.g. DICOM, XML, EDF, JSON, HL7, SCP, WFDB, CSV, etc.)
Available subject clinical data associated with the ECG
For 12-lead ECGs, a minimum length of 10 seconds at a minimum sample frequency of 250 Hz
For ECGs from Holters, wearables, patches, insertable cardiac monitors, telemetries, etc., a minimum length of 30 seconds at a minimum sample frequency of 200 Hz with a lead I / II or its MCL-DII lead approximation
For prospective eligibility only:
Signed informed consent form, unless previously waived by the EC/IRB
Site technical viability for ECG and subject clinical data transfer (e.g. end-to-end integration following interoperability standards such as FHIR, HL7 or DICOM)

Exclusion

Unavailable or suboptimal quality of the raw data from the ECG signal
Age \< 18 years-old

What this trial measures

  • Primary endpoint analysis: Willem performanceFrom enrollment to any standard of care timepoint when the patient underwent (retrospective) or will undergo within the next 10 years (prospective) an eligible electrocardiogram

    ECG data will be categorized according to SOC-defined cardiopathies, arrhythmic events, and cardiac diseases. If SOC diagnosis is unavailable or inconsistent, an independent committee of expert cardiologists will review and provide their diagnosis according to a cardiac defined ontology which extends values defined in HL7-aECG data store. Then, the performance of Willem to detect cardiac patterns, arrhythmias, and cardiac disease from ECGs will be assessed. In order to define True Positive, True Negative, False Positive, and False Negative classifications, the ground truth for comparison will be Standard Of Care (SOC) manually performed cardiologist diagnosis. Performance metrics such as diagnostic accuracy, sensitivity, specificity, predictive positive value (PPV), negative predictive value (NPV), F1-Score and Area Under the Receiver Operating Characteristic Curve (AUROC) will be obtained.