Ignite Creation Date:
2026-03-26 @ 3:15 PM
Ignite Modification Date:
2026-03-30 @ 3:06 AM
Study NCT ID:
NCT07479992
Status:
NOT_YET_RECRUITING
Last Update Posted:
2026-03-20
First Post:
2026-03-13
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Multicenter Study for the Validation of Willem AI: Aortic StenoSis Early Diagnosis With AI-electrocardiogram Study
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D001024', 'term': 'Aortic Valve Stenosis'}, {'id': 'D006331', 'term': 'Heart Diseases'}], 'ancestors': [{'id': 'D000082862', 'term': 'Aortic Valve Disease'}, {'id': 'D006349', 'term': 'Heart Valve Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D014694', 'term': 'Ventricular Outflow Obstruction'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'OTHER', 'observationalModel': 'CASE_CONTROL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 5000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-03', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-03', 'completionDateStruct': {'date': '2026-06', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-03-18', 'studyFirstSubmitDate': '2026-03-13', 'studyFirstSubmitQcDate': '2026-03-13', 'lastUpdatePostDateStruct': {'date': '2026-03-20', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-03-18', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-06', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Willem performance to detect severe Aortic Stenosis', 'timeFrame': 'ECG will be performed at baseline, and ECG analysis by Willem AI will be performed retrospectively throughout the trial, and finalized upon recruitment completion.', 'description': 'Performance of Willem AI platform to distinguish between severe Aortic Stenosis (AS) patients with confirmed diagnosis and non-AS patients, by means of the following performance metrics: Area Under the Receiver Operating Characteristic curve (AUROC), diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).\n\nThe Standard Of Care (SOC) investigator diagnosis will be used as the ground truth.'}], 'secondaryOutcomes': [{'measure': 'Willem performance to detect moderate Aortic Stenosis', 'timeFrame': 'ECG will be performed at baseline, and ECG analysis by Willem AI will be performed retrospectively throughout the trial, and finalized upon recruitment completion.', 'description': 'Performance of Willem AI platform to distinguish between moderate Aortic Stenosis (AS) patients with confirmed diagnosis and non-AS patients, by means of the following performance metrics: Area Under the Receiver Operating Characteristic curve (AUROC), diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The Standard Of Care (SOC) investigator diagnosis will be used as the ground truth.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isUnapprovedDevice': True, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['artificial intelligence', 'electrocardiogram', 'deep learning', 'cardiac disease', 'aortic stenosis'], 'conditions': ['Aortic Stenosis']}, 'descriptionModule': {'briefSummary': 'AoS-SEDAI study is an observational, multicenter, retrospective and prospective clinical study.\n\nThis study aims to assess Willem Artificial Intelligence (AI) ability to distinguish between aortic stenosis (AS) and non-AS patients from 12-lead electrocardiogram (ECG) data.', 'detailedDescription': 'Aortic Stenosis (AS) is a common and progressive valvular heart disease, especially in older adults, yet significantly underdiagnosed. Many individuals with significant AS may remain asymptomatic for extended periods or experience vague symptoms, delaying diagnosis for several years. In some cases, sudden cardiac events or decompensation may be the first indication of advanced AS, particularly in those who have not undergone regular cardiovascular evaluation.\n\nThe primary method for diagnosing AS is echocardiography, which allows visualization of valve anatomy and assessment of transvalvular gradients. However, reliance on symptom reporting or late-stage signs, that trigger a provider to order an echo, can result in missed opportunities for earlier detection. Additionally, electrocardiographic changes-such as left ventricular hypertrophy or strain patterns-can often be detected before structural abnormalities are visible on imaging studies. This delay between electrical changes evolution and later structural findings creates a valuable opportunity to intervene sooner, for example with a valve replacement. This diagnostic latency highlights a critical window where early identification through Artificial Intelligence (AI) analysis of electrocardiograms (ECGs) and timely referral to cardiology can significantly alter disease trajectory and improve outcomes, especially in primary care and community health settings.\n\nAoS-SEDAI study is an observational, retrospective and prospective, multicenter clinical study. Even though controls will be distinguished from AS patients for ground truth and performance evaluation, this is a single-arm study since there are no differences in study interventions.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The target population consists of patients under assessment of Aortic Stenosis (AS).\n\nScreened patients need to have 12-lead ECG recordings within 90 days of a transthoracic echocardiogram (TTE). In the case of multiple ECGs and TTEs per patient, all of them will be selected and analysed accordingly considering the corresponding PI diagnosis at each time. In the event of a patient with a different diagnosis over time (e.g. non-AS confirmed diagnosis later on diagnosed with AS), multiple timepoint ECGs will be categorized so that the same patient is not included both in the control and in the AS populations.\n\nControls will include non-AS patients with relevant TTE and clinical findings during AS differential diagnosis (such as hypertensive heart disease, structural heart diseases, hypertrophic cardiomyopathy among other cardiomyopathies, mild aortic stenosis, and no significant aortic leaflet calcification).', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Age ≥ 18 years;\n* All available 12-lead ECG with a 10 seconds minimum length on raw data digital format will be included\n* Available clinical data corresponding to each ECG to confirm patient demographics and Aortic Stenosis diagnosis\n* Available transthoracic echocardiogram (TTE) within +/- 90 days of each ECG recording\n\nNo exclusion criteria are defined for this study.'}, 'identificationModule': {'nctId': 'NCT07479992', 'acronym': 'AoS-SEDAI', 'briefTitle': 'Multicenter Study for the Validation of Willem AI: Aortic StenoSis Early Diagnosis With AI-electrocardiogram Study', 'organization': {'class': 'INDUSTRY', 'fullName': 'Idoven 1903 S.L.'}, 'officialTitle': 'Multicenter Study for the Validation of Willem AI: Aortic StenoSis Early Diagnosis With AI-electrocardiogram (Willem AoS-SEDAI) Study', 'orgStudyIdInfo': {'id': 'AoS-SEDAI_01'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Aortic Stenosis patients', 'description': 'Subjects with an Aortic Stenosis diagnosis confirmed by the physician following their routine practice (e.g. ESC Guidelines), including complete clinical assessment with echocardiogram or other complementary techniques.', 'interventionNames': ['Device: Willem AI ECG assessment']}, {'label': 'Non-Aortic Stenosis patients (Controls)', 'description': 'Subjects without Aortic Stenosis confirmed by the physician after thorough evaluation following their routine practice.', 'interventionNames': ['Device: Willem AI ECG assessment']}], 'interventions': [{'name': 'Willem AI ECG assessment', 'type': 'DEVICE', 'description': 'There is no study intervention. The Willem AI platform will assess all study electrocardiograms (ECGs) for the identification of aortic stenosis. Regardless of retrospective or prospective enrollment, Willem output will not be provided to the healthcare professional user for clinical evaluation, and therefore routine practice will not be impacted nor altered.', 'armGroupLabels': ['Aortic Stenosis patients', 'Non-Aortic Stenosis patients (Controls)']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Bonn', 'country': 'Germany', 'contacts': [{'name': 'Sebastian Zimmer, MD, PhD', 'role': 'CONTACT'}], 'facility': 'Universitätsklinikum Bonn', 'geoPoint': {'lat': 50.73438, 'lon': 7.09549}}, {'city': 'Munich', 'country': 'Germany', 'contacts': [{'name': 'Moritz von Scheidt, MD, PhD', 'role': 'CONTACT'}], 'facility': 'Technical University of Munich', 'geoPoint': {'lat': 48.13743, 'lon': 11.57549}}], 'centralContacts': [{'name': 'Manuel Marina-Breysse, MD, PhD', 'role': 'CONTACT', 'email': 'clinical@idoven.ai', 'phone': '+34669752391'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Idoven 1903 S.L.', 'class': 'INDUSTRY'}, 'responsibleParty': {'type': 'SPONSOR'}}}}