AoS-SEDAI study is an observational, multicenter, retrospective and prospective clinical study. This 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.
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. The 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. AoS-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.
Study Type
OBSERVATIONAL
Enrollment
5,000
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.
Universitätsklinikum Bonn
Bonn, Germany
Technical University of Munich
Munich, Germany
Willem performance to detect severe Aortic Stenosis
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). The Standard Of Care (SOC) investigator diagnosis will be used as the ground truth.
Time frame: ECG will be performed at baseline, and ECG analysis by Willem AI will be performed retrospectively throughout the trial, and finalized upon recruitment completion.
Willem performance to detect moderate Aortic Stenosis
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.
Time frame: ECG will be performed at baseline, and ECG analysis by Willem AI will be performed retrospectively throughout the trial, and finalized upon recruitment completion.
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