Multicenter Study for the Validation of Willem AI: Aortic StenoSis Early Diagnosis With AI-electrocardiogram (Willem AoS-SEDAI) Study

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.