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| Name | Class |
|---|---|
| The Institute of Bioorganic Chemistry, Polish Academy of Sciences | UNKNOWN |
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The purpose of this study is to evaluate the effectiveness of an artificial intelligence (AI) model developed by the investigators for identifying severe low-gradient aortic valve stenosis. Accurate assessment of stenosis severity is crucial for proper qualification for surgical treatment. It is expected that the use of AI will improve diagnostic accuracy and thereby support better clinical outcomes.
Patients with suspected significant low-gradient aortic stenosis will be enrolled. The study is observational and involves no additional risk for participants. Standard imaging studies performed for clinical indications will be additionally analyzed by the AI model, which will classify aortic stenosis as severe or moderate. The model's results will not influence the clinical management of participants but will be compared with physicians' assessments to validate its diagnostic performance.
The study will be conducted in 2025-2026. The findings will provide insights into the usefulness of AI in the diagnosis of severe aortic stenosis and may contribute to the development of advanced clinical decision-support tools.
This study is a prospective multicenter observational validation of an artificial intelligence (AI) model for differentiating severe low-gradient from moderate aortic stenosis using transthoracic echocardiography images. The model, developed and published by the investigators, demonstrated promising diagnostic performance in retrospective data. In the present trial, approximately 300 participants with suspected significant low-gradient aortic stenosis will be enrolled during 2025-2026. Standard imaging studies performed for clinical indications will be analyzed by the AI model, which will classify aortic stenosis as severe or moderate. The AI-derived results will not influence clinical decision-making but will be compared with physicians assessments to evaluate diagnostic accuracy and reproducibility in real-world practice.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Patients with suspected significant low-gradient aortic stenosis. | Approximately 300 patients with suspected significant low-gradient aortic stenosis undergoing standard echocardiographic evaluation between 2025 and 2026. Echocardiographic images will be secondarily analyzed by the AI model to classify stenosis as severe or moderate. The AI results will be compared with physicians assessments. No intervention or modification of clinical care is involved. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI diagnostic test for severe low-gradient aortic stenosis | Diagnostic Test | All participants will undergo standard transthoracic echocardiography performed for clinical indications. Echocardiographic images will be analyzed both by experienced physicians and by the investigational AI model. Additional diagnostic tests (such as cardiac CT, low-dose dobutamine stress echocardiography or transesophageal echocardiography) may be performed if clinically indicated, according to current guideline recommendations. The AI-derived results will not influence clinical decision-making. |
| Measure | Description | Time Frame |
|---|---|---|
| Area Under the Receiver Operating Characteristic Curve (AUC) describing the sensitivity-specificity relationship of the AI model. | AUC will be calculated to assess the ability of the AI model to differentiate between severe low-gradient and moderate aortic stenosis. The analysis will use physician assessment and guideline-based diagnostic criteria as the reference standard. AUC will be reported with 95% confidence intervals. | At the time of the nearest Heart Team meeting following the echocardiographic examination (typically within 1 week). |
| Measure | Description | Time Frame |
|---|---|---|
| Diagnostic performance of the AI model in clinically relevant subgroups. | Diagnostic performance of the AI model (AUC, sensitivity, specificity) in clinically relevant subgroups, such as patients with atrial fibrillation or subtypes of low-gradient aortic stenosis (classic, paradoxical, normal flow). | At the nearest Heart Team meeting following the echocardiographic examination (typically within 1 week). |
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Inclusion Criteria:
Exclusion Criteria:
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The study population will include adult patients (â„18 years) diagnosed in the Echocardiography Laboratory of the National Institute of Cardiology in Warsaw, Department of Valvular Heart Disease. Eligible participants will be those referred for echocardiographic evaluation due to clinical suspicion of significant low-gradient aortic stenosis. Standard-of-care imaging performed for clinical indications will be analyzed. Approximately 300 patients are expected to be enrolled between 2025 and 2026. Patients with previous aortic valve intervention (surgical or transcatheter) or other exclusion criteria will not be included.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| MichaĆ Wrzosek, MD | Contact | +48 22 3434189 | mwrzosek@ikard.pl | |
| Tomasz Hryniewiecki, Professor of Medicine | Contact | +48 223434180 | thryniewiecki@ikard.pl |
| Name | Affiliation | Role |
|---|---|---|
| Tomasz Hryniewiecki, Professor of Medicine | Department of Valvular Heart Disease, National Institute of Cardiology, Warsaw, Poland | Study Chair |
| MichaĆ Wrzosek, MD | Department of Valvular Heart Disease, National Institute of Cardiology, Warsaw, Poland |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of Valvular Heart Disease, National Institute of Cardiology, Warsaw, Poland | Recruiting | Warsaw | Masovian Voivodeship | Poland |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40259202 | Result | Wrzosek M, Buchwald M, Czernik P, Kupinski S, Zatorska K, Jasinska A, Zakrzewski D, Pukacki J, Mazurek C, Pekal R, Hryniewiecki T. Diagnosing Severe Low-Gradient vs Moderate Aortic Stenosis with Artificial Intelligence Based on Echocardiography Images. J Imaging Inform Med. 2026 Feb;39(1):926-932. doi: 10.1007/s10278-025-01497-4. Epub 2025 Apr 21. |
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| ID | Term |
|---|---|
| D001024 | Aortic Valve Stenosis |
| ID | Term |
|---|---|
| D000082862 | Aortic Valve Disease |
| D006349 | Heart Valve Diseases |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
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| Karina Zatorska, MD, PhD | Department of Valvular Heart Disease, National Institute of Cardiology, Warsaw, Poland | Study Director |
| D014694 |
| Ventricular Outflow Obstruction |