Diagnosing Severe Low-Gradient vs Moderate Aortic Stenosis with Artificial Intelligence Based on Echocardiography Images.
Michał Wrzosek, Mikolaj Buchwald, Patryk Czernik...
https://pubmed.ncbi.nlm.nih.gov/40259202Actively Recruiting
Age: 18Years +
All Genders
ID07144189
Artificial Intelligence-Based Assessment of Low-Gradient Aortic Stenosis Severity Using Echocardiographic Images
Led by National Institute of Cardiology, Warsaw, Poland · Updated on 2025-12-02
300
Participants Needed
1
Research Sites
N/A
Total Duration
On this page
Sponsors
N
National Institute of Cardiology, Warsaw, Poland
Lead Sponsor
T
The Institute of Bioorganic Chemistry, Polish Academy of Sciences
Collaborating Sponsor
AI-Summary
What this Trial Is About
Researchers are evaluating an artificial intelligence (AI) model designed to identify severe low-gradient aortic valve stenosis using echocardiographic images. This condition requires accurate diagnosis to determine the need for surgical treatment. The study is observational and aims to validate the AI model's diagnostic accuracy by comparing its assessments with those of experienced physicians in real-world practice. Participants with suspected significant low-gradient aortic stenosis will have standard transthoracic echocardiography performed as part of their clinical care. The echocardiographic images will then be analyzed by the AI model to classify the stenosis as severe or moderate. Additional diagnostic tests may be done if clinically needed, but the AI results will not affect clinical decisions. Approximately 300 patients will be enrolled during 2025-2026. During the study, the AI model's performance will be measured by the area under the receiver operating characteristic curve (AUC) and diagnostic accuracy in specific patient subgroups. These assessments will occur around the time of the nearest Heart Team meeting following echocardiography, typically within one week. The study involves no additional risk or changes to patient care, focusing on collecting data to improve future diagnostic tools.
CONDITIONS
Brief Title
AI Assessment of Low-Gradient Aortic Stenosis Severity Based on Echocardiography
Who Can Participate
Age: 18Years +
All Genders
Eligibility Criteria
You may qualify if you...
- Age 38; 18 years
- Clinical suspicion of significant low-gradient aortic stenosis
- Echocardiographic examination performed for clinical indications
- Ability to provide informed consent
You will not qualify if you...
- Previous aortic valve intervention (surgical or transcatheter)
- Inadequate image quality precluding echocardiographic analysis
- Concomitant severe valvular disease (severe mitral stenosis or mitral/aortic regurgitation) that could confound assessment
- Patients unwilling or unable to provide informed consent
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
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Your Study Journey
Screening
Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
1 visit (in-person) during which eligibility is assessed and informed consent is obtained
Diagnostic Evaluation
Duration - Up to 1 week
Participants undergo standard transthoracic echocardiography and other clinically indicated diagnostic tests. Echocardiographic images are analyzed by physicians and the AI model for classification of aortic stenosis severity.
1 visit (in-person) for echocardiographic examination and image collection
Long-term Monitoring
Duration - Up to several months depending on routine clinical follow-up
Participants are observed without intervention as clinical care continues independently of the trial. AI results do not influence clinical decisions.
Visit schedule as per routine clinical care; no additional trial visits
Trial Site Locations
Total: 1 location
1
Department of Valvular Heart Disease, National Institute of Cardiology, Warsaw, Poland
Warsaw, Masovian Voivodeship, Poland
Actively Recruiting
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Research Team
M
Michał Wrzosek, MD
T
Tomasz Hryniewiecki, Professor of Medicine
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
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