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NCT Number: NCT07144189

AI Assessment of Low-Gradient Aortic Stenosis Severity Based on Echocardiography

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.

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Key information

Conditions

Low-gradient Aortic Stenosis Aortic Stenosis Aortic Valve Disease Aortic Valve Stenosis Cardiovascular Diseases Heart Diseases Heart Valve Diseases Ventricular Outflow Obstruction

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Department of Valvular Heart Disease, National Institute of Cardiology, Warsaw, Poland

Warsaw, Masovian Voivodeship, Poland

Location status: Recruiting

Location contact

Michał Wrzosek, MD

CONTACT

[email protected]

+48516652370

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Age ≥ 18 years
  • Clinical suspicion of significant low-gradient aortic stenosis
  • Echocardiographic examination performed for clinical indications
  • Ability to provide informed consent

Exclusion criteria

  • 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

Treatment and study plan

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.

Primary outcomes

  1. Area Under the Receiver Operating Characteristic Curve (AUC) describing the sensitivity-specificity relationship of the AI model.

    Time frame: At the time of the nearest Heart Team meeting following the echocardiographic examination (typically within 1 week).

    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.

Secondary outcomes

  1. Diagnostic performance of the AI model in clinically relevant subgroups.

    Time frame: At the nearest Heart Team meeting following the echocardiographic examination (typically within 1 week).

    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).

Study contacts

Contact information is provided by the study sponsor or research team.

Michał Wrzosek, MD

CONTACT

[email protected]

+48 22 3434189

Tomasz Hryniewiecki, Professor of Medicine

CONTACT

[email protected]

+48 223434180

Sponsors and collaborators

Lead sponsor

National Institute of Cardiology, Warsaw, Poland

Other

Collaborators

  • The Institute of Bioorganic Chemistry, Polish Academy of Sciences

Registry information

Official study title

Artificial Intelligence-Based Assessment of Low-Gradient Aortic Stenosis Severity Using Echocardiographic Images

Acronym: ASAI-POL

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Aug 27, 2025
Registry last updated
Dec 2, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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