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

NCT ID: NCT07144189

Last Updated: 2025-12-02

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 300 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-08-20
• Study Completion Date: 2026-08-20

Brief Summary

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.

Detailed Description

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.

Conditions

Low-gradient Aortic Stenosis; Aortic Stenosis

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

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.

AI diagnostic test for severe low-gradient aortic stenosis

Intervention Type: 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.

Interventions

AI diagnostic test for severe low-gradient aortic stenosis

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.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

The Institute of Bioorganic Chemistry, Polish Academy of Sciences

UNKNOWN

Sponsor Role: collaborator

National Institute of Cardiology, Warsaw, Poland

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Tomasz Hryniewiecki, Professor of Medicine

Role: STUDY_CHAIR

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

Michał Wrzosek, MD

Role: PRINCIPAL_INVESTIGATOR

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

Karina Zatorska, MD, PhD

Role: STUDY_DIRECTOR

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

Locations

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

Warsaw, Masovian Voivodeship, Poland

Site Status: RECRUITING

Countries

Poland

Central Contacts

Michał Wrzosek, MD

Role: CONTACT

mwrzosek@ikard.pl

+48 22 3434189

Tomasz Hryniewiecki, Professor of Medicine

Role: CONTACT

thryniewiecki@ikard.pl

+48 223434180

Facility Contacts

Michał Wrzosek, MD

Role: primary

mwrzosek@ikard.pl

+48516652370

References

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. 2025 Apr 21. doi: 10.1007/s10278-025-01497-4. Online ahead of print.

Reference Type: RESULT

PMID: 40259202 (View on PubMed)

Other Identifiers

4.35/VI/25

Identifier Type: -

Identifier Source: org_study_id