Recruiting

AI Echocardiography

Sponsor:

Columbia University

Code:

NCT07197736

Conditions

Valve Disease, Aortic

Mitral Regurgitation (MR)

Aortic Stenosis

Valvular Heart Disease

Tricuspid Regurgitation (TR)

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Not accepted

Study Details

Brief summary:

Heart disease is the leading cause of death in the United States, and echocardiography (or "echo") is the most common way doctors look at the heart. Echo is safe, painless, and can detect major heart problems, including weak heart pumping and valve disease.

Valve disease, especially aortic stenosis (narrowing) and mitral regurgitation (leakage), is common in older adults but often goes undiagnosed. While echo is the main tool for finding valve problems, it takes time, requires expert training, and results can vary between readers.

Recent advances in artificial intelligence (AI), especially deep learning (DL), have shown promise in automatically analyzing heart images. However, past research hasn't fully tackled key echo techniques-like color Doppler and spectral Doppler-that are crucial for measuring how blood moves through heart valves. AI tools also face challenges in being used in everyday medical practice because of workflow issues, lack of real-world testing, and concerns about how the algorithms make decisions.

At Columbia University Irving Medical Center, researchers have built a large database of heart tests over the last six years and developed AI programs to analyze echocardiograms. The current study will test whether providing AI analysis to cardiologists in real time during echo reading can make the process faster and more consistent.

Conditions

Valve Disease, Aortic

Mitral Regurgitation (MR)

Aortic Stenosis

Valvular Heart Disease

Tricuspid Regurgitation (TR)

Study ID

NCT07197736

Start date

Apr 15, 2026

Status verified date

Apr, 2026

Completion date

Oct 1, 2028

Anticipated

Primary completion date

Oct 1, 2027

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Not accepted

Inclusion Criteria:

  • Attending cardiologist employed by Columbia University, ColumbiaDoctors, or NewYork Presbyterian Hospital who reads transthoracic echocardiograms in the Columbia echocardiography laboratory
  • Provided informed consent to take part in the questionnaires or pivotal study

Exclusion Criteria:

  • Physician in training (cardiology fellow or advanced imaging fellow)

Study Design

Enrollment

50 participants

Anticipated

Interventions and Outcome Measures

Arms

Intervention Group

Studies meeting the following criteria will undergo adjudication by an expert panel: Moderate, moderate-severe, or severe mitral, aortic, or tricuspid regurgitation by physician or AI model assessment.

Discrepancy between physician and AI interpretations, where AI-assessed severity is greater than the physician-assessed severity (i.e. indicates that more valvular regurgitation is present)

Control Group

A stratified random sample of cases will be selected to match the distribution of AI-flagged cases by physician-assessed valvular regurgitation severity and will undergo the same expert panel adjudication.

Primary outcome measure

  • Proportion of Clinically Meaningful Reclassification by Panel Review [ Time Frame: 18 months ]

Central Contacts and Locations

Central contacts

Locations

Columbia University Irving Medical Center

Recruiting

New York, New York, United States, 10032

Contacts

Principal Investigator:

Pierre A Elias, MD

More Information

Sponsor

Columbia University

Last update posted

Apr 16, 2026

Last verified

Apr, 2026

Keywords

  • artificial intelligence
  • deep learning
  • valvular heart disease
  • echocardiography
  • cardiovascular disease
  • mitral regurgitation
  • aortic stenosis

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-10. This information was provided to ClinicalTrials.gov by Columbia University on 2026-04-16.