DELINEATE-Prospective

Part of paid clinical trials in New York, New York.

Sponsor
Columbia University
Study ID
NCT07197736
Status
Recruiting
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Conditions

  • Aortic Regurgitation
  • Aortic Stenosis
  • Mitral Regurgitation (MR)
  • Tricuspid Regurgitation (TR)
  • Valve Disease, Aortic
  • Valvular Heart Disease

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Not accepted

Study Details

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.

Key Dates

Start date
Apr 15, 2026
Status verified
Apr 2026
Primary completion
Oct 1, 2027
Completion
Oct 1, 2028

Study Design

Enrollment
50 participants (estimated)

Arms

  • Arm: 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)
  • Arm: 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

Locations (1)

FacilityCityStateZIPSite coordinators
Columbia University Irving Medical CenterNew YorkNew York10032
Jeffrey Ruhl, MS
[email protected]
570-713-7815
Michelle Castillo, BS
[email protected]
212-305-9161
Pierre A Elias, MD (PRINCIPAL_INVESTIGATOR)

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