DELINEATE-Prospective

NCT07197736 | Recruiting

• 1 other identifier: AAAU9603
• Study Type: observational
• Target: 50
• Locations: 1 country
• Sites: 1

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

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

Keywords

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

Outcome Measures

Primary Outcomes (1)

• Proportion of Clinically Meaningful Reclassification by Panel Review

  Proportion of cases where the expert panel reclassifies valvular regurgitation severity by at least one grade (upgrade or downgrade). The proportion will be calculated as the number of cases with reclassification ÷ total number of cases reviewed.

  18 months

Secondary Outcomes (4)

• Proportion of Cases with AI-Based Reclassification Leading to a Change in Clinical Management

  18 months

• Proportion of Cases with AI-Based Reclassification Leading to Referral to a Valve Specialist or Surgeon

  18 months

• Proportion of Cases with AI-Based Reclassification Leading to a Change in Frequency of Follow-Up Echocardiography

  18 months

• Proportion of Cases with AI-Based Reclassification Leading to Referral for Further Testing (TEE or Cardiac MRI)

  18 months

Other Outcomes (5)

• Concordance Between AI and Panel Review

  18 months

• Concordance Between Cardiologist Clinical Read and Panel Review

  18 months

• Comparison of Concordance Rates (AI vs Cardiologist) Against Panel Review

  18 months

Study Arms (2)

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Board-certified attending cardiologists at Columbia University, ColumbiaDoctors, or NewYork-Presbyterian Hospital who interpret transthoracic echocardiograms in the Columbia echocardiography laboratory and have provided informed consent to participate

You may qualify if:

• 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

You may not qualify if:

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

Sponsors & Collaborators

• Columbia University: lead
• American Heart Association: collaborator

Study Sites (1)

Columbia University Irving Medical Center

New York, New York, 10032, United States

RECRUITING

MeSH Terms

Conditions

Aortic Valve Disease; Mitral Valve Insufficiency; Aortic Valve Stenosis; Heart Valve Diseases; Tricuspid Valve Insufficiency; Aortic Valve Insufficiency; Cardiovascular Diseases

Condition Hierarchy (Ancestors)

Heart Diseases; Ventricular Outflow Obstruction

Study Officials

• Pierre A Elias, MD

  Columbia University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Heidi S Hartman, MD

CONTACT

212-305-3068; hl2738@cumc.columbia.edu

Michelle Castillo, BS

CONTACT

212-305-9161; mc5067@cumc.columbia.edu

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor of Medicine in the Department of Biomedical Informatics

Study Record Dates

• First Submitted: August 14, 2025
• First Posted: September 29, 2025
• Study Start: April 15, 2026
• Primary Completion (Estimated): October 1, 2027
• Study Completion (Estimated): October 1, 2028
• Last Updated: April 16, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)