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NCT Number: NCT07197736

DELINEATE-Prospective

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.

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Key information

Conditions

Valve Disease, Aortic Aortic Regurgitation Aortic Stenosis Aortic Valve Disease Aortic Valve Insufficiency Aortic Valve Stenosis Cardiovascular Diseases Heart Diseases Heart Valve Diseases Mitral Regurgitation (MR) Mitral Valve Insufficiency Tricuspid Regurgitation (TR) Tricuspid Valve Insufficiency Valvular Heart Disease Ventricular Outflow Obstruction

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

In a prior Columbia University study, a series of deep learning algorithms analyzing echocardiograms is in development. These algorithms include, but are not limited to, algorithms that enable view classification, structure identification, left ventricle (LV) dimension measurements, Left Ventricular Ejection Fraction (LVEF) determination, left atrium (LA) volume assessments, and valvular heart disease diagnosis. Briefly, these algorithms are based on architectures shown to be useful in image and video analysis, including ones specific to echocardiography interpretation. Algorithms based off these architectures can be generalized to interpretation of video-based echocardiogram data such as valvular regurgitation assessment. As part of this study protocol, these models will continue to be developed using patient echocardiogram data. This study aims to create an automated, end-to-end system that can deliver deep learning analyses of echocardiograms to the interpreting cardiologist in real-time. If successful, this program could enable improvements in echocardiography reading efficiency and reliability.

Who can participate

Healthy volunteers accepted: No

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

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)

Treatment and study plan

Primary outcomes

  1. Proportion of Clinically Meaningful Reclassification by Panel Review

    Time frame: 18 months

    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.

Secondary outcomes

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

    Time frame: 18 months

    The proportion will be calculated as the number of cases with any management change ÷ total number of cases reviewed.

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

    Time frame: 18 months

    Definition: The proportion will be calculated as the number of cases referred to a valve specialist or surgeon ÷ total number of cases reviewed.

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

    Time frame: 18 months

    The proportion will be calculated as the number of cases with a change in recommended follow-up echocardiography frequency ÷ total number of cases reviewed.

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

    Time frame: 18 months

    The proportion will be calculated as the number of cases referred for further testing with TEE or cardiac MRI ÷ total number of cases reviewed.

Other outcomes

  1. Concordance Between AI and Panel Review

    Time frame: 18 months

    Proportion of cases where AI classification agrees with expert panel review of valvular regurgitation severity.

  2. Concordance Between Cardiologist Clinical Read and Panel Review

    Time frame: 18 months

    Proportion of cases where cardiologist clinical interpretation agrees with expert panel review of valvular regurgitation severity.

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

    Time frame: 18 months

    Difference between the concordance rate of AI vs panel review and the concordance rate of cardiologist clinical read vs panel review.

  4. Inter-Reader Agreement for Categorical Echocardiographic Measures

    Time frame: 18 months

    Agreement between independent cardiologist readers for categorical variables (e.g., severity of valvular regurgitation) will be quantified using Cohen's kappa statistic.

  5. Inter-Reader Agreement for Continuous Echocardiographic Measures

    Time frame: 18 months

    Agreement between independent cardiologist readers for continuous measures (e.g., left ventricular ejection fraction [LVEF] category) will be quantified using the intraclass correlation coefficient (ICC)

Study contacts

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

Heidi S Hartman, MD

CONTACT

[email protected]

212-305-3068

Michelle Castillo, BS

CONTACT

[email protected]

212-305-9161

Sponsors and collaborators

Lead sponsor

Columbia University

Other

Collaborators

  • American Heart Association

Registry information

Official study title

Deep Learning for Echo Analysis, Tracking, and Evaluation Prospective Evaluation (DELINEATE-Prospective)

Acronym: DELINEATE

Important dates

Study start
2026
Primary completion
2027
Study completion
2028
First posted
Sep 29, 2025
Registry last updated
Apr 16, 2026

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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