Valve Disease, AorticDELINEATENCT07197736
DELINEATE-Prospective
An observational study of Valve Disease, Aortic and Mitral Regurgitation (MR), run by Columbia University at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Valve Disease, Aortic, Mitral Regurgitation (MR), Aortic Stenosis
- How many people
- 50 participants
- Where
- 1 siteUnited States
- Study dates
- 15 Apr 2026to 1 Oct 2028
- Phase
- Observational
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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
Who may be able to join
- 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
Who cannot join
- Physician in training (cardiology fellow or advanced imaging fellow)
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Columbia University Irving Medical Center
New York, New York, 10032
Jeffrey Ruhl, MS, 570-713-7815, hvx9001@nyp.org
Michelle Castillo, BS, 212-305-9161, mc5067@cumc.columbia.edu
About this study
Official title: Deep Learning for Echo Analysis, Tracking, and Evaluation Prospective Evaluation (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.
Read more
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.
Read the detailed description
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.
- Conditions
- Valve Disease, Aortic, Mitral Regurgitation (MR), Aortic Stenosis, Valvular Heart Disease, Tricuspid Regurgitation (TR), Aortic Regurgitation
- Keywords
- artificial intelligence, deep learning, valvular heart disease, echocardiography, cardiovascular disease, mitral regurgitation, aortic stenosis
- Medical subject headings
- Aortic Valve Disease, Mitral Valve Insufficiency, Aortic Valve Stenosis, Heart Valve Diseases, Tricuspid Valve Insufficiency, Aortic Valve Insufficiency, Cardiovascular Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 18 months
Secondary outcomes (4)
Proportion of Cases with AI-Based Reclassification Leading to a Change in Clinical Management
The proportion will be calculated as the number of cases with any management change ÷ total number of cases reviewed.
Measured: 18 months
Proportion of Cases with AI-Based Reclassification Leading to Referral to a Valve Specialist or Surgeon
Definition: The proportion will be calculated as the number of cases referred to a valve specialist or surgeon ÷ total number of cases reviewed.
Measured: 18 months
Proportion of Cases with AI-Based Reclassification Leading to a Change in Frequency of Follow-Up Echocardiography
The proportion will be calculated as the number of cases with a change in recommended follow-up echocardiography frequency ÷ total number of cases reviewed.
Measured: 18 months
Proportion of Cases with AI-Based Reclassification Leading to Referral for Further Testing (TEE or Cardiac MRI)
The proportion will be calculated as the number of cases referred for further testing with TEE or cardiac MRI ÷ total number of cases reviewed.
Measured: 18 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Sep 2025
- Study start15 Apr 2026 (estimated)
- Primary completion1 Oct 2027 (estimated)
- Study completion1 Oct 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Valve Disease, AorticStudies of the same condition, open and under way.
- More trials run by Columbia UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 50 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Columbia University
- Sponsor type
- University, hospital or other
- Collaborators
- American Heart Association
- Responsible party
- Principal investigator, Pierre Elias, Columbia University
- Investigators
- Pierre A Elias, MD, Principal investigator, Columbia University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07197736
- Sponsor's protocol ID
- AAAU9603
- Acronym
- DELINEATE
- Status last verified
- Apr 2026
- First submitted
- 14 Aug 2025
- Last updated
- 16 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Patient privacy and confidentiality: Even with de-identification, sharing detailed health data could risk re-identification of participants. Regulatory restrictions: Institutional Review Boards (IRBs), HIPAA rules, or local laws may limit data sharing, especially for sensitive health information like echocardiograms. Consent limitations: If participants did not explicitly consent to broad data sharing at enrollment, the study cannot ethically or legally provide their IPD.