Aortic Regurgitation Clinical Trials

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

Status: Recruiting
Location: See location...
Study Type: Observational
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.

Eligibility
Participation Requirements
Sex: All
Minimum Age: 18
Healthy Volunteers: f
View:

• 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

Locations
United States
New York
Columbia University Irving Medical Center
RECRUITING
New York
Contact Information
Primary
Heidi S Hartman, MD
hl2738@cumc.columbia.edu
212-305-3068
Backup
Michelle Castillo, BS
mc5067@cumc.columbia.edu
212-305-9161
Time Frame
Start Date: 2026-04-15
Estimated Completion Date: 2028-10-01
Participants
Target number of participants: 50
Treatments
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.
Sponsors
Leads: Columbia University
Collaborators: American Heart Association

This content was sourced from clinicaltrials.gov