DELINEATE-Prospective

Not yet recruiting

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

• Registration Number: NCT07197736
• Lead Sponsor: Columbia University

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.

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.

Study Timeline

• Study First Submitted: 2025-08-14
• Status Verified: 2025-09
• Study First Submitted QC: 2025-09-24
• Last Update Submitted: 2025-09-24
• Study First Posted: 2025-09-29
• Last Update Posted: 2025-09-29
• Study Start: 2025-10-01
• Primary Completion: 2027-10-01
• Study Completion: 2028-10-01

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 50

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)

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Proportion of Clinically Meaningful Reclassification by Panel Review	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 Outcome Measures

Name	Time	Method
Proportion of Cases with AI-Based Reclassification Leading to a Change in Clinical Management	18 months	The proportion will be calculated as the number of cases with any management change ÷ total number of cases reviewed.
Proportion of Cases with AI-Based Reclassification Leading to Referral to a Valve Specialist or Surgeon	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.
Proportion of Cases with AI-Based Reclassification Leading to a Change in Frequency of Follow-Up Echocardiography	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.
Proportion of Cases with AI-Based Reclassification Leading to Referral for Further Testing (TEE or Cardiac MRI)	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.

Trial Locations

Locations (1)

Columbia University Irving Medical Center; 🇺🇸 New York, New York, United States