AI to find early mitral valve problems and predict future heart risks
Deep Learning Based Phenotyping and Outcomes Prediction for Valvular Heart Disease
This project uses artificial intelligence on heart ultrasound images to find early mitral valve changes and predict which adults might later develop serious heart complications.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Cedars-Sinai Medical Center NIH-funded |
| Lab location | 1 site (Los Angeles, United States) |
| Project ID | NIH-11325063 on NIH RePORTER |
What this research studies
The team will train deep learning models on thousands of echocardiograms linked to clinical outcomes to learn subtle imaging patterns of mitral valve disease and myocardial remodeling. If your images are included, the AI will look for changes that are hard for humans to see and compare them with later events like worsening mitral regurgitation, heart failure, or cardiac arrest. The work combines image analysis with patient health records to build risk prediction tools. The aim is to create automated signals that could one day help clinicians decide who needs closer follow-up or earlier treatment.
Who could benefit from this research
Good fit: Adults aged 21 and older with known or suspected mitral valve disease or who have had echocardiograms are the most likely candidates for participation.
Not a fit: People under 21, those without available echocardiogram images or clinical follow-up data, or those with valve problems unrelated to the mitral valve may not see direct benefit from this work.
Why it matters
Potential benefit: If successful, the approach could help detect dangerous valve changes earlier and guide timely treatment to reduce heart failure and sudden complications.
How similar studies have performed: Previous AI work on echocardiography has shown promising ability to measure heart structure and flag risk features, but using AI to predict long-term mitral valve outcomes is a newer and still-emerging application.
Where this research is happening
Los Angeles, United States
- Cedars-Sinai Medical Center — Los Angeles, United States (Active)
Researchers
- Principal investigator: Ouyang, David — Cedars-Sinai Medical Center
- Study coordinator: Ouyang, David
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.