Using AI to improve diagnosis and outcomes for mitral valve disease
Official title: Deep Learning Based Phenotyping and Outcomes Prediction for Valvular Heart Disease
Funded at Cedars-Sinai Medical Center · NIH-11064070 · Data updated 2026-11-12
This study is looking at how we can use advanced computer technology to better understand mitral valve disease, helping doctors spot early signs of problems in heart images so they can provide better care and treatment for patients like you.
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 start | May 1, 2024 |
| Project end | April 30, 2029 |
| Funding year | 2025 |
| Award (this year) | $750,403 |
| NIH project number | 5R01HL173526-02 |
| Project ID | NIH-11064070 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals diagnosed with mitral valve disease or those at risk due to factors like age, sex, diabetes, or hypertension.
✕Less likely to help
Patients with no history of mitral valve disease or those who do not undergo echocardiography may not benefit from this research.
What this research studies
This research focuses on mitral valve disease, a condition affecting millions worldwide, and aims to enhance diagnosis and predict outcomes using advanced artificial intelligence techniques. By applying deep learning algorithms to echocardiography images, the study seeks to identify subtle changes in heart structure and function that may indicate early signs of serious complications. The approach involves analyzing large datasets to improve the accuracy of phenotyping and risk assessment for patients with mitral valve issues. Ultimately, this research aims to provide better insights into patient management and treatment options.
Why it matters
Potential benefit: If successful, this research could lead to earlier and more accurate diagnoses of mitral valve disease, potentially improving patient outcomes and reducing the risk of severe complications.
How similar studies have gone: Previous research has demonstrated the effectiveness of AI in analyzing medical imaging, suggesting that this approach has the potential for significant advancements in the field.
Where this research is happening
-
Cedars-Sinai Medical Center
Los Angeles, United States Active
Researchers
- Principal investigator: Ouyang, David — Cedars-Sinai Medical Center
- Study coordinator: Ouyang, David
Related research
Other NIH-funded projects on the same topics.
-
AI to find early mitral valve problems and predict future heart risks
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.
-
Understanding how blood vessel cells change to help or worsen heart valves
Researchers are studying how endothelial cells change and contribute to mitral valve prolapse to find better ways to treat this common heart valve condition.
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Improving heart valve disease diagnosis with stress cardiac MRI
This project aims to create a better way to understand mitral valve problems using a special heart MRI scan, helping doctors decide the best treatment for patients.
-
Understanding the role of PKD3 in heart valve disease
This study is looking into how certain heart cells change and affect heart valve health in people with valvular heart disease, with the hope of finding new ways to treat this condition and help patients feel better.
-
Understanding Cilia's Role in Heart Valve Disease
This project explores how tiny structures called cilia and certain gene changes might cause common heart valve problems like mitral valve prolapse.
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Improving heart valve disease assessment using advanced MRI techniques
This study is testing a new type of heart scan to help doctors better understand and treat mitral valve regurgitation, so if you have this condition, it could lead to more accurate diagnoses and better treatment options for you.
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.