Using AI to understand aortic valve disease and predict patient outcomes
Official title: AI-enabled characterization of fibrocalcific aortic valve disease and inflammation from CT Angiography: Patient-specific outcome prediction
Funded at Cedars-Sinai Medical Center · NIH-11181172 · Data updated 2026-11-12
This project uses artificial intelligence to better understand aortic valve disease from CT scans and predict how the condition might progress for patients.
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 | September 1, 2024 |
| Project end | May 31, 2028 |
| Funding year | 2025 |
| Award (this year) | $788,556 |
| NIH project number | 5R01HL175875-02 |
| Project ID | NIH-11181172 on NIH RePORTER |
Who could benefit from this research
✓Could help
This work is relevant for patients diagnosed with aortic stenosis who undergo CT angiography.
✕Less likely to help
Patients without aortic stenosis or those who do not undergo CT angiography would not directly benefit from this specific prediction tool.
What this research studies
Aortic stenosis is a common heart valve condition where the valve narrows, leading to heart problems. Currently, doctors often wait until symptoms are severe before recommending valve replacement, but there isn't a good way to know which patients will get worse quickly. This project uses advanced artificial intelligence to analyze CT scans of the heart valve, looking for specific tissue characteristics and signs of inflammation. By combining these detailed scan findings with other patient information, we hope to create a new way to predict how the disease will progress and the risk of complications after valve replacement. This could help doctors make more timely decisions about care.
Why it matters
Potential benefit: If successful, this work could help doctors predict which patients with aortic stenosis are at higher risk for rapid disease progression or complications after valve replacement, allowing for more personalized and timely treatment plans.
How similar studies have gone: Preliminary studies guide this proposal, suggesting a foundation for this AI-driven approach, though the integrated risk score is a novel development.
Where this research is happening
-
Cedars-Sinai Medical Center
Los Angeles, United States Active
Researchers
- Principal investigator: Dey, Damini — Cedars-Sinai Medical Center
- Study coordinator: Dey, Damini
Related research
Other NIH-funded projects on the same topics.
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GATA4's role in why some hearts are born with two valve cusps
A study in mice and human genetics will look at how a specific GATA4 gene change leads to bicuspid aortic valve and related heart problems.
-
AI-guided prediction of aortic valve disease progression using CT scans
A patient-friendly AI tool analyzes CT angiography data to predict how quickly aortic valve disease may advance and the risk of complications after valve replacement.
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Small molecule therapy for calcific aortic valve disease
A new drug candidate is being studied to slow or stop calcification of the aortic valve in adults with valve disease.
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Understanding and treating congenital aortic valve problems
This project develops medicines targeting the genetic and molecular causes of malformed aortic valves to help people born with bicuspid or other congenital valve problems avoid or delay surgery.
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Understanding why some people with calcific aortic valve disease develop atherogenic vs non-atherogenic patterns
This NIH-funded project explores why some people with aortic valve disease have accompanying atherosclerosis while others do not, using human genetics, proteomics, and imaging data to uncover differences that could lead to better treatments.
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Lab-grown models of how aging and sex affect aortic valve narrowing
Researchers will build lab-grown heart valve models that mimic aging and male/female differences to learn why older adults develop aortic valve narrowing.
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.