Using machine learning to assess the risk of thoracic aortic aneurysm rupture
Official title: Machine learning-based biomechanical analysis for thoracic aortic aneurysm rupture risk assessment
Funded at University of Miami Coral Gables · NIH-10993130 · Data updated 2026-11-12
This study is looking at how advanced computer technology can help doctors better understand the risk of smaller thoracic aortic aneurysms bursting, so they can identify patients who might need surgery sooner.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Miami Coral Gables NIH-funded |
| Lab location | 1 site (Coral Gables, United States) |
| Project start | December 3, 2021 |
| Project end | November 30, 2026 |
| Funding year | 2025 |
| Award (this year) | $464,137 |
| NIH project number | 5R01HL158829-04 |
| Project ID | NIH-10993130 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are adults with thoracic aortic aneurysms, particularly those with aneurysms smaller than 5 cm in diameter.
✕Less likely to help
Patients with thoracic aortic aneurysms that are already larger than 5.5 cm or those without any aortic aneurysm may not benefit from this research.
What this research studies
This research investigates how machine learning can improve the assessment of rupture risk in thoracic aortic aneurysms (TAAs), particularly for those smaller than the current surgical intervention threshold. By analyzing 3D CT images of the thoracic aorta, the study aims to develop automated models that can reconstruct aorta geometry and perform real-time stress analysis. The goal is to create a probabilistic risk index that combines various patient characteristics to provide a more accurate risk assessment. This innovative approach seeks to identify patients who may benefit from surgical intervention before reaching critical sizes.
Why it matters
Potential benefit: If successful, this research could lead to earlier and more accurate identification of patients at risk for TAA rupture, potentially saving lives through timely surgical intervention.
How similar studies have gone: Other research has shown promise in using machine learning for medical imaging and risk assessment, indicating that this approach could be effective.
Where this research is happening
-
University of Miami Coral Gables
Coral Gables, United States Active
Researchers
- Principal investigator: Liang, Liang — University of Miami Coral Gables
- Study coordinator: Liang, Liang
Related research
Other NIH-funded projects on the same topics.
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Using AI and 3D CT scans to predict rupture risk for thoracic aortic aneurysms
This project uses machine learning plus 3D CT scans to help identify adults with thoracic aortic aneurysms—especially smaller ones—who may be at higher risk of rupture.
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Researchers are combining mouse experiments, computer simulations, and patient scans to find sets of markers that could warn people with ascending aortic aneurysms about the risk of rapid growth, tearing, or rupture.
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Understanding how biomechanical factors affect the growth and rupture of aortic aneurysms
This study is looking at how certain physical changes in the aorta can help us understand why some aortic aneurysms grow or tear, specifically in veterans, so we can improve how we decide when surgery is needed.
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.