Using advanced machine learning to predict the growth of thoracic aortic aneurysms.
Official title: Neural Operator Learning to Predict Aneurysmal Growth and Outcomes
Funded at Yale University · NIH-11063198 · Data updated 2026-11-12
This study is working on a new tool that uses advanced technology to better predict how thoracic aortic aneurysms (TAAs) grow and what might happen next, helping patients get more personalized care and potentially improving their treatment options.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project start | April 1, 2023 |
| Project end | March 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $671,287 |
| NIH project number | 5R01HL168473-03 |
| Project ID | NIH-11063198 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals diagnosed with thoracic aortic aneurysms, particularly those with risk factors such as uncontrolled hypertension or genetic predispositions.
✕Less likely to help
Patients with aortic conditions unrelated to aneurysms or those who do not meet the specific risk criteria may not benefit from this research.
What this research studies
This research focuses on developing a new machine learning tool that utilizes mechanobiological and biomechanical data to predict the growth of thoracic aortic aneurysms (TAAs) and their potential outcomes. By analyzing complex data patterns, the study aims to improve the accuracy of predicting life-threatening events such as dissection and rupture, which are currently inadequately addressed by existing methods. Patients will benefit from a more personalized approach to monitoring their condition, potentially leading to better timing for surgical interventions and improved overall outcomes.
Why it matters
Potential benefit: If successful, this research could lead to more accurate predictions of aneurysm growth, allowing for timely and appropriate interventions that could save lives.
How similar studies have gone: Other research has shown promise in using machine learning approaches for predicting medical outcomes, indicating that this innovative method could be a significant advancement in the field.
Where this research is happening
-
Yale University
New Haven, United States Active
Researchers
- Principal investigator: Assi, Roland — Yale University
- Study coordinator: Assi, Roland
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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.