AI-powered personalized heart and blood-flow modeling from medical images
SCH: Efficient Image-based Hemodynamic Modeling via Physics-integrated Bayesian Deep Learning
Using AI to turn heart and vessel scans into fast, personalized blood-flow maps to help people with heart or vascular conditions get clearer treatment information.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Cornell University NIH-funded |
| Lab location | 1 site (Ithaca, United States) |
| Project ID | NIH-11376133 on NIH RePORTER |
What this research studies
This project builds AI tools that automatically convert your CT, MRI, or ultrasound images into 3-D models of your heart and blood vessels and then runs fast computer simulations of blood flow and wall stress. The team combines physics-based knowledge with deep learning so the results are both rapid and grounded in real blood-flow principles. They will add Bayesian methods to show how certain each prediction is and create visual tools so doctors and patients can explore many simulated scenarios. The approach aims to reduce the manual work, time, and computational cost of current image-based modeling.
Who could benefit from this research
Good fit: Ideal candidates are people with heart or blood-vessel conditions who have recent CT, MRI, or ultrasound images and are willing to share those images for analysis.
Not a fit: People without cardiovascular problems or without usable medical imaging are unlikely to benefit directly from this work.
Why it matters
Potential benefit: If successful, this could give patients quicker, more reliable personalized blood-flow information to guide diagnosis and treatment choices.
How similar studies have performed: Related AI methods have shown promise for speeding up blood-flow modeling, but the specific blend of physics-integrated, mesh-aware deep learning with Bayesian uncertainty quantification is relatively new.
Where this research is happening
Ithaca, United States
- Cornell University — Ithaca, United States (Active)
Researchers
- Principal investigator: Wang, Jian-Xun — Cornell University
- Study coordinator: Wang, Jian-Xun
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.