AI-powered, patient-specific heart flow modeling platform
Official title: SCH: Efficient Image-based Hemodynamic Modeling via Physics-integrated Bayesian Deep Learning
Funded at Cornell University · NIH-11402826 · Data updated 2026-11-12
A patient-focused project to create a fast, AI-assisted way to turn medical heart images into accurate simulations of blood flow and pressure for individual patients.
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 start | September 1, 2024 |
| Project end | May 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $328,388 |
| NIH project number | 5R01HL177814-03 |
| Project ID | NIH-11402826 on NIH RePORTER |
Who could benefit from this research
✓Could help
Patients with cardiovascular conditions who have suitable imaging data (e.g., MRI/CT scans) that can be transformed into 3-D heart geometries for modeling.
✕Less likely to help
Patients without high-quality imaging data or with conditions not addressed by the modeling platform may not benefit.
What this research studies
This project will combine artificial intelligence with physics-based modeling to convert heart images into personalized 3-D models for computational fluid dynamics and heart-wall interactions. It aims to provide fast, data-driven predictions of blood flow, pressures, and wall stresses that clinicians can use to understand a patient’s cardiovascular health. The approach uses differentiable programming to couple machine learning with physics, and a Bayesian framework to quantify uncertainty in the results. Advanced visual analytics will help clinicians interpret large sets of hemodynamic data for better decision making.
Why it matters
Potential benefit: If successful, patients could receive faster, more accurate assessments of their heart health and personalized treatment planning based on realistic simulations.
How similar studies have gone: Previous efforts show promise for image-based heart modeling, but this project aims to dramatically increase speed and reliability with AI and uncertainty quantification.
Where this research is happening
-
Cornell University
Ithaca, United States Active
Researchers
- Principal investigator: Wang, Jian-Xun — Cornell University
- Study coordinator: Wang, Jian-Xun
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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.