Computer-guided planning for heart rhythm (atrial fibrillation) procedures
Official title: SCH: Simulation Optimization of Cardiac Surgical Planning
Funded at University of Tennessee Knoxville · NIH-11171655 · Data updated 2026-11-12
This project uses sensors and computer models to help doctors plan catheter ablation for people with atrial fibrillation.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Tennessee Knoxville NIH-funded |
| Lab location | 1 site (Knoxville, United States) |
| Project start | September 7, 2023 |
| Project end | August 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $290,547 |
| NIH project number | 5R01HL172292-03 |
| Project ID | NIH-11171655 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates are people with atrial fibrillation who are scheduled for or considering catheter ablation and who receive care at participating centers.
✕Less likely to help
People without atrial fibrillation, those not undergoing ablation, or patients treated at centers that do not use the project's sensing or planning tools are unlikely to benefit directly.
What this research studies
If I have atrial fibrillation and may need ablation, this work combines data from sensors used during procedures with computer simulations to guide planning. The researchers will merge patient signals with heart physics to train AI that knows how electrical activity flows in the heart. They will run simulations to test different ablation line plans and use physics-augmented machine learning to make the planning more reliable. The goal is to use these tools to reduce variation in outcomes across patients and surgeons.
Why it matters
Potential benefit: If successful, this could help physicians choose safer and more effective ablation plans, lowering the chance of repeat procedures and complications.
How similar studies have gone: Some prior studies using imaging and patient-specific simulations have shown promising results for guiding ablation, but combining physics-informed AI with intraoperative sensor signals is a relatively new and evolving approach.
Where this research is happening
-
University of Tennessee Knoxville
Knoxville, United States Active
Researchers
- Principal investigator: Yao, Bing — University of Tennessee Knoxville
- Study coordinator: Yao, Bing
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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.