Mapping hidden 3D heart circuits that cause ventricular tachycardia
Official title: Elucidating 3D Constructs of Reentrant Circuits via a Novel Noninvasive Hybrid-AI System
Funded at Rochester Institute of Technology · NIH-11233764 · Data updated 2026-11-12
This project uses a new AI-plus-physics approach to create three-dimensional maps of the hidden heart circuits that cause scar-related ventricular tachycardia so doctors can target ablation more accurately.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Rochester Institute of Technology NIH-funded |
| Lab location | 1 site (Rochester, United States) |
| Project start | February 8, 2019 |
| Project end | August 31, 2030 |
| Funding year | 2025 |
| Award (this year) | $672,323 |
| NIH project number | 2R01HL145590-06A1 |
| Project ID | NIH-11233764 on NIH RePORTER |
Who could benefit from this research
✓Could help
People with scar-related ventricular tachycardia—often after a heart attack or with structural heart disease—who are candidates for catheter ablation would be the most likely participants.
✕Less likely to help
This approach may not help people with non-reentrant arrhythmias, primary electrical channelopathies, or those whose ECGs are not interpretable or who are not candidates for ablation.
What this research studies
Researchers combine body-surface ECGs with physics-based heart models and neural networks to detect the tiny signals of reentrant circuits buried in scar tissue and reconstruct their 3D paths through the heart wall. The method is noninvasive, using surface recordings and computerized modeling rather than invasive catheter mapping to estimate circuit depth and course. The team will build on prior prototype data, validate the approach against imaging and clinical ablation findings, and refine the hybrid AI to improve detection and decision support for physicians.
Why it matters
Potential benefit: If successful, this could help doctors pinpoint ablation targets more precisely, reduce repeat procedures, and lower the risk of sudden death from VT.
How similar studies have gone: Noninvasive ECG/ECGI mapping methods have shown promise guiding ablation, but using a hybrid neural-physics AI to reconstruct intramural 3D reentrant circuits is a novel and less-tested approach.
Where this research is happening
-
Rochester Institute of Technology
Rochester, United States Active
Researchers
- Principal investigator: Wang, Linwei — Rochester Institute of Technology
- Study coordinator: Wang, Linwei
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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.