Personalized CRT planning using advanced heart imaging and AI
Official title: Deep Learning to Advance Cardiac Magnetic Resonance Strain Imaging and Heart Failure Treatment Planning
Funded at University of Virginia · NIH-11461313 · Data updated 2026-11-12
A scientist is testing a new AI-assisted imaging approach to help doctors choose the best CRT device (BIVP or LBBAP) for heart failure patients with specific conduction problems.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Virginia NIH-funded |
| Lab location | 1 site (Charlottesville, United States) |
| Project start | August 1, 2026 |
| Project end | May 31, 2031 |
| Funding year | 2026 |
| Award (this year) | $760,465 |
| NIH project number | 1R01HL181107-01A1 |
| Project ID | NIH-11461313 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults with heart failure considered for CRT and who have conduction system issues such as left bundle branch block.
✕Less likely to help
Patients not eligible for CRT or those without conduction-system-related heart failure symptoms may not benefit.
What this research studies
This project aims to use advanced heart MRI data and AI tools to improve how clinicians predict which CRT approach will help a patient most. Researchers will compare conventional MRI-based strain measures with more detailed, high-precision strain data to guide device choice. The work focuses on translating imaging insights into clearer treatment plans that could reduce non-response to CRT. The effort involves collaboration with clinicians and will consider real patient imaging data to refine decision-making. If successful, it could lead to more personalized, effective CRT strategies in busy clinical settings.
Why it matters
Potential benefit: If successful, patients may receive more effective CRT therapy sooner, reducing symptoms and improving quality of life.
How similar studies have gone: Previous imaging-guided CRT planning work showed promise, but this project seeks to enhance accuracy with richer strain data and AI.
Where this research is happening
-
University of Virginia
Charlottesville, United States Active
Researchers
- Principal investigator: Zhang, Miaomiao — University of Virginia
- Study coordinator: Zhang, Miaomiao
Related research
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-
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AI-supported heart failure diagnosis and management in Rwanda
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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.