Using AI to detect heart activation patterns from MRI images
Official title: Deep Learning To Automate Late Mechanical Activation Detection From Cardiac Magnetic Resonance Images
Funded at University of Virginia · NIH-11091450 · Data updated 2026-11-12
This study is working on using smart computer technology to help find areas in the heart that aren't working properly in MRI images, which could make treatments for heart failure patients more effective.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Virginia NIH-funded |
| Lab location | 1 site (Charlottesville, United States) |
| Project start | May 1, 2023 |
| Project end | April 30, 2026 |
| Funding year | 2025 |
| Award (this year) | $188,736 |
| NIH project number | 5R21EB032597-03 |
| Project ID | NIH-11091450 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients with heart failure who are undergoing cardiac resynchronization therapy.
✕Less likely to help
Patients without heart failure or those not undergoing cardiac resynchronization therapy may not benefit from this research.
What this research studies
This research focuses on developing advanced machine learning and artificial intelligence techniques to identify late mechanical activation (LMA) in cardiac MRI images. By analyzing standard cine cardiac magnetic resonance images, the project aims to automate the detection of LMA sites and compute circumferential uniformity estimates. The goal is to enhance the guidance of device-based therapies, such as cardiac resynchronization therapy, for heart failure patients. The research utilizes a unique dataset from over 200 patients, incorporating various health metrics and imaging data to improve accuracy and predict patient outcomes.
Why it matters
Potential benefit: If successful, this research could lead to more accurate and timely identification of heart activation issues, improving treatment outcomes for heart failure patients.
How similar studies have gone: Other research has shown promise in using machine learning techniques for medical imaging, indicating potential success for this approach.
Where this research is happening
-
University of Virginia
Charlottesville, United States Active
Researchers
- Principal investigator: Zhang, Miaomiao — University of Virginia
- Study coordinator: Zhang, Miaomiao
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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.