Using AI to predict and classify heart failure with preserved ejection fraction
Official title: ECG-AI Based Prediction and Phenotyping of Heart Failure with Preserved Ejection Fraction
Funded at Wake Forest University Health Sciences · NIH-11099929 · Data updated 2026-11-12
This study is looking at how we can use artificial intelligence to better spot and treat heart failure with preserved ejection fraction (HFpEF) by analyzing heart rhythm data from simple ECG tests, so we can find different types of HFpEF and offer more personalized care for patients.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Wake Forest University Health Sciences NIH-funded |
| Lab location | 1 site (Winston-Salem, United States) |
| Project start | September 1, 2023 |
| Project end | May 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $581,981 |
| NIH project number | 5R01HL169451-03 |
| Project ID | NIH-11099929 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include adults over 21 years old who are at risk for or currently diagnosed with heart failure with preserved ejection fraction.
✕Less likely to help
Patients with heart failure with reduced ejection fraction or those under 21 years old may not benefit from this research.
What this research studies
This research focuses on improving the early detection and treatment of heart failure with preserved ejection fraction (HFpEF) using artificial intelligence (AI) applied to electrocardiogram (ECG) data. The study aims to identify different subtypes of HFpEF, which can lead to more personalized treatment options. By utilizing low-cost and accessible ECG data, the research seeks to screen large populations for HFpEF risk and assist in targeted therapeutic strategies. The approach combines deep phenotyping, multi-omics, and machine learning to enhance understanding and management of this complex condition.
Why it matters
Potential benefit: If successful, this research could lead to earlier diagnosis and more effective treatments for patients with heart failure, particularly those with preserved ejection fraction.
How similar studies have gone: Previous research has demonstrated success in using AI with ECG data to differentiate between heart failure types, indicating a promising approach for this study.
Where this research is happening
-
Wake Forest University Health Sciences
Winston-Salem, United States Active
Researchers
- Principal investigator: Akbilgic, Oguz — Wake Forest University Health Sciences
- Study coordinator: Akbilgic, Oguz
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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.