Using deep learning to improve risk estimation for atrial fibrillation
Official title: Electrocardiogram-based deep learning and decision analysis to improve atrial fibrillation risk estimation
Funded at Massachusetts General Hospital · NIH-10915638 · Data updated 2026-11-12
This study is looking at a new way to better spot atrial fibrillation, a heart condition that can cause serious problems like strokes, by using smart technology on mobile heart monitors, so we can help more people at risk get the care they need.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Massachusetts General Hospital NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project start | August 15, 2023 |
| Project end | July 31, 2028 |
| Funding year | 2024 |
| Award (this year) | $195,810 |
| NIH project number | 5K23HL169839-02 |
| Project ID | NIH-10915638 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals who are at risk for atrial fibrillation, particularly those with risk factors such as age, obesity, or a history of heart disease.
✕Less likely to help
Patients who are already diagnosed with atrial fibrillation or those without any risk factors for the condition may not benefit from this research.
What this research studies
This research focuses on enhancing the detection of atrial fibrillation (AF), a common heart condition that can lead to serious complications like strokes. By utilizing advanced deep learning techniques applied to mobile electrocardiograms, the project aims to identify individuals at higher risk for AF more effectively. This approach seeks to streamline screening processes, making them more efficient and targeted, ultimately leading to better preventive care. The research also includes a cost-effectiveness analysis to compare this new method against current screening practices.
Why it matters
Potential benefit: If successful, this research could lead to earlier and more accurate identification of individuals at risk for atrial fibrillation, potentially reducing the incidence of strokes and other complications.
How similar studies have gone: Previous research has shown promise in using machine learning techniques for cardiovascular risk assessment, indicating that this approach may yield successful outcomes.
Where this research is happening
-
Massachusetts General Hospital
Boston, United States Active
Researchers
- Principal investigator: Khurshid, Shaan — Massachusetts General Hospital
- Study coordinator: Khurshid, Shaan
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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.