Using machine learning to analyze heart rhythms in patients with congenital heart disease over time
Official title: Machine Learning Based Analysis of Longitudinal Changes in the Congenital Heart Disease Electrocardiogram
Funded at Emory University · NIH-10977102 · Data updated 2026-11-12
This study is looking at how smart computer programs can help doctors understand heart health better by analyzing heart tests from patients with congenital heart disease, so they can catch any problems early and provide the best care possible.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Emory University NIH-funded |
| Lab location | 1 site (Atlanta, United States) |
| Project start | August 1, 2024 |
| Project end | July 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $117,375 |
| NIH project number | 1R21HL172209-01A1 |
| Project ID | NIH-10977102 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are adults with a history of congenital heart disease who are 21 years or older.
✕Less likely to help
Patients without congenital heart disease or those under 21 years old may not benefit from this research.
What this research studies
This research investigates how machine learning can be used to analyze electrocardiograms (ECGs) from patients with congenital heart disease to track changes in heart function over time. By examining ECG data, the study aims to identify subtle changes that could indicate worsening heart conditions, allowing for timely interventions. The approach involves transferring ECG data from local healthcare providers to specialized analysis centers, where advanced algorithms will assess the data for potential biomarkers of heart issues. This could lead to more personalized and effective monitoring of patients' heart health.
Why it matters
Potential benefit: If successful, this research could enable earlier detection of heart problems in congenital heart disease patients, potentially preventing severe complications.
How similar studies have gone: Other research has shown promise in using machine learning for ECG analysis, suggesting that this approach could be effective in monitoring heart conditions.
Where this research is happening
-
Emory University
Atlanta, United States Active
Researchers
- Principal investigator: Book, Wendy M. — Emory University
- Study coordinator: Book, Wendy M.
Related research
Other NIH-funded projects on the same topics.
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-
Multimodal AI to improve genetic diagnosis of congenital heart disease
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AI-powered echocardiography to spot common heart defects in newborns
A new AI tool analyzes infant heart ultrasound images to help non-specialist health workers identify five common, treatable heart defects early.
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.