AI tracking of ECG changes in adults born with heart defects
Official title: Machine Learning Based Analysis of Longitudinal Changes in the Congenital Heart Disease Electrocardiogram
Funded at Emory University · NIH-11141170 · Data updated 2026-11-12
This project uses artificial intelligence to spot gradual changes in ECGs of adults born with heart defects so worsening heart problems can be caught earlier.
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, 2027 |
| Funding year | 2025 |
| Award (this year) | $117,375 |
| NIH project number | 5R21HL172209-02 |
| Project ID | NIH-11141170 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults (21+) born with congenital heart defects who can share past ECGs and agree to ongoing ECG monitoring or remote ECG transfer would be ideal candidates.
✕Less likely to help
People without congenital heart disease, children under 21, or those who cannot provide serial ECG data are unlikely to benefit from this work.
What this research studies
You would contribute routine ECG recordings that the team will analyze over time using machine learning and time-series methods to find subtle changes in heart electrical signals. The researchers will look at waveforms, conduction intervals, and other ECG features and track how they change by age, sex, race, and ethnicity in adults with congenital heart disease. The goal is to identify ECG-based biomarkers that signal early chamber dysfunction or fibrosis before symptoms or expensive imaging show problems. If patterns emerge, the approach could allow remote transfer of ECGs for centralized analysis to help guide when interventions are needed.
Why it matters
Potential benefit: If successful, this could give adults with congenital heart defects earlier, clearer warnings of worsening heart function so interventions can be timed to prevent complications and reduce unnecessary imaging.
How similar studies have gone: AI has already shown promise detecting several heart problems from single ECGs, but using longitudinal, time-series AI specifically for adult congenital heart disease is largely new.
Where this research is happening
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Emory University
Atlanta, United States Active
Researchers
- Principal investigator: Book, Wendy M. — Emory University
- Study coordinator: Book, Wendy M.
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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.