Using artificial intelligence to assess genetic risk for sudden cardiac arrest through ECG analysis
Official title: Artificial Intelligence for Determining Genetic Risk of Sudden Cardiac Arrest using the Electrocardiogram
Funded at Icahn School of Medicine at Mount Sinai · NIH-10998172 · Data updated 2026-11-12
This study is looking at how artificial intelligence can help doctors read heart tests (ECGs) to find out if someone has a genetic risk for sudden cardiac arrest, especially for people who might not have easy access to regular genetic testing.
Quick facts
| Grant type | Fellowship grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Icahn School of Medicine at Mount Sinai NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | September 30, 2024 |
| Project end | September 29, 2027 |
| Funding year | 2024 |
| Award (this year) | $49,474 |
| NIH project number | 1F30HL176177-01 |
| Project ID | NIH-10998172 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals with a family history of sudden cardiac arrest or those exhibiting symptoms of cardiac arrhythmias.
✕Less likely to help
Patients without any family history of cardiac issues or those who do not exhibit any symptoms related to cardiac arrhythmias may not benefit from this research.
What this research studies
This research investigates how artificial intelligence can analyze electrocardiograms (ECGs) to identify genetic risks associated with sudden cardiac arrest (SCA). By leveraging deep learning techniques, the study aims to uncover subtle waveform patterns in ECGs that may indicate underlying genetic predispositions to SCA. This approach seeks to provide a more accessible and cost-effective method for early detection of genetic risks, particularly for underrepresented populations who may face barriers to traditional genetic testing. The ultimate goal is to enhance the ability to predict SCA risk and improve patient outcomes through timely interventions.
Why it matters
Potential benefit: If successful, this research could lead to earlier identification of individuals at risk for sudden cardiac arrest, potentially saving lives through preventive measures.
How similar studies have gone: Previous research has shown promise in using artificial intelligence for ECG analysis, indicating that this approach could be a significant advancement in predicting cardiac risks.
Where this research is happening
-
Icahn School of Medicine at Mount Sinai
New York, United States Active
Researchers
- Principal investigator: Jiang, Joy — Icahn School of Medicine at Mount Sinai
- Study coordinator: Jiang, Joy
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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.