Cross-D-domain AI method to predict major cardiovascular events
Official title: SCH: Transfer Regression to Enable Cross-Domain Cardiovascular Event Prediction
Funded at Case Western Reserve University · NIH-11417269 · Data updated 2026-11-12
A precision AI tool will use transfer learning to predict major cardiovascular events like stroke or heart attack using simple, low-cost screening data.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Case Western Reserve University NIH-funded |
| Lab location | 1 site (Cleveland, United States) |
| Project start | September 1, 2024 |
| Project end | August 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $296,788 |
| NIH project number | 5R01HL177813-03 |
| Project ID | NIH-11417269 on NIH RePORTER |
Who could benefit from this research
✓Could help
Individuals at risk for ASCVD or with a concern for cardiovascular event risk who may undergo routine screening.
✕Less likely to help
Patients without access to the types of screening data used or those not at risk for cardiovascular events may not benefit.
What this research studies
This project develops a new transfer regression method to create a standardized, fair, and user-friendly risk predictor for major cardiovascular events. It will combine advanced machine learning and data science with biomedical signals from routine, low-radiation screenings. The model will be trained and tested on four large, diverse datasets to ensure robustness and applicability in real-world clinics. The goal is a practical tool that clinicians can use to guide preventive care without requiring expensive or invasive tests.
Why it matters
Potential benefit: If successful, patients could receive earlier and more accurate risk predictions guiding preventive treatment to reduce heart attacks, strokes, and related deaths.
How similar studies have gone: Previous work shows promise for AI-based risk prediction, but this project emphasizes transfer learning for cross-domain data and fairness, representing a novel approach with potential for stronger generalization.
Where this research is happening
-
Case Western Reserve University
Cleveland, United States Active
Researchers
- Principal investigator: Li, Shuo — Case Western Reserve University
- Study coordinator: Li, Shuo
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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.