Improving heart disease detection using machine learning with fewer data requirements
Official title: Overcoming explainability and data availability barriers to broad application of ECG ML screening with a system-wide ECG dataset
Funded at University of Utah · NIH-11002330 · Data updated 2026-11-12
This study is working on using smart computer programs to better spot heart problems by analyzing heart rhythm readings, making it easier for doctors to understand and use in their everyday care for patients.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Utah NIH-funded |
| Lab location | 1 site (Salt Lake City, United States) |
| Project start | December 20, 2023 |
| Project end | November 30, 2025 |
| Funding year | 2025 |
| Award (this year) | $115,500 |
| NIH project number | 5R21HL172288-02 |
| Project ID | NIH-11002330 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals at risk for cardiovascular diseases, particularly those with limited access to extensive healthcare resources.
✕Less likely to help
Patients with well-established cardiovascular conditions who are already receiving specialized care may not benefit from this research.
What this research studies
This research focuses on enhancing the detection of cardiovascular diseases through machine learning (ML) techniques applied to electrocardiogram (ECG) signals. It aims to develop algorithms that can effectively learn from smaller datasets while maintaining accuracy, making it easier to implement in clinical settings. The project also seeks to improve the explainability of these algorithms, helping healthcare providers understand how the ML models make decisions. By leveraging existing ECG data, the research intends to create tools that can identify serious heart conditions in diverse populations.
Why it matters
Potential benefit: If successful, this research could lead to more accessible and accurate heart disease screening methods, ultimately improving patient outcomes.
How similar studies have gone: Previous research has shown promise in using machine learning for ECG analysis, indicating that this approach could lead to significant advancements in cardiovascular disease detection.
Where this research is happening
-
University of Utah
Salt Lake City, United States Active
Researchers
- Principal investigator: Tasdizen, Tolga — University of Utah
- Study coordinator: Tasdizen, Tolga
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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.