Using deep learning to improve assessment of right heart function and its impact on health outcomes
Official title: Deep Learning Assessment of the Right Ventricle: Function, Etiology, and Prognosis
Funded at Kaiser Foundation Research Institute · NIH-11292559 · Data updated 2026-11-12
This study is looking at how new technology can help doctors get a better look at the right side of the heart using ultrasound images, which could lead to more accurate information about heart health and improve care for people with heart failure.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Kaiser Foundation Research Institute NIH-funded |
| Lab location | 1 site (Oakland, UNITED STATES) |
| Project start | April 15, 2025 |
| Project end | January 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $222,739 |
| NIH project number | 7R00HL157421-05 |
| Project ID | NIH-11292559 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals with known or suspected right ventricular dysfunction or heart failure.
✕Less likely to help
Patients with no cardiovascular issues or those who do not undergo echocardiographic imaging may not benefit from this research.
What this research studies
This research focuses on enhancing the evaluation of right ventricular (RV) function using advanced deep learning techniques applied to echocardiographic imaging. By analyzing video-based images, the study aims to provide more accurate assessments of RV morphology and function, which are crucial for understanding heart failure outcomes. The methodology involves developing segmentation models that can classify RV imaging phenotypes and predict cardiovascular risks based on patient characteristics. This innovative approach seeks to overcome current limitations in RV imaging and interpretation.
Why it matters
Potential benefit: If successful, this research could lead to more precise diagnoses and better management of heart failure, ultimately improving patient outcomes.
How similar studies have gone: Other research has shown promising results using deep learning for cardiac imaging, indicating potential for success in this novel approach.
Where this research is happening
-
Kaiser Foundation Research Institute
Oakland, United States Active
Researchers
- Principal investigator: Ouyang, David — Kaiser Foundation Research Institute
- Study coordinator: Ouyang, David
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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.