Using machine learning to predict brain injuries in patients on ECMO.
Official title: Predicting ECMO NeuroLogICal Injuries using mAchiNe Learning (PELICAN)
Funded at Ut Southwestern Medical Center · NIH-11116943 · Data updated 2026-11-12
This study is looking at how we can use computer technology to predict brain injuries in patients on a special life-support system called ECMO, so doctors can spot risks early and make changes to help keep patients safer and healthier.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Ut Southwestern Medical Center NIH-funded |
| Lab location | 1 site (Dallas, United States) |
| Project start | July 1, 2023 |
| Project end | May 31, 2028 |
| Funding year | 2025 |
| Award (this year) | $523,509 |
| NIH project number | 5R01NS133142-03 |
| Project ID | NIH-11116943 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include critically ill children and adults who are receiving ECMO support.
✕Less likely to help
Patients who are not undergoing ECMO treatment or those with pre-existing severe neurological conditions may not benefit from this research.
What this research studies
This research investigates how machine learning can be utilized to predict neurological injuries in patients receiving Extracorporeal Membrane Oxygenation (ECMO), a life-support technique for critically ill individuals. By analyzing various clinical data points and their dynamic interactions, the study aims to uncover patterns that may indicate the risk of brain injury before it occurs. The goal is to identify modifiable factors that healthcare providers can monitor and adjust to improve patient outcomes. This innovative approach seeks to enhance understanding of the mechanisms behind ECMO-related brain injuries and ultimately improve care for affected patients.
Why it matters
Potential benefit: If successful, this research could lead to better prediction and prevention of neurological injuries in patients undergoing ECMO, improving their long-term health outcomes.
How similar studies have gone: While machine learning applications in healthcare are growing, this specific approach to predicting ECMO-related brain injuries is novel and has not been extensively tested.
Where this research is happening
-
Ut Southwestern Medical Center
Dallas, United States Active
Researchers
- Principal investigator: Raman, Lakshmi — Ut Southwestern Medical Center
- Study coordinator: Raman, Lakshmi
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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.