Improving Stroke Treatment Decisions with AI
Official title: Predicting Tissue and Functional Outcome in Acute Stroke
Funded at Stanford University · NIH-11177055 · Data updated 2026-11-12
This project is developing an advanced computer system to help doctors make faster and better decisions for patients experiencing a sudden stroke.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project start | August 15, 2023 |
| Project end | July 31, 2028 |
| Funding year | 2025 |
| Award (this year) | $611,421 |
| NIH project number | 5R01NS130172-03 |
| Project ID | NIH-11177055 on NIH RePORTER |
Who could benefit from this research
✓Could help
This work is focused on acute ischemic stroke patients who would typically undergo initial MR and CT imaging.
✕Less likely to help
Patients who have not experienced an acute ischemic stroke or those not undergoing standard neuroimaging would not directly benefit from this specific AI tool.
What this research studies
When someone has a stroke, every minute counts, but current methods for deciding on the best treatment, like clot removal, are not always perfect. This project aims to create a new artificial intelligence (AI) system that can look at brain scans and other patient information to predict how a patient's brain tissue and overall function might recover. By training this AI with a lot of patient data, including different types of scans, we hope it can accurately estimate the best treatment path for each individual. The goal is to make these predictions quickly and safely, even without certain types of contrast imaging, and to ensure the system works well for diverse patient populations and different hospital settings.
Why it matters
Potential benefit: If successful, this AI system could lead to more precise and timely treatment decisions for stroke patients, potentially improving their recovery and reducing disability.
How similar studies have gone: Deep learning and AI have shown great promise in various medical applications, suggesting a strong foundation for this novel approach in stroke care.
Where this research is happening
-
Stanford University
Stanford, United States Active
Researchers
- Principal investigator: Zaharchuk, Gregory George — Stanford University
- Study coordinator: Zaharchuk, Gregory George
Related research
Other NIH-funded projects on the same topics.
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In-bed wearable rehab robot to help stroke survivors recover movement
A wearable robotic device will be developed to help adults who recently had an acute stroke regain movement while still in the hospital.
-
When people are most at risk of another stroke after a minor stroke or TIA
Researchers are combining past trial data to find the timing of highest risk for a second stroke after a minor stroke or TIA so treatments can be better timed.
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Improving Participation after Stroke: Self-Management Rehab for Sub-Acute Stroke
A doctor- and patient-friendly program delivered virtually helps people who recently had a stroke feel more confident, stay active at home, and improve quality of life.
-
Targeting a brain inflammation regulator to protect cells after a stroke
Aims to test a new approach that tampers with a key brain cell regulator to reduce inflammation and protect brain tissue after a stroke, potentially lowering disability.
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Predicting brain tissue outcome after acute stroke using AI
A researchers at Stanford are testing an AI system that uses brain scans and clinical data to predict which parts of the brain will recover after an acute stroke and how treatment might help.
-
Helping brain recovery for people with severe paralysis after a recent stroke
This project builds a wearable in-bed robot to give gentle movement and sensory training to adults with severe weakness right after a stroke.
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.