Predicting brain tissue outcome after acute stroke using AI
Official title: Predicting Tissue and Functional Outcome in Acute Stroke
Funded at Stanford University · NIH-11378453 · Data updated 2026-11-12
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.
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 | 2026 |
| Award (this year) | $559,833 |
| NIH project number | 5R01NS130172-04 |
| Project ID | NIH-11378453 on NIH RePORTER |
Who could benefit from this research
✓Could help
Patients with acute ischemic stroke who undergo standard imaging (MRI/CT) and are candidates for reperfusion therapies.
✕Less likely to help
Patients with strokes outside the acquisition window or those not eligible for reperfusion therapy may not benefit.
What this research studies
This project will develop deep learning models that analyze initial MR and CT brain images along with clinical information to predict final tissue and functional outcomes under different treatment scenarios. The team will train convolutional neural networks to estimate how much benefit a patient could get from reperfusion therapies, and they will explore models that do not require contrast perfusion imaging to improve safety and reduce costs. They will test generalizability and explainability of the AI tools in external datasets from different populations and scanners, including mobile CT data. The ultimate aim is to guide triage decisions and design more precise clinical trials for stroke treatment.
Why it matters
Potential benefit: If successful, the AI system could help doctors personalize stroke treatment decisions and speed up the path to recovery for patients.
How similar studies have gone: Prior studies show promise for AI in imaging-based stroke prediction, but this project seeks broader generalizability and safety without contrast imaging.
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.
-
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.
-
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.
-
Language recovery after stroke: how people regain communication skills and how to help
A Johns Hopkins team is studying how people recover language after a left-hemisphere stroke and what helps them regain reading, spelling, and understanding spoken and written language.
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.