Using AI to predict cognitive impairment after a stroke
Official title: Using AI on Routine Clinical and Imaging Data from Acute Stroke Encounter to Predict Post-Stroke Vascular Contributions to Cognitive Impairment (AI - RESPECT)
Funded at Emory University · NIH-10985212 · Data updated 2026-11-12
This study is looking at how artificial intelligence can help doctors understand which stroke patients might have trouble with thinking and memory after their stroke, so they can get the right support to recover better.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Emory University NIH-funded |
| Lab location | 1 site (Atlanta, United States) |
| Project start | September 1, 2024 |
| Project end | July 31, 2027 |
| Funding year | 2024 |
| Award (this year) | $2,340,263 |
| NIH project number | 1RF1NS139325-01 |
| Project ID | NIH-10985212 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals who have recently experienced a stroke and may be at risk for cognitive impairment.
✕Less likely to help
Patients who have not had a stroke or those with pre-existing severe cognitive impairments unrelated to stroke may not benefit from this research.
What this research studies
This research investigates how artificial intelligence can analyze routine clinical and imaging data from patients who have experienced a stroke to predict the risk of post-stroke cognitive impairment (PSCI). By utilizing electronic health records and neuroimaging data, the study aims to identify patients at high risk for cognitive issues that may affect their recovery and adherence to treatment. The approach includes both retrospective data analysis and prospective cognitive testing to ensure accurate identification of PSCI. This could lead to more tailored recovery strategies for stroke patients.
Why it matters
Potential benefit: If successful, this research could lead to improved identification and management of cognitive impairments in stroke patients, enhancing their recovery and quality of life.
How similar studies have gone: Other research has shown promise in using machine learning approaches to predict cognitive outcomes in various patient populations, indicating that this method could be effective.
Where this research is happening
-
Emory University
Atlanta, United States Active
Researchers
- Principal investigator: Hu, Xiao — Emory University
- Study coordinator: Hu, Xiao
Related research
Other NIH-funded projects on the same topics.
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AI to Predict Memory and Thinking Problems After Stroke
This project uses artificial intelligence to help doctors understand which stroke patients might develop memory and thinking problems, so they can receive better care.
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Pericyte signaling in diabetic stroke-related cognitive problems
A patient-facing study exploring how blood vessel cells and inflammation after stroke interact in people with diabetes to influence cognitive decline and dementia.
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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.
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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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Understanding and reducing cognitive problems after stroke
A study from the University of Michigan aims to see if simple cognitive activities and sleep improvements can lower the risk of developing vascular cognitive impairment and dementia after stroke.
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Post-stroke cognitive decline and dementia: understanding and preventing PSCID
A large NIH project will use real-world data from two million stroke records to better understand how many people develop PSCID and what factors raise that risk, with the goal of guiding prevention and care.
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.