Using AI to improve understanding and diagnosis of Alzheimer's disease
Official title: Federated Deep Learning to Accelerate Alzheimer's Disease Research
Funded at University of Southern California · NIH-10827337 · Data updated 2026-11-12
This study is bringing together researchers from around the world to use smart technology and big data to better understand Alzheimer's disease, helping to find out what causes it and how it progresses, so we can improve diagnosis and care for patients.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Southern California NIH-funded |
| Lab location | 1 site (Los Angeles, UNITED STATES) |
| Project start | September 30, 2024 |
| Project end | August 31, 2029 |
| Funding year | 2024 |
| Award (this year) | $661,016 |
| NIH project number | 1R01AG081571-01A1 |
| Project ID | NIH-10827337 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals at risk for Alzheimer's disease, those with mild cognitive impairment, and patients diagnosed with Alzheimer's or related dementias.
✕Less likely to help
Patients with other forms of dementia unrelated to Alzheimer's disease may not receive direct benefits from this research.
What this research studies
This research aims to create an international alliance called 'Federate AD' that utilizes advanced artificial intelligence techniques and distributed computing to tackle critical challenges in Alzheimer's disease (AD) research. By analyzing large datasets from various biobanks around the world, the project seeks to identify genetic and environmental factors that influence the onset and progression of dementia. The approach involves using deep learning algorithms on neuroimaging data to enhance diagnosis and predict clinical decline in patients. This innovative platform will enable collaboration among researchers globally, enhancing the power of data analysis in understanding Alzheimer's disease.
Why it matters
Potential benefit: If successful, this research could lead to improved diagnostic methods and better understanding of Alzheimer's disease, ultimately benefiting patients through earlier detection and tailored treatment strategies.
How similar studies have gone: Previous research utilizing AI and large-scale data analysis has shown promise in improving diagnostic accuracy and understanding of Alzheimer's disease, indicating that this approach could be effective.
Where this research is happening
-
University of Southern California
Los Angeles, United States Active
Researchers
- Principal investigator: Thompson, Paul M — University of Southern California
- Study coordinator: Thompson, Paul M
Related research
Other NIH-funded projects on the same topics.
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Interpretable deep learning to map genetics and epigenetics of Alzheimer's at single-cell level
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This project builds a secure, shared AI system that learns from brain scans and medical data from people worldwide to help spot and predict Alzheimer's for patients and those at risk.
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AI-built genetic map to understand Alzheimer's disease
Using artificial intelligence to combine genetic, molecular, and brain imaging data to find genetic clues that could help people with or at risk for Alzheimer's disease.
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.