Using deep learning to find early signs of Alzheimer's disease
Official title: Deep Learning for Detecting the Early Anatomical Effects of Alzheimer's Disease
Funded at Massachusetts General Hospital · NIH-10846724 · Data updated 2026-11-12
This study is looking at how to better spot early signs of Alzheimer's disease by using advanced computer technology to analyze MRI scans of the brain, which could help doctors diagnose and plan treatment more effectively.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Massachusetts General Hospital NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project start | June 1, 2023 |
| Project end | February 28, 2026 |
| Funding year | 2024 |
| Award (this year) | $250,500 |
| NIH project number | 5R21AG082082-02 |
| Project ID | NIH-10846724 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals at risk for Alzheimer's disease or those experiencing early symptoms of cognitive decline.
✕Less likely to help
Patients with advanced Alzheimer's disease or those who do not have any cognitive impairment may not benefit from this research.
What this research studies
This research focuses on improving the detection of early anatomical changes associated with Alzheimer's disease using advanced deep learning techniques. By analyzing MRI scans over time, the study aims to enhance the accuracy and efficiency of identifying subtle changes in brain structure that may indicate the onset of Alzheimer's. The approach seeks to minimize noise in the imaging data and ensure that each scan is evaluated fairly, which could lead to better understanding and monitoring of disease progression. Ultimately, this research aims to develop tools that can be used directly in clinical settings to aid in early diagnosis and treatment planning.
Why it matters
Potential benefit: If successful, this research could lead to earlier and more accurate diagnoses of Alzheimer's disease, allowing for timely interventions.
How similar studies have gone: Other research has shown promise in using deep learning for medical imaging, indicating that this approach could be effective in detecting early signs of Alzheimer's disease.
Where this research is happening
-
Massachusetts General Hospital
Boston, United States Active
Researchers
- Principal investigator: Fischl, Bruce — Massachusetts General Hospital
- Study coordinator: Fischl, Bruce
Related research
Other NIH-funded projects on the same topics.
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AIM-AI: an actionable, integrated, multiscale genetic map of Alzheimer’s disease powered by deep learning
A research project uses advanced AI to connect genetic data, biology, and brain scans to better understand Alzheimer’s disease and its risk factors.
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Deep Learning Retina Tool to Find Early Alzheimer’s
A new AI-based retinal imaging tool aims to help identify people at risk for Alzheimer’s before symptoms appear using a quick, noninvasive eye scan.
-
Interpretable deep learning to map genetics and epigenetics of Alzheimer's at single-cell level
A new project will use advanced computer models to understand how genetic and epigenetic changes drive Alzheimer's disease at the level of individual brain cells.
-
Deep learning to uncover neurovascular disruptions in Alzheimer's disease
A neuron-and-blood-vessel focused study uses advanced computer methods to find molecular changes in brain blood vessels of people with Alzheimer’s disease.
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Global shared-AI network to improve Alzheimer's detection and prediction
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.
-
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.