Predicting early Alzheimer's disease using advanced data analysis techniques
Early Alzheimers Forecasting from Multimodal Data via Deep Transfer Learning, Evaluated on a Large-Scale Prospective Cohort Study
This study is looking for new ways to spot Alzheimer's disease early by using different types of information, like genetics and brain scans, to help create smart tools that can predict how the disease might progress, making it easier for everyone to get the right care sooner.
Quick facts
| Grant type | R03 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Massachusetts Amherst NIH-funded |
| Lab location | 1 site (Hadley, United States) |
| Project ID | NIH-10732306 on NIH RePORTER |
What this research studies
This research aims to develop innovative methods for the early detection of Alzheimer's disease by analyzing various types of data, including genetic information, brain MRIs, and cognitive assessments. By utilizing deep transfer learning techniques, the project seeks to create models that can accurately predict the progression of Alzheimer's disease without needing extensive expert input on MRI scans. The study will evaluate these models on a large, diverse group of participants to ensure the findings are applicable to the general population. This approach could significantly enhance the ability to identify Alzheimer's disease in its early stages, potentially leading to better management and treatment options.
Who could benefit from this research
Good fit: Ideal candidates for this research include individuals at risk for Alzheimer's disease, particularly those with a family history or genetic predisposition.
Not a fit: Patients with advanced Alzheimer's disease or those who do not have any risk factors for the disease may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to earlier and more accurate detection of Alzheimer's disease, allowing for timely interventions and improved patient outcomes.
How similar studies have performed: Previous research has shown promise in using multimodal data and machine learning techniques for predicting Alzheimer's disease, indicating that this approach could be effective.
Where this research is happening
Hadley, United States
- University of Massachusetts Amherst — Hadley, United States (Active)
Researchers
- Principal investigator: Fiterau Brostean, Madalina — University of Massachusetts Amherst
- Study coordinator: Fiterau Brostean, Madalina
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 below.