Early and accurate detection of Alzheimer's in Veterans using machine learning and simple tests
Official title: Combining Machine Learning Methods and Scalable In Vivo Biomarkers for the Early and Accurate Detection of Alzheimer's Disease in Veterans
Funded at VA Boston Health Care System · NIH-11251156 · Data updated 2026-11-12
Researchers are developing a quick, affordable way to detect Alzheimer's disease in Veterans using computer analysis of existing cognitive tests and digital data from a clock drawing test.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | VA Boston Health Care System NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project start | January 1, 2025 |
| Project end | December 31, 2029 |
| Funding year | 2027 |
| NIH project number | 5IK2CX002625-02 |
| Project ID | NIH-11251156 on NIH RePORTER |
Who could benefit from this research
✓Could help
Veterans who have or are at risk for Alzheimer’s disease and related disorders and who have undergone routine clinical neuropsychological testing or clock drawing test data.
✕Less likely to help
Patients without risk factors for Alzheimer’s disease or those without access to neuropsychological testing data may not benefit from this approach.
What this research studies
This project aims to create a scalable tool that combines machine learning with non-invasive data from routine neuropsychological testing and a digital version of the clock drawing test to identify Alzheimer's pathology in Veterans. It leverages archival data from VA clinics and hopes to make detection possible without expensive imaging or lumbar punctures. The goal is to give Veterans earlier access to care and treatment options by providing a convenient, cost-effective screening method. The study will use non-proprietary methods and data from a diverse Veteran population to develop predictive models for amyloid positivity. If successful, this approach could be implemented broadly across VA facilities to improve early diagnosis.
Why it matters
Potential benefit: If successful, Veterans could receive earlier diagnosis and quicker access to treatments and services.
How similar studies have gone: Previous studies show promise for machine learning using cognitive data, but this project is novel in applying non-proprietary methods to a Veteran population with scalable digital phenotyping.
Where this research is happening
-
VA Boston Health Care System
Boston, United States Active
Researchers
- Principal investigator: Frank, Brandon Edward — VA Boston Health Care System
- Study coordinator: Frank, Brandon Edward
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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.