AI Article Synopsis
- Identifying healthy individuals with amyloid pathology is crucial for secondary prevention in Alzheimer's disease (AD) clinical trials, highlighting the need for noninvasive detection methods.
- Researchers applied machine learning to MRI scans of 96 cognitively normal subjects to identify those who are amyloid-positive, using a model trained on publicly available data.
- The proposed approach can significantly cut costs and reduce the need for invasive testing by 60%, enhancing recruitment strategies for prevention trials and potentially aiding in the development of secondary prevention methods for AD.
Article Abstract
The identification of healthy individuals harboring amyloid pathology represents one important challenge for secondary prevention clinical trials in Alzheimer's disease (AD). Consequently, noninvasive and cost-efficient techniques to detect preclinical AD constitute an unmet need of critical importance. In this manuscript, we apply machine learning to structural MRI (T1 and DTI) of 96 cognitively normal subjects to identify amyloid-positive ones. Models were trained on public ADNI data and validated on an independent local cohort. Used for subject classification in a simulated clinical trial setting, the proposed method is able to save 60% of unnecessary CSF/PET tests and to reduce 47% of the cost of recruitment. This recruitment strategy capitalizes on available MR scans to reduce the overall amount of invasive PET/CSF tests in prevention trials, demonstrating a potential value as a tool for preclinical AD screening. This protocol could foster the development of secondary prevention strategies for AD.
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| http://dx.doi.org/10.3233/JAD-180299 | DOI Listing |
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