What is it about?
Here we apply machine learning techniques over magnetic resonance images (MRIs) of the brain of healthy individuals to predict who is harboring abnormal amyloid levels. The method has been trained and tested on two independent cohorts using cerebrospinal fluid levels of amyloid as gold-standard. Predictive capacity is modest (AUC=0.76), but used as a pre-screening tool, it has a notable impact since can cut down to half the burden to detect healthy individuals at risk of Alzheimer's.
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Why is it important?
Healthy individuals harboring amyloid protein in the brain are at increased risk of developping Alzheimer's and could benefit from secondary preventive interventions. However, gold-standard techniques for amyloid are not suitable for screening the general population. Here, we present a method that, used as a pre-screening tool, cuts more than half the burden of innecessary tests to detect these individuals. This method comes at no-extra cost as it capitalizes brain scans that need to be acquired anyway for safety reasons.
Perspectives
This is a proof-of-concept that machine learning techniques applied on structural brain scans may be useful in real-world scenarios to detect individuals that could benefit from secondary preventive interventions for Alzheimer's. Performance is expected to improve when more complex machine learning / deep learning methods can be applied. To this end, more data is needed to robustly train the system. These kind of methods can pave the way to forster secondary preventive trials for Alzheimer's
Juan Domingo Gispert
Read the Original
This page is a summary of: MRI-Based Screening of Preclinical Alzheimer’s Disease for Prevention Clinical Trials, Journal of Alzheimer s Disease, July 2018, IOS Press,
DOI: 10.3233/jad-180299.
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