Unsupervised machine-learning approach shows potential for diagnosing Alzheimer’s disease

Researchers have developed a new method combining statistical and unsupervised machine-learning approaches that could be used to differentiate between magnetic resonance imaging (MRI) scans from people with Alzheimer’s disease (AD) and cognitively unimpaired individuals.
The method was tested on brain structural MRI scans from 198 people with AD and 231 controls, and had an accuracy of 84% for discriminating between the two groups.
The study authors highlight that existing algorithms for AD diagnosis use a supervised learning approach that requires a large volume of labelled MRI scans, which is difficult to do, but their new method only requires a limited number of labelled scans.