The World Health Organization (WHO) states that Alzheimer’s disease (Alzheimer’s) is the world’s leading cause of dementia, causing or contributing to an estimated 70% of cases and counting. Noting this, researchers at the Kaunas University of Technology (KTU) in Lithuania have developed an advanced system that can predict the risk of someone developing Alzheimer’s from brain images with almost 100% accuracy.

An early sign of Alzheimer’s is mild cognitive impairment (MCI), a middle ground between the decline we could reasonably expect to see naturally as we age, and dementia. Previous research has shown that functional magnetic resonance imaging (fMRI) can identify areas of the brain where MCI is ongoing, although not all cases can be detected in this way. At the same time, finding physical features associated with MCI in the brain doesn’t necessarily prove illness, but is more of a strong indicator that something is not working as it normally should.

While possible to detect early-onset Alzheimer’s this way, however, manually identifying MCI in these images is a lengthy process, requires highly specific knowledge, and could still be subject to error.

So, the team at Kaunas designed a special algorithm trained on fMRI images from 138 subjects from The Alzheimer’s Disease Neuroimaging Initiative fMRI dataset; it was tasked with separating these images into six categories, ranging across the spectrum from healthy through to full-onset Alzheimer’s. Several tens of thousands of images were selected for training and validation purposes.

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At the end, the system was able to correctly identify MCI features in this dataset, achieving accuracies between 99.95% and 99.99% for different subsets of the data. While this is not the first automated system meant to identify early onset of Alzheimer’s from this type of data, the accuracy of this system is nothing short of impressive.

“Modern signal processing allows delegating the image processing to the machine, which can complete it faster and accurately enough. Of course, we don’t dare to suggest that a medical professional should ever rely on any algorithm one hundred percent,” said Rytis Maskeliūnas, a researcher at the Department of Multimedia Engineering, Faculty of Informatics, KTU.

“Think of a machine as a robot capable of doing the most tedious task of sorting the data and searching for features. In this scenario, after the computer algorithm selects potentially affected cases, the specialist can look into them more closely, and at the end, everybody benefits as the diagnosis and the treatment reaches the patient much faster,” Maskeliūnas added.

The team is working to improve their algorithm with more data and turn it into a portable, easy-to-use software — perhaps even an app.