Machine learning tool 99% accurate at spotting early signs of Alzheimer’s in the lab

Researchers at the Kaunas Universities in Lithuania have developed an algorithm that can predict the risk of someone developing Alzheimer’s disease from brain images with over 99% accuracy.

Image credits Nevit Dilmen via Wikimedia.

Alzheimer’s is the world’s leading cause of dementia, according to the World Health Organization, causing or contributing to an estimated 70% of cases. As living standards improve and the average age of global populations increase, it is very likely that the number of dementia cases will increase greatly in the future, as the condition is highly correlated with age.

However, since the early stages of dementia have almost no clear, accepted symptoms, the condition is almost always identified in its latter stages, where intervention options are limited. The team from Kaunas hopes that their work will help protect people from dementia by allowing doctors to identify those at risk much earlier.

Finding our early

“Medical professionals all over the world attempt to raise awareness of an early Alzheimer’s diagnosis, which provides the affected with a better chance of benefiting from treatment. This was one of the most important issues for choosing a topic for Modupe Odusami, a Ph.D. student from Nigeria,” says Rytis Maskeliūnas, a researcher at the Department of Multimedia Engineering, Faculty of Informatics, Kaunas University of Technology (KTU), Odusami’s Ph.D. supervisor.

One possible 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 well.

While possible to detect early-onset Alzheimer’s this way, however, the authors explain that manually identifying MCI in these images is extremely time-consuming and requires highly specific knowledge, meaning any implementation would be prohibitively expensive and could only handle a tiny amount of cases.

“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. 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,” says Maskeliūnas, who supervised the team working on the model.

The model was trained on fMRI images from 138 subjects from The Alzheimer’s Disease Neuroimaging Initiative fMRI dataset. It was asked to separate 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. The authors report that it 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. The team cautions that “such high numbers are not indicators of true real-life performance”, but the results are still encouraging, and they are working to improve their algorithm with more data.

Their end goal is to turn this algorithm into a portable, easy-to-use software — perhaps even an app.

“Technologies can make medicine more accessible and cheaper. Although they will never (or at least not soon) truly replace the medical professional, technologies can encourage seeking timely diagnosis and help,” says Maskeliūnas.

The paper “Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network” has been published in the journal Diagnostics.

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