Algorithm can predict Alzheimer’s with nearly 100 per cent accuracy

Dementia cases are set to double by 2050 with a growing elderly population. Image: Shutterstock

Researchers from Kaunas universities in Lithuania developed a deep learning-based method that can predict the possible onset of Alzheimer’s disease from brain images with an accuracy of over 99 per cent.

The method was developed while analysing functional MRI images obtained from 138 subjects and performed better in terms of accuracy, sensitivity and specificity than previously developed methods.

According to World Health Organisation, Alzheimer’s disease is the most frequent cause of dementia, contributing to up to 70 per cent of dementia cases. Worldwide, approximately 24 million people are affected, and this number is expected to double every 20 years. Owing to societal ageing, the disease will become a costly public health burden in the years to come.

One of the possible Alzheimer’s first signs is mild cognitive impairment (MCI), which is the stage between the expected cognitive decline of normal ageing and dementia. Based on the previous research, functional magnetic resonance imaging (fMRI) can be used to identify the regions in the brain which can be associated with the onset of Alzheimer’s disease, according to Rytis Maskeliūnas, a researcher at the Department of Multimedia Engineering.

The earliest stages of MCI often have almost no clear symptoms, but in quite a few cases can be detected by neuroimaging.

However, although theoretically possible, manual analysing of fMRI images attempting to identify the changes associated with Alzheimer’s not only requires specific knowledge but is also time-consuming – application of Deep learning and other AI methods can speed this up by a significant time margin. Finding MCI features does not necessarily mean the presence of illness, as it can also be a symptom of other related diseases, but it is more of an indicator and possible helper to steer toward an evaluation by a medical professional.

“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-per cent. 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,” said Maskeliūnas, who supervised the team working on the model.

The deep learning-based model was developed as a fruitful collaboration of leading Lithuanian researchers in the Artificial Intelligence sector, using a modification of well-known fine-tuned ResNet 18 (residual neural network) to classify functional MRI images obtained from 138 subjects. The images fell into six different categories: from healthy through the spectre of mild cognitive impairment (MCI) to Alzheimer’s disease. In total, 51,443 and 27,310 images from The Alzheimer’s Disease Neuroimaging Initiative fMRI dataset were selected for training and validation.

Technologies can make medicine more accessible and cheaper. Although they will never replace the medical professional, technologies can encourage seeking timely diagnosis and help.

“Although this was not the first attempt to diagnose the early onset of Alzheimer’s from similar data, our main breakthrough is the accuracy of the algorithm. Obviously, such high numbers are not indicators of true real-life performance, but we’re working with medical institutions to get more data,” says Maskeliūnas.

The algorithm could be developed into software, which would analyse the collected data from vulnerable groups – those over 65, having a history of brain injury, high blood pressure – and notify the medical personnel about the anomalies related to the early onset of Alzheimer’s.

“We need to make the most of data,” added Maskeliūnas, “that’s why our research group focuses on the European open science principle, so anyone can use our knowledge and develop it further. I believe that this principle contributes greatly to societal advancement.”


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Deirdre Tynan

Deirdre Tynan is an award-winning journalist who enjoys bringing the best in news reporting to Spain’s largest English-language newspaper, Euro Weekly News. She has previously worked at The Mirror, Ireland on Sunday and for news agencies, media outlets and international organisations in America, Europe and Asia. A huge fan of British politics and newspapers, Deirdre is equally fascinated by the political scene in Madrid and Sevilla. She moved to Spain in 2018 and is based in Jaen.

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