Scientists create new technology to spot early Alzheimer’s with ’99 per cent accuracy rate’
ALZHEIMER’S can be predicted “with nearly 100 per cent accuracy” after a medical breakthrough, say researchers.
Experts have developed an algorithm that whizzes through dense amounts of data to predict possible sufferers of the brain condition.
Researchers developed a "deep learning-based" method in aid of patients with the disease, known for impairing memory and thinking skills.
This method can "predict the possible onset of Alzheimer’s disease from brain images with an accuracy of over 99 per cent", they said.
It was developed while analysing MRI images of the brains of 138 research participants.
The boffins were trying to tackle the problem of manually analysing "functional" MRI images - which measures brain activity by detecting changes associated with blood flow.
Trying to identify changes in the brain associated with Alzheimer’s not only requires specific knowledge but is also vastly time-consuming.
But applying 'deep learning' and other Artificial Intelligence methods can speed this up by a significant time frame, the experts said.
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Professor Rytis Maskeliunas, of Kaunas University of Technology, Lithuania, said: "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.
"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.
"We’re working with medical institutions to get more data."
He said the algorithm could be developed into software, which would analyse the collected data from vulnerable groups - those over 65, people with a history of brain injury, high blood pressure and other ailments.
One of the first possible signs of Alzheimer’s is mild cognitive impairment - which is the stage between the expected cognitive decline of normal ageing and dementia.
The earliest stages of this can often go unnoticed, but in quite a few cases can be detected by neuroimaging.
Doctors would be told about any anomalies detected, related to the early onset of Alzheimer’s.
Prof Maskeliunas said the model could also be integrated and used to analyse different parameters, for example, monitoring eye movements, face reading, or voice analysing.
He said: “Technologies can make medicine more accessible and cheaper.
"Although they will never truly replace the medical professional, technologies can encourage seeking timely diagnosis and help," added Maskeliunas.
According to the World Health Organisation, Alzheimer’s disease is the most frequent cause of dementia, contributing to up to 70 per cent of dementia cases.
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Worldwide, some 24million 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.
