VILNIUS (Dispatches) --
Researchers from Kaunas universities, Lithuania have 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.
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, according to World Health Organization
Although theoretically possible, manual analysing of functional magnetic resonance imaging ( 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 mild cognitive impairment (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.
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. In total, 51,443 and 27,310 images from The Alzheimer’s Disease Neuroimaging Initiative fMRI dataset were selected for training and validation.