A method based on deep learning that can predict the possible onset of Alzheimer’s disease from brain images with an accuracy of more than 99 percent has been developed by researchers from the universities of Kaunas, Lithuania.
The method was developed by analyzing functional magnetic resonance images obtained from 138 subjects.
Mild cognitive impairment or MCI
One of the first possible signs of Alzheimer’s disease is mild cognitive impairment (MCI), which is the stage between the expected cognitive decline of normal aging and dementia.
Based on previous research, functional magnetic resonance imaging (fMRI) can be used to identify brain regions that may be associated with the onset of Alzheimer’s disease. The early stages of MCI often have almost no clear symptoms, but in many cases they can be detected by neuroimaging .
However, although theoretically possible, the manual analysis of functional magnetic resonance images that attempt to identify the changes associated with Alzheimer’s disease requires not only specific knowledge, but is also time-consuming: the application of deep learning and other methods of artificial intelligence can accelerate this by a significant amount of time .
Finding characteristics of MCI does not necessarily mean the presence of a disease, as it can also be a symptom of other related diseases, but it is more of an indicator and a possible aid in guiding towards an evaluation by a medical professional. According to the lead author of the study:
Modern signal processing makes it possible to delegate image processing to the machine, which can complete it quickly and accurately enough. Of course, we dare not suggest that a medical professional should trust an algorithm one hundred percent. Think of a machine as a robot capable of the most tedious task of sorting data and looking for features. In this scenario, after the computer algorithm selects the potentially affected cases, the specialist can examine them more closely, and ultimately everyone benefits as diagnosis and treatment reach the patient much faster.