Depression, which is often difficult to diagnose accurately, can now be detected by brain scans. Russian scientists have developed an algorithm that can distinguish patients with clinical depression from healthy people using MRI data with up to 86% accuracy. The development was reported by the press service of the Russian Science Foundation.
According to Semyon Kurkin, Chief Researcher at the Research Institute of Applied Artificial Intelligence and Digital Solutions at Plekhanov Russian University of Economics, a tool has been created for the early diagnosis of depression – one of the most common causes of disability.
The method allows for the detection of hidden abnormalities in the interaction of large brain networks that are not identified by traditional diagnostic methods.
Major depressive disorder is accompanied by changes in the functioning of certain brain areas. These changes can be seen on MRI or with functional diagnostics, but previously they were difficult to interpret accurately.
The new approach uses two machine learning technologies simultaneously, which analyze the same MRI scans. The first studies how brain neural networks are structured and which of them are most strongly associated with changes in human behavior. The second is a contrastive approach that compares these features in patients with depression and in other people.
In combination, these methods allow for the detection of small but clinically significant changes in brain structure. This is what provides up to 86% accuracy, which is significantly higher than most existing algorithms, which usually show about 50% or less.
According to the researchers, this approach opens the way to more personalized treatment and can improve the quality of life of patients, as well as reduce the socio-economic burden of the disease.
In the future, scientists plan to apply this method to other diseases as well – for example, schizophrenia and bipolar disorder.
According to the World Health Organization, more than 330 million people worldwide suffer from depression. At the same time, a significant proportion of patients are young people aged 15 to 29.