AI can identify clinically anxious youth based on brain structure: Study

The research, published in the journal Nature Mental Health, involved about 3,500 youth between 10 and 25 years old from across the globe.

The researchers used machine learning a type of AI that help machines learn and improve from data analysis without explicit programming looked at cortical thickness and surface area, along with volumes of deep-lying brain regions.

To improve the results, the algorithms must be further refined and other types of brain data, such as brain function and connections, must be added, they said.

These initial results tend to hold are generalisable in such a diverse group of youngsters in terms of ethnicity, geographical location and clinical characteristics, the researchers said.

This renders the study outcomes rather fascinating, they said.

According to lead researcher Moji Aghajani, Assistant Professor at Leiden University in Netherlands, the study could eventually facilitate a more personalised approach to prevention, diagnostics and care.

Anxiety disorders typically first emerge during adolescence and early adulthood. These disorders cause major emotional, social and economic problems for millions of youngsters worldwide.

However, it is unclear which brain processes are involved in these anxiety disorders, the researchers said.

“This incomplete understanding of underlying brain bases is largely due to our simplistic approach to mental disorders among youths, in which clinical studies are often too small in size, with way too much focus on the ‘average patient’ rather than the individual,” said Aghajani.

“This, moreover, concurs with use of traditional analytical techniques, which are unable to produce individual-level outcomes,” the researcher added.

However, the field is slowly changing, with more focus on individuals and their unique brain characteristics, through the use of large and diverse datasets also known as “big data” combined with AI.

This article was generated from an automated news agency feed without modifications to text.