AI Can Identify Clinically Anxious Youth Based On Brain Structure: Study
Artificial intelligence (AI) can help recognise individuals with anxiety disorders based on their unique brain structure, according to a 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 (ML) - 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
This incomplete understanding
Classic Public High School Ahmadpora Zone Singhpora Pattan
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
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.
S.no 01 02 03 04 05
6
07 08 09 10
11
12
13
14
bmg Fee structure for session 2023-24
Ref no: CPHS/AP/163
Class P. Nry Nursery L.K.G U.K.G 1st
2nd
3rd
4th
5th
6th
7th
8th
9th 10th
Dated: 22-02-2024
Monthly Tuition Fee 650
650
650
650
700
700
750
750
750
800
800
800
1300
1300