machine learning algorithm developed by the University of Tokyo and their research colleagues that can predict the risk of psychosis from brain images.
The National Institute of Mental Health defines psychosis as a set of symptoms such as delusions, false beliefs, and hallucinations, that impact a person’s contact with reality. According to NIMH estimates, annually anywhere from 15 to 100 people out of 100,000 will develop psychosis, and often behavior changes such as withdrawing socially, decreased personal cleanliness or self-care, trouble separatingEarly detection of psychosis frequently yields more favorable recovery results, per the NIMH.
The scientists developed an AI machine learning algorithm using MRI brain scan data from people at clinical high risk who later had psychosis from 21 sites of the ENIGMA Clinical High Risk for Psychosis Working Group. The AI machine learning algorithm used was XGBoost , a scalable, open-source software library for distributed gradient-boosted decision tree algorithms.
MRI brain scans of those at clinically high risk for psychosis in other studies have shown structural differences in the brain, namely in reduced gray matter in the medial and superior temporal and medial frontal cortex according to the scientists.
“These results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis,” the scientists concluded.