Tokyo, Feb 9 (IANS) An international team of researchers has developed a machine learning (ML) tool to predict the onset of psychosis. The tool can classify MRI brain scans between those who are healthy and those at risk of suffering a psychotic episode.
The team led by researchers at the University of Tokyo used the classifier to compare scans of more than 2,000 people from 21 global locations.
Approximately half of the participants had been identified as clinically at high risk of developing psychosis.
Using training data, the classifier was 85 percent accurate in differentiating between people who were not at risk and those who later experienced overt psychotic symptoms. Using new data, it was 73 percent accurate.
The work has been published in Molecular Psychiatry.
“At most, only 30 percent of clinically high-risk individuals subsequently develop overt psychotic symptoms, while the remaining 70 percent do not,” said associate professor Shinsuke Koike of the Graduate School of Arts and Sciences at the University of Tokyo.
“Therefore, clinicians need help identifying those who will have psychotic symptoms using not only subclinical signs, such as changes in thinking, behavior and emotions, but also some biological markers.”
This tool could be useful in future clinical settings, as while most people who experience psychosis make a full recovery, earlier intervention generally leads to better outcomes with less negative impact on people’s lives.
Anyone can experience a psychotic episode, commonly involving delusions, hallucinations, or disorganized thoughts. There is no single cause, but it can be triggered by an illness or injury, trauma, drug or alcohol use, medications, or a genetic predisposition.
Although it can be frightening or disturbing, psychosis is treatable and most people recover.
Because the most common age for a first episode is during adolescence or early adulthood, when the brain and body are undergoing many changes, it can be difficult to identify young people who need help.