Artificial intelligence could help predict psychosis before it occurs

By Stephen Beech via SWNS

Psychosis could be predicted before it occurs - thanks to artificial intelligence.

Brain images from thousands of people worldwide have been used to create a machine learning-based classifier that could aid early diagnosis of severe mental health conditions.

The machine-learning tool classifies MRI brain scans into those who are healthy and those at risk of a psychotic episode.

An international research team used the classifier to compare scans from more than 2,000 people from 21 global locations.

Around half of the participants had been identified as being clinically at "high risk" of developing psychosis.

Using training data, the classifier was 85 percent accurate at differentiating between people who were not at risk and those who later experienced overt psychotic symptoms. Using new data, it was 73 percent accurate.

Scientists say the tool could be helpful in future clinical settings, as while most people who experience psychosis make a full recovery, earlier intervention typically leads to better outcomes with less negative impact on people’s lives.

A psychotic episode commonly involves delusions, hallucinations or disorganized thinking.

There is no single cause, but it can be triggered by illness or injury, trauma, drug or alcohol use, medication, or a genetic predisposition.

Although an episode can be scary or unsettling, psychosis is treatable and most people recover.

As the most common age for a first episode is as a teenager or in early adulthood, when the brain and body are undergoing a lot of change, doctors say it can be difficult to identify young people in need of help.

Professor Shinsuke Koike, of the University of Tokyo in Japan, said: “At most only 30 percent of clinical high-risk individuals later have overt psychotic symptoms, while the remaining 70 percent do not.

“Therefore, clinicians need help to identify those who will go on to have psychotic symptoms using not only subclinical signs, such as changes in thinking, behavior and emotions but also some biological markers.”

Previous studies using brain MRI have suggested that structural differences occur in the brain after the onset of psychosis.

However, the new study is believed to be the first time that differences in the brains of those who are at very high risk but have not yet experienced psychosis have been identified.

The team, from 21 different institutions in 15 different countries, gathered a large and diverse group of adolescent and young adult participants.

Koike says MRI research into psychotic disorders can be challenging because variations in brain development and in MRI machines make it difficult to get very accurate, comparable results.

And, with young people, it can be difficult to differentiate between changes that are taking place because of typical development and those due to mental illness.

Koike said: “Different MRI models have different parameters which also influence the results.

“Just like with cameras, varied instruments and shooting specifications create different images of the same scene, in this case, the participant’s brain.

"However, we were able to correct for these differences and create a classifier which is well tuned to predicting psychosis onset.”

A total of 1,165 participants were divided into three groups of people at clinically high risk: those who later developed psychosis; those who didn’t develop psychosis; and people with uncertain follow-up status.

A fourth group of 1,029 healthy controls were monitored for comparison.

Using the scans, the research team trained a machine-learning algorithm to identify patterns in the brain anatomy of the participants.

From the four groups, the team used the algorithm to classify participants into two main groups of interest: healthy controls and those at high risk who later developed overt psychotic symptoms.

In training, the tool was 85 percent accurate at classifying the results, while in the final test using new data, it was 73 percent accurate at predicting which participants were at high risk of psychosis onset.

Based on the results, published in the journal Molecular Psychiatry, the team considers that providing brain MRI scans for people identified as being at clinically high risk may be helpful for predicting future psychosis onset.

Koike said: “We still have to test whether the classifier will work well for new sets of data.

"Since some of the software we used is best for a fixed data set, we need to build a classifier that can robustly classify MRIs from new sites and machines, a challenge which a national brain science project in Japan, called Brain/MINDS Beyond, is now taking on."

He added: “If we can do this successfully, we can create more robust classifiers for new data sets, which can then be applied to real-life and routine clinical settings.”