By measuring the connections of a brain region called the superior temporal cortex to other regions of the brain, the algorithm also predicted with 82 per cent accuracy whether or not a patient would respond positively to a specific anti-psychotic treatment.
"This is the first step, but ultimately we hope to find reliable biomarkers that can predict schizophrenia before the symptoms show up," said co-author Bo Cao from the University of Alberta in Canada.
"We also want to use machine learning to optimize a patient's treatment plan. It wouldn't replace the doctor. In the future, with the help of machine learning, if the doctor can select the best medicine or procedure for a specific patient at the first visit, it would be a good step forward," Cao added.
Approximately one in 100 people will be affected by schizophrenia at some point in their lives, a severe and disabling psychiatric disorder that comes with delusions, hallucinations and cognitive impairments, suggests the study published in the journal Molecular Psychiatry.
Early diagnosis of schizophrenia and many mental disorders is an ongoing challenge. Coming up with a personalized treatment strategy at the first visit with a patient is also a challenge for clinicians, the researchers said.
Current treatment of schizophrenia is still often determined by a trial-and-error style. If a drug is not working properly, the patient may suffer prolonged symptoms and side effects, and miss the best time window to get the disease controlled and treated, they noted.
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