Canadian researchers have taken another step forward in developing an artificial intelligence tool to predict schizophrenia by analyzing brain scans. In recently published research, the tool was used to analyze functional MRI of 57 healthy first-degree relatives (siblings or children) of schizophrenia patients. It accurately identified the 14 individuals who scored highest on a self-reported schizotypal personality trait scale.
Schizophrenia can cause delusions, hallucinations, disorganized speech, trouble with thinking and lack of motivation, and is usually treated with a combination of drugs, psychotherapy, and brain stimulation. First-degree relatives of patients have up to a 19% risk of developing schizophrenia during their lifetime, compared with the general population risk of less than 1%.
“Our evidence-based tool looks at the neural signature in the brain, with the potential to be more accurate than diagnosis by the subjective assessment of symptoms alone,” says lead author Sunil Kalmady Vasu, senior machine learning specialist in the University of Alberta Faculty of Medicine & Dentistry.
Vasu notes that the tool is designed to be a decision support tool and would not replace diagnosis by a psychiatrist. He also pointed out that while having schizotypal personality traits may cause people to be more vulnerable to psychosis, it is not certain that they will develop full-blown schizophrenia. “The goal is for the tool to help with early diagnosis, to study the disease process of schizophrenia and to help identify symptom clusters,” says Vasu, who is also a member of the Alberta Machine Intelligence Institute.
The tool, dubbed EMPaSchiz (Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction), was previously used to predict a diagnosis of schizophrenia with 87% accuracy by examining patient brain scans. It was developed by a team of researchers from the University of Alberta and the National Institute of Mental Health and Neurosciences in India.
Vasu says next steps for the research will test the tool’s accuracy on non-familial individuals with schizotypal traits, and to track assessed individuals over time to learn whether they develop schizophrenia later in life. Vasu is also using the same principles to develop algorithms to predict outcomes such as mortality and readmissions for heart failure in cardiovascular patients through the Canadian VIGOUR Centre.
“Severe mental illness and cardiovascular problems cause functional disability and impair quality of life,” Vasu says. “It is very important to develop objective, evidence-based tools for these complex disorders that afflict humankind.”
