University of Alberta 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 magnetic resonance images 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, which affects
300,000 Canadians, 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 per cent risk of developing schizophrenia
during their lifetime, compared with the general population risk of less than
one per cent.
“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,” said
lead author Sunil Kalmady Vasu, senior machine learning specialist in the
Faculty of Medicine & Dentistry.
Kalmady Vasu noted 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,” said Kalmady 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
per cent accuracy by examining patient brain scans. It was developed by a team
of researchers from U of A and the National Institute of Mental Health and
Neurosciences in India. The team also includes three members of the U of
A’s Neuroscience and Mental Health Institute–computing scientist and
Canada CIFAR AI Chair Russ Greiner from the Faculty of Science, and
psychiatrists Andrew Greenshaw and Serdar Dursun, who are authors on the latest
paper as well.
Kalmady Vasu said 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.
Kalmady 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,” Kalmady Vasu said. “It is very important to develop objective,
evidence-based tools for these complex disorders that afflict humankind.”
Journal article: https://www.nature.com/articles/s41537-020-00119-y
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