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University of Alberta researchers have developed an AI tool to predict schizophrenia by analysing brain scans, aiming for earlier diagnosis and treatment.
Sunil Kalmady Vasu (centre) led a recent study with fellow U of A researchers including Russ Greiner (left), Andrew Greenshaw (right) and Serdar Dursun (not pictured), showing that a machine learning tool could help predict early symptoms of schizophrenia in siblings and children of patients
In a recently published study, researchers explained how their tool was used to analyse functional magnetic resonance images of 57 healthy first-degree relatives (siblings or children) of schizophrenia patients. According to the team, the tool accurately identified the 14 individuals who scored highest on a self-reported schizotypal personality trait scale.
New machine learning tool predicts schizophrenia
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.
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Machine learning tool used to predict early symptoms of schizophrenia in relatives of patients
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 ce
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IMAGE: Sunil Kalmady Vasu (centre) led a recent study with fellow U of A researchers including Russ Greiner (left), Andrew Greenshaw (right) and Serdar Dursun (not pictured), showing that a machine. view more
Credit: University of Alberta (taken pre-COVID-19)
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.