Researchers Develop New Technique to Predict Treatment Outcomes of Schizophrenia

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Researchers from University of Alberta Faculty of Medicine & Dentistry identify the responses of treatments for schizophrenia using artificial intelligence.

According to Schizophrenia and Related Disorders Alliance of America (SARDAA), around 3.5 million people in the U.S are diagnosed with schizophrenia and it is one of the leading causes of disability that comes with delusions, hallucinations and cognitive impairments. Moreover, around 4.9% of people suffering from the disease die by suicide. The research led by Bo Cao at the University of Alberta’s Department of Psychiatry, with the collaboration of Xiang Yang Zhang at the University of Texas Health Science Center at Houston, suggested that the diagnosis and treatment of mental health disorders can be aided through the help of artificial intelligence. A machine-learning algorithm was used to examine functional magnetic resonance imaging (MRI) images of schizophrenia patients. The group included both newly diagnosed and previously untreated schizophrenia patients and healthy subjects whose connections between the superior temporal cortex and other regions of the brain were measured. The researchers observed that the algorithm successfully identified patients with schizophrenia with an accuracy of 78%. Furthermore, it predicted whether or not a patient would respond positively to risperidone, a specific antipsychotic treatment, with an accuracy of 82%. The research was published in the journal Molecular Psychiatry on June 19, 2018.

The researchers stated that the technique is expected to find reliable biomarkers that can predict schizophrenia prior to observed symptoms. Furthermore, it can be used to optimize a patient’s treatment plan. Current treatments of schizophrenia are based on a trial-and-error style. Moreover, an ineffective drug may lead to prolonged symptoms and side effects in the patients and hinder the best time window to get the disease controlled and treated. The researchers are further aiming to use their technique against other mental illnesses such as major depressive and bipolar disorders.

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