Schizophrenia is a mental disorder that affects more than 3 million people in the US alone. It’s a serious condition, that as of yet, has no cure. However, thanks to a group of researchers from the University of Alberta, there may be light at the end of the tunnel.

Using a machine learning algorithm to take a closer look at magnetic resonance imaging (MRI) images they were successfully able to diagnose schizophrenia patients and predict the treatment outcomes of schizophrenia patients with an average accuracy of around 80 percent. The research was carried out by Bo Cao at the U of A’s Department of Psychiatry alongside Xiang Yang Zhang from the University of Texas Health Science Center at Houston.





The way the algorithm worked is by measuring the connections between the superior temporal cortex and other regions of the brain. In doing this the researchers were able to identify correctly 78 percent of patients with schizophrenia. They also predicted how many would respond to positively to the antipsychotic medicine, risperidone, with an 82 percent accuracy.

“This is the first step, but ultimately we hope to find reliable biomarkers that can predict schizophrenia before the symptoms show up,” says Cao, an assistant professor of psychiatry at the U of A. “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.”





Schizophrenia is unfortunately quite common, affecting as many as 1 in every 100 people. Most people are diagnosed with the disorder between the ages of 16 and 35 and struggle for many years. Diagnosing schizophrenia early on has always been a challenge and so is the development of personalized treatments. At the moment many treatments for the illness is determined by trial and error method. The problem in doing this is that each drug that doesn’t work, pushes that window of chance for treating the disease a little further away.

Cao is hoping to open up his research to cover other mental illnesses including bipolar and major depressive disorders. While the results from this study are promising, Cao says that more work is required to increase the accuracy before it can be used in a clinical setting. “It will be a joint effort of the patients, psychiatrists, neuroscientists, computer scientists and researchers in other disciplines to build better tools for precise mental health,” confirms Cao.

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