Machine Learning Helps Predict Treatment Outcomes of Schizophrenia

Posted on July 19, 2018

Approximately one in 100 people will be affected by schizophrenia at some point in their lives, a severe and disabling psychiatric disorder that comes with delusions, hallucinations and cognitive impairments. Most patients with schizophrenia develop the symptoms early in life and will struggle with them for decades.

Researchers have used artificial intelligence to help identify patients suffering from schizophrenia and to ascertain if they would respond to treatment. They used a machine-learning algorithm to examine functional magnetic resonance imaging (MRI) images of both newly diagnosed, previously untreated schizophrenia patients and healthy subjects. By measuring the connections of a brain region called the superior temporal cortex to other regions of the brain, the algorithm successfully identified patients with schizophrenia at 78 per cent accuracy. It also predicted with 82 per cent accuracy whether or not a patient would respond positively to a specific antipsychotic treatment named risperidone.

According to researchers, early diagnosis of schizophrenia and many mental disorders is an ongoing challenge. Coming up with the personalized treatment strategy at the first visit with a patient is also a challenge for clinicians. Current treatment of schizophrenia is still often determined by a trial-and-error style. If a drug is not working properly, the patient may suffer prolonged symptoms and side effects, and miss the best time window to get the disease controlled and treated.

Researchers hope to expand the work to include other mental illness such as major depressive and bipolar disorders. While the initial results of schizophrenia diagnosis and treatment are encouraging, further validations on large samples will be necessary and more refinement is needed to increase accuracy before the work can be translated into a useful tool in a clinical environment.


Category(s):Schizophrenia

Source material from Science Daily