Artificial intelligence predicts schizophrenia with 74% accuracy

IBM’s Israel office in Petah Tikva. (photo credit:NIR ELIAS / REUTERS) Artificial intelligence and machine-learning algorithms are a useful predictor of schizophrenia with 74% accuracy, according to research at IBM and the University of Alberta in Edmonton, Canada.The retrospective study, which just appeared in Schizophrenia – published by the journal Nature – shows that the technology is capable of predicting the severity of certain symptoms of schizophrenia. The algorithm shows a significant correlation among activities observed in different areas of the brain. The pioneering study also helps identify objective biomarkers in which brain images can be used to predict the disease and its severity.The researchers analyzed images of schizophrenia patients – with characteristic disorders of the disease – and of a healthy control group, using functional magnetic resonance imaging. The fMRI test measures brain activity by analyzing changes in blood flow in certain areas of the brain. The researchers studied fMRI images of 95 participants in the study and applied machine-learning techniques to develop a model of schizophrenia that identifies the same brain connections most closely associated with the disease.The results of the study, headed by Dr. Mina Gheiratmand in Alberta, show that even when using medical imaging collected from…


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