Utilizing A.I., Machine Learning to Better Understand Schizophrenia

A mix of machine learning and artificial intelligence-based algorithms could redefine how schizophrenia is diagnosed. Scientists at IBM Canada and the University of Alberta created a specialized program that was able to assist in predicting instances of schizophrenia with 74 percent accuracy. The algorithms sifted through de-identified brain functional Magnetic Resonance Imaging (fMRI) data from an initiative called the Function Biomedical Informatics Research Network. The neuroimaging information used in this study was of 95 patients diagnosed with schizophrenia and schizoaffective disorders as well as individuals that served as a healthy control group. Scientists can use fMRI to gage blood flow changes in specific areas of the brain, but this specific data set was reflective of research done on brain networks at different resolution levels.