AI Can Now Predict Schizophrenia by Observing Blood Flow in the Brain
Results of a research conducted by IBM and the University of Alberta, published in July 2017, indicates an early diagnostic procedure for schizophrenia by analyzing the blood flow within the brain.
Schizophrenia affects only 1.2% of the population in the U.S., however, it majorly affects the daily functioning of its patient. The research conducted, aimed at helping doctors diagnose the onset of the disorder and the severity of its symptoms with the help of an MRI scan and a neural network built especially to observe blood flow within the brain. The research team secured fMRI images of 95 anonymous members from the Function Biomedical Informatics Research Network, including brain scans of a healthy control group and a schizophrenic experimental group. The patients were made to complete a simple audio-based exercise after which scans were taken to illustrate blood flow through various parts of their brain. The data received was then used by a neural network to create a predictive model that would identify schizophrenia based on the blood flow. This model was able differentiate between the control group and the schizophrenic group with 74% level of accuracy.
“We have discovered a number of significant abnormal connections in the brain that can be explored in future studies and AI-created models bring us one step closer to finding objective neuroimaging-based patterns that are diagnostic and prognostic markers of schizophrenia.” Said Dr. Serdar Dursun, Professor of Psychiatry & Neuroscience, University of Alberta.
This innovative multidisciplinary approach helps generate new insights and better understand the neurobiology of schizophrenia, also predicting severity of its symptoms, in turn leading to the development of improved technologies to control and treat the disease in the near future.
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