EARLY DIAGNOSIS OF NEUROPSYCHIATRIC DISORDERS USING COMBINED MEDICAL IMAGING AND PSYCHOLOGICAL ASSESSMENT DATA

Abstract

Neuropsychiatric diseases are challenging to diagnose early because they are so complex and contain many diverse parts. It's especially fascinating that this has an effect on schizophrenia, bipolar illness, and major depressive disorder. Traditional methods don't do a very good job of accurately diagnosing patients because they mostly employ medical imaging or psychological evaluations. We need diagnostic frameworks that employ a lot of various sorts of data straight quickly in order to get better results from early detection and intervention. The people who wrote this study recommend a deep multimodal learning framework that can discover neuropsychiatric diseases early and accurately. Combining the results of psychological assessments with structural and functional magnetic resonance imaging (fMRI) would make this framework work. The method employs convolutional neural networks (CNNs) to find characteristics in pictures and feed-forward neural networks to encode mental data. Putting all of these features into a shared representation layer is the first step in the categorization process. The layers are all related to each other. We use cross-validation and tagged clinical datasets to train the model from start to finish. This makes sure it works in a number of diverse situations. The tests demonstrated that the multimodal framework that was built makes things 8–12% more accurate on average across a range of neuropsychiatric disease groups.