Obsessive Compulsive Disorder

Obsessive-Compulsive Disorder (OCD) research is actively exploring the use of machine learning to improve diagnosis and understanding of the disorder's neurological underpinnings. Current efforts focus on applying various machine learning models, including convolutional neural networks, XGBoost, and other neural network architectures, to analyze neuroimaging data (like fMRI and T1 resting-state MRI) and biomarkers to identify predictive patterns and potential diagnostic tools. This work aims to improve diagnostic accuracy, potentially leading to earlier and more effective interventions. The ultimate goal is to develop more precise and objective methods for identifying and classifying OCD, improving patient care and advancing our understanding of its complex etiology.