Application of Artificial Intelligence in Obsessive-Compulsive Disorder Detection and Response to Treatment: A Systematic Review
Application of Artificial Intelligence in Obsessive-Compulsive Disorder Detection and Response to Treatment: A Systematic Review
Blog Article
Obsessive-compulsive disorder (OCD) is a neuropsychiatric disorder that causes unwanted thoughts and repetitive behaviors in a person’s life.Clinical data and neuroimaging technologies are common methods used to diagnose OCD disorders and predict treatment responses.This study reviews the application of artificial intelligence (AI), particularly traditional machine learning (ML) and deep learning (DL), in the detection of OCD and the prediction of responses to various 5 Pc. Sectional treatments from clinical data and neuroimaging modalities, including electroencephalography (EEG), magnetic resonance imaging (MRI), functional MRI (fMRI) and diffusion tensor imaging data (DTI).A comprehensive search was performed utilizing Google Scholar and PubMed for published papers up to 2024.The search utilized the following keywords: “obsessive-compulsive disorder,” “OCD + prediction response to treatment,” “OCD + electroencephalography (EEG),” “OCD classification,” “EEG + OCD diagnosis,” “OCD + machine learning,” “neuroimaging + OCD + predict,” and “OCD + predictor.
” This paper summarizes and discusses the 88 selected papers.Our findings demonstrate the significant role of AI in OCD diagnosis and predicting treatment response.Support Vector Machines methods have been used more among AI methods.Moreover, extracting and selecting features from EEG is more common than other neuroimaging modalities.Some clinical characteristics, such as age, gender, and the severity of the obsession, along with neuroimaging-derived factors like EEG frequency band power, EEG complexity, and white matter volume, help diagnose Cap OCD and predict treatment response.
Future studies should aim to develop deep learning to facilitate the rapid diagnosis of OCD and accurately predict treatment responses.