{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:19:13Z","timestamp":1791915553063,"version":"4.3.4"},"reference-count":73,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T00:00:00Z","timestamp":1743638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Brain and Behavior Research Foundation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Obsessive-compulsive disorder (OCD) is a complex psychiatric condition characterized by significant heterogeneity in symptomatology and treatment response. Advances in neuroimaging, EEG, and other multimodal datasets have created opportunities to identify biomarkers and predict outcomes, yet traditional statistical methods often fall short in analyzing such high-dimensional data. Deep learning (DL) offers powerful tools for addressing these challenges by leveraging architectures capable of classification, prediction, and data generation. This brief review provides an overview of five key DL architectures\u2014feedforward neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, and transformers\u2014and their applications in OCD research and clinical practice. We highlight how these models have been used to identify the neural predictors of treatment response, diagnose and classify OCD, and advance precision psychiatry. We conclude by discussing the clinical implementation of DL, summarizing its advances and promises in OCD, and underscoring key challenges for the field.<\/jats:p>","DOI":"10.3390\/jcm14072442","type":"journal-article","created":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T04:33:43Z","timestamp":1743654823000},"page":"2442","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Precision Psychiatry for Obsessive-Compulsive Disorder: Clinical Applications of Deep Learning Architectures"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4567-8694","authenticated-orcid":false,"given":"Brian A.","family":"Zaboski","sequence":"first","affiliation":[{"name":"Yale School of Medicine, Department of Psychiatry, Yale University, New Haven, CT 06510, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2214-7858","authenticated-orcid":false,"given":"Lora","family":"Bednarek","sequence":"additional","affiliation":[{"name":"Department of Psychology, University of California, San Diego, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100231","DOI":"10.1016\/j.jadr.2021.100231","article-title":"A Systematic Review of Misdiagnosis in Those with Obsessive-Compulsive Disorder","volume":"6","author":"Stahnke","year":"2021","journal-title":"J. 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