REVIEW article

Front. Psychiatry

Sec. Anxiety and Stress Disorders

Volume 16 - 2025 | doi: 10.3389/fpsyt.2025.1581297

Deep Learning in Obsessive-Compulsive Disorder: A Narrative Review

Provisionally accepted
Brian A ZaboskiBrian A Zaboski1*Lora BednarekLora Bednarek2Karen AyoubKaren Ayoub1Christopher PittengerChristopher Pittenger1,3,4,5,6,7
  • 1Department of Psychiatry,School of Medicine, Yale University, New Haven, Connecticut, United States
  • 2University of California, San Diego, La Jolla, California, United States
  • 3Department of Psychology, Faculty of Arts and Sciences, Yale University, New Haven, Connecticut, United States
  • 4Child Study Center, School of Medicine, Yale University, New Haven, Connecticut, United States
  • 5Department of Neuroscience, School of Medicine, Yale University, New Haven, Connecticut, United States
  • 6Center for Brain and Mind Health, School of Medicine, Yale University, New Haven, Connecticut, United States
  • 7Wu Tsai Institute, Yale University, New Haven, Connecticut, United States

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Obsessive-compulsive disorder (OCD) is a debilitating psychiatric condition characterized by intrusive thoughts and repetitive behaviors, with significant barriers to timely diagnosis and effective treatment. Deep learning, a subset of machine learning, offers promising tools to address these challenges by leveraging large, complex datasets to identify OCD, classify symptoms, and predict treatment outcomes. This narrative review synthesizes findings from 11 studies that applied deep learning to OCD research. Results demonstrate high accuracy in diagnostic classification (80-98%) using neuroimaging, EEG, and clinical data, as well as promising applications in symptom classification and treatment response prediction. However, current models are limited by small sample sizes, lack of comparative treatment predictions, and minimal focus on early response detection or scalable monitoring solutions. Emerging opportunities include leveraging passively collected data, such as wearable sensors or electronic medical records, to enhance early detection and continuous symptom tracking. Future research should prioritize multimodal datasets, prospective study designs, and clinically implementable models to translate deep learning advancements into precision psychiatry for OCD.

Keywords: Obsessive-Compulsive Disorder, precision psychiatry, machine learning, deep learning, Neuroimaging, treatment prediction, Diagnostic prediction

Received: 21 Feb 2025; Accepted: 15 May 2025.

Copyright: © 2025 Zaboski, Bednarek, Ayoub and Pittenger. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Brian A Zaboski, Department of Psychiatry,School of Medicine, Yale University, New Haven, 06510, Connecticut, United States

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Supplementary Material