Zaboski Brian A, Bednarek Lora, Ayoub Karen, Pittenger Christopher
Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States.
University of California, San Diego, San Diego, CA, United States.
Front Psychiatry. 2025 Jun 13;16:1581297. doi: 10.3389/fpsyt.2025.1581297. eCollection 2025.
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 10 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.
强迫症（OCD）是一种使人衰弱的精神疾病，其特征为侵入性思维和重复行为，在及时诊断和有效治疗方面存在重大障碍。深度学习作为机器学习的一个子集，通过利用大型复杂数据集来识别强迫症、对症状进行分类并预测治疗结果，提供了有前景的工具来应对这些挑战。这篇叙述性综述综合了10项将深度学习应用于强迫症研究的研究结果。结果表明，使用神经影像学、脑电图和临床数据进行诊断分类的准确率很高（80-98%），在症状分类和治疗反应预测方面也有很有前景的应用。然而，当前模型受到样本量小、缺乏对比治疗预测以及对早期反应检测或可扩展监测解决方案关注极少的限制。新兴机会包括利用被动收集的数据，如可穿戴传感器或电子病历，来加强早期检测和持续症状跟踪。未来的研究应优先考虑多模态数据集、前瞻性研究设计以及临床可实施的模型，以便将深度学习进展转化为针对强迫症的精准精神病学。
