ReviewJournal of clinical medicine2025
Precision Psychiatry for Obsessive-Compulsive Disorder: Clinical Applications of Deep Learning Architectures.
Review in Journal of clinical medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- A Narrative Review of Ethical Issues in Precision Psychiatry: Mapping Unresolved Tensions Across Modalities.Journal of personalized medicine · 2026Review
- Global scientific trends on neuroimaging in obsessive-compulsive disorder in the early twenty-first century: a bibliometric analysis and visualization analysis.Annals of general psychiatry · 2026Review
- Classifying Obsessive-Compulsive Disorder from Resting-State EEG Using Convolutional Neural Networks: A Pilot Study.Computational psychiatry (Cambridge, Mass.) · 2026Article
- Quo Vadis translational neuroscience?Translational neuroscience · 2026Review
- Commentary: Deep learning in obsessive-compulsive disorder: a narrative review.Frontiers in psychiatry · 2026Article
- Classifying Obsessive-Compulsive Disorder from Resting-State EEG using Convolutional Neural Networks: A Pilot Study.medRxiv : the preprint server for health sciences · 2025Article
- Deep learning in obsessive-compulsive disorder: a narrative review.Frontiers in psychiatry · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
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-feedforward neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, and transformers-and 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.