SynthesisNeuroImage. Clinical2021
Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis.
Synthesis in NeuroImage. Clinical, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 5 of them syntheses that pooled it.
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Who cites it
43 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Artificial intelligence support for diagnosis of neurodevelopmental disorders during childhood: an umbrella review.Frontiers in psychiatry · 2026Pooled it
- Integrating Neuroimaging and Machine Learning to Predict Mental Disorder Outcomes: A Systematic Review.Advances in experimental medicine and biology · 2026Pooled it
- Recent developments in omics studies and artificial intelligence in depression and suicide.Translational psychiatry · 2025Pooled it
- Deep learning approach to predict autism spectrum disorder: a systematic review and meta-analysis.BMC psychiatry · 2024Pooled it
- Multivariate brain-behaviour associations in psychiatric disorders.Translational psychiatry · 2024Pooled it
- Review
- Interrogating cognitive and neural group variation in childhood: Examples in adolescent brain cognitive development and Oregon attention-deficit/hyperactivity disorder-1000 cohorts.Journal of psychopathology and clinical science · 2026Article
- Blocking the Reflection: Milestones and Hurdles for Digital Twins in Mental Health.Pharmacopsychiatry · 2026Review
- Deep learning-based differential diagnosis of major depression and bipolar disorder using microglia-cellular sensors and patient-derived small extracellular vesicles.Scientific reports · 2026Article
- Artificial intelligence in psychiatry: clinical applications, limitations, and ethical challenges.Frontiers in behavioral neuroscience · 2026Article
- Leveraging stacked classifiers for exploring the role of hedonic processing between major depressive disorder and schizophrenia.Psychological medicine · 2025Article
- Influence of cognitive networks and task performance on fMRI-based state classification using DNN models.Scientific reports · 2025Article
- A controlled trial examining large Language model conformity in psychiatric assessment using the Asch paradigm.BMC psychiatry · 2025Article
- Latest clinical frontiers related to autism diagnostic strategies.Cell reports. Medicine · 2025Review
- Diagnosis of Schizophrenia and Its Subtypes Using MRI and Machine Learning.Brain and behavior · 2025Article
- Artificial intelligence in ADHD assessment: a comprehensive review of research progress from early screening to precise differential diagnosis.Frontiers in artificial intelligence · 2025Review
- Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Predicting adolescent psychopathology from early life factors: A machine learning tutorial.Global epidemiology · 2024Article
- Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry.Brain sciences · 2024Article
- Enhanced ADHD classification through deep learning and dynamic resting state fMRI analysis.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning (DL) methods have been increasingly applied to neuroimaging data to identify patients with psychiatric and neurological disorders. This review provides an overview of the different DL applications within psychiatry and compares DL model accuracy to standard machine learning (SML). Fifty-three articles were included for qualitative analysis, primarily investigating autism spectrum disorder (ASD; n = 22), schizophrenia (SZ; n = 22) and attention-deficit/hyperactivity disorder (ADHD; n = 9). Thirty-two of the thirty-five studies that directly compared DL to SML reported a higher accuracy for DL. Only sixteen studies could be included in a meta-regression to quantitatively compare DL and SML performance. This showed a higher odds ratio for DL models, though the comparison attained significance only for ASD. Our results suggest that deep learning of neuroimaging data is a promising tool for the classification of individual psychiatric patients. However, it is not yet used to its full potential: most studies use pre-engineered features, whereas one of the main advantages of DL is its ability to learn representations of minimally processed data. Our current evaluation is limited by minimal reporting of performance measures to enable quantitative comparisons, and the restriction to ADHD, SZ and ASD as current research focusses on large publicly available datasets. To truly uncover the added value of DL, we need carefully designed comparisons of SML and DL models which are yet rarely performed.
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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 OpenQuestion graph.