ReviewNeuropsychiatric disease and treatment2019
Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review.
Review in Neuropsychiatric disease and treatment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 4 of them syntheses that pooled it.
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Who cites it
55 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Application of artificial intelligence and psychosocial functioning in psychosis: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis.Molecular psychiatry · 2023Pooled it
- Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications.Applied clinical informatics · 2022Pooled it
- Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review.Frontiers in psychiatry · 2020Pooled it
- Decoding cortical folding with deep learning: toward neurodevelopmental biomarkers of psychiatric disorders.Journal of neural transmission (Vienna, Austria : 1996) · 2026Article
- Subtyping schizophrenia via machine learning by using structural neuroimaging.Translational psychiatry · 2025Review
- Disentangling shared and unique brain functional changes associated with clinical severity and cognitive phenotypes in schizophrenia via deep learning.Communications biology · 2025Article
- Infant rat ultrasonic vocalizations in the neurodevelopmental model of schizophrenia.Scientific reports · 2025Article
- Using the excitation/inhibition ratio to optimize the classification of autism and schizophrenia.Translational psychiatry · 2025Article
- Quantification of motor abnormalities with instrumental tasks among adolescents with first-episode schizophrenia and depressive disorders.Scientific reports · 2025Article
- Graph Neural Networks and Multimodal DTI Features for Schizophrenia Classification: Insights from Brain Network Analysis and Gene Expression.Neuroscience bulletin · 2025Article
- Diagnostic value of regional homogeneity and fractional amplitude of low-frequency fluctuations in the classification of schizophrenia and bipolar disorders.European archives of psychiatry and clinical neuroscience · 2025Article
- miRNA-Based Diagnosis of Schizophrenia Using Machine Learning.International journal of molecular sciences · 2025Article
- BPEN: Brain Posterior Evidential Network for trustworthy brain imaging analysis.Neural networks : the official journal of the International Neural Network Society · 2025Article
- Improved patient identification by incorporating symptom severity in deep learning using neuroanatomic images in first episode schizophrenia.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025Article
- Convolutional Neural Network for Depression and Schizophrenia Detection.Diagnostics (Basel, Switzerland) · 2025Article
- Reinforcement learning processes as forecasters of depression remission.Journal of affective disorders · 2025Article
- Article
- Schizophrenia Detection and Classification: A Systematic Review of the Last Decade.Diagnostics (Basel, Switzerland) · 2024Review
- Quantifying brain-functional dynamics using deep dynamical systems: Technical considerations.iScience · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiagnosis of schizophrenia (SCZ) is made exclusively clinically, since specific biomarkers that can predict the disease accurately remain unknown. Machine learning (ML) represents a promising approach that could support clinicians in the diagnosis of mental disorders.
objectivesA systematic review, according to the PRISMA statement, was conducted to evaluate its accuracy to distinguish SCZ patients from healthy controls.
methodsWe systematically searched PubMed, Embase, MEDLINE, PsychINFO and the Cochrane Library through December 2018 using generic terms for ML techniques and SCZ without language or time restriction. Thirty-five studies were included in this review: eight of them used structural neuroimaging, twenty-six used functional neuroimaging and one both, with a minimum accuracy >60% (most of them 75-90%). Sensitivity, Specificity and accuracy were extracted from each publication or obtained directly from authors.
resultsSupport vector machine, the most frequent technique, if associated with other ML techniques achieved accuracy close to 100%. The prefrontal and temporal cortices appeared to be the most useful brain regions for the diagnosis of SCZ. ML analysis can efficiently detect significantly altered brain connectivity in patients with SCZ (eg, default mode network, visual network, sensorimotor network, frontoparietal network and salience network).
conclusionThe greater accuracy demonstrated by these predictive models and the new models resulting from the integration of multiple ML techniques will be increasingly decisive for early diagnosis and evaluation of the treatment response and to establish the prognosis of patients with SCZ. To achieve a real benefit for patients, the future challenge is to reach an accurate diagnosis not only through clinical evaluation but also with the aid of ML algorithms.
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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.