Trial reportScientific reports2022
Machine learning classification of schizophrenia patients and healthy controls using diverse neuroanatomical markers and Ensemble methods.
Trial report in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
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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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications.Psychological medicine · 2025Pooled it
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- Improved schizophrenia detection using structural MRI with multi-scale transform and deep orthogonal features.Physical and engineering sciences in medicine · 2026Article
- Explainable AI based ensemble model for the identification of Schizophrenia prodromal phase.Scientific reports · 2026Article
- Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Characterizing multivariate regional hubs for schizophrenia classification, sex differences, and brain age estimation using explainable AI.medRxiv : the preprint server for health sciences · 2025Article
- Can artificial intelligence be the future solution to the enormous challenges and suffering caused by Schizophrenia?Schizophrenia (Heidelberg, Germany) · 2025Review
- Predicting antipsychotic responsiveness using a machine learning classifier trained on plasma levels of inflammatory markers in schizophrenia.Translational psychiatry · 2025Article
- Peering into the mind: unraveling schizophrenia's secrets using models.Molecular psychiatry · 2025Review
- Schizophrenia Detection and Classification: A Systematic Review of the Last Decade.Diagnostics (Basel, Switzerland) · 2024Review
- Identification of male schizophrenia patients using brain morphology based on machine learning algorithms.World journal of psychiatry · 2024Article
- Classification of First-Episode Psychosis with EEG Signals: ciSSA and Machine Learning Approach.Biomedicines · 2023Article
- Constructing the Schizophrenia Recognition Method Employing GLCM Features from Multiple Brain Regions and Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
- Editorial: Machine learning methods for human brain imaging.Frontiers in neuroinformatics · 2023Article
- Effectiveness of Artificial Intelligence Methods in Personalized Aggression Risk Prediction within Inpatient Psychiatric Treatment Settings-A Systematic Review.Journal of personalized medicine · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Schizophrenia is a major psychiatric disorder that imposes enormous clinical burden on patients and their caregivers. Determining classification biomarkers can complement clinical measures and improve understanding of the neural basis underlying schizophrenia. Using neuroanatomical features, several machine learning based investigations have attempted to classify schizophrenia from healthy controls but the range of neuroanatomical measures employed have been limited in range to date. In this study, we sought to classify schizophrenia and healthy control cohorts using a diverse set of neuroanatomical measures (cortical and subcortical volumes, cortical areas and thickness, cortical mean curvature) and adopted Ensemble methods for better performance. Additionally, we correlated such neuroanatomical features with Quality of Life (QoL) assessment scores within the schizophrenia cohort. With Ensemble methods and diverse neuroanatomical measures, we achieved classification accuracies ranging from 83 to 87%, sensitivities and specificities varying between 90-98% and 65-70% respectively. In addition to lower QoL scores within schizophrenia cohort, significant correlations were found between specific neuroanatomical measures and psychological health, social relationship subscale domains of QoL. Our results suggest the utility of inclusion of subcortical and cortical measures and Ensemble methods to achieve better classification performance and their potential impact of parsing out neurobiological correlates of quality of life in schizophrenia.
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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.