AIMC Topic: Schizophrenia
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Classification of schizophrenia spectrum disorder using machine learning and functional connectivity: reconsidering the clinical application.
BMC psychiatry
Apr 14, 2025
BACKGROUND: Early identification of Schizophrenia Spectrum Disorder (SSD) is crucial for effective intervention and prognosis improvement. Previous neuroimaging-based classifications have primarily focused on chronic, medicated SSD cohorts. However, ...
Machine learning-based differentiation of schizophrenia and bipolar disorder using multiscale fuzzy entropy and relative power from resting-state EEG.
Translational psychiatry
Apr 12, 2025
Schizophrenia (SZ) and bipolar disorder (BD) pose diagnostic challenges due to overlapping clinical symptoms and genetic factors, often resulting in misdiagnosis and suboptimal treatment outcomes. This study aimed to identify EEG-based biomarkers tha...
Classification of schizophrenia based on RAnet-ET: resnet based attention network for eye-tracking.
Journal of neural engineering
Apr 10, 2025
There is a notable need of quantifiable and objective methods for the classification of schizophrenia. Patients with schizophrenia exhibit atypical eye movements compared with healthy individuals. To address this need, we have developed a classificat...
Association of the characteristics of brain magnetic resonance imaging with genes related to disease onset in schizophrenia patients.
SLAS technology
Mar 28, 2025
BACKGROUND: Schizophrenia (SCH) is a complex neurodevelopmental disorder, whose pathogenesis is not fully elucidated. This article aims to reveal disease-specific brain structural and functional changes and their potential genetic basis by analyzing ...
An ensemble approach using multidimensional convolutional neural networks in wavelet domain for schizophrenia classification from sMRI data.
Scientific reports
Mar 25, 2025
Schizophrenia is a complicated mental condition marked by disruptions in thought processes, perceptions, and emotional responses, which can cause severe impairment in everyday functioning. sMRI is a non-invasive neuroimaging technology that visualize...
Deep Learning-Assisted SERS for Therapeutic Drug Monitoring of Clozapine in Serum on Plasmonic Metasurfaces.
Nano letters
Mar 20, 2025
Clozapine is widely regarded as one of the most effective therapeutics for treatment-resistant schizophrenia. Despite its proven efficacy, the therapeutic use of clozapine is complicated by its narrow therapeutic index, which necessitates rapid and p...
Exploring the significance of the frontal lobe for diagnosis of schizophrenia using explainable artificial intelligence and group level analysis.
Psychiatry research. Neuroimaging
Mar 13, 2025
Schizophrenia (SZ) is a complex mental disorder characterized by a profound disruption in cognition and emotion, often resulting in a distorted perception of reality. Magnetic resonance imaging (MRI) is an essential tool for diagnosing SZ which helps...
miRNA-Based Diagnosis of Schizophrenia Using Machine Learning.
International journal of molecular sciences
Mar 4, 2025
Diagnostic practices for schizophrenia are unreliable due to the lack of a stable biomarker. However, machine learning holds promise in aiding in the diagnosis of schizophrenia and other neurological disorders. Dysregulated miRNAs were extracted from...
Neurofind: using deep learning to make individualised inferences in brain-based disorders.
Translational psychiatry
Feb 27, 2025
Within precision psychiatry, there is a growing interest in normative models given their ability to parse heterogeneity. While they are intuitive and informative, the technical expertise and resources required to develop normative models may not be a...
Identifying periphery biomarkers of first-episode drug-naïve patients with schizophrenia using machine-learning-based strategies.
Progress in neuro-psychopharmacology & biological psychiatry
Feb 25, 2025
Schizophrenia is a complex mental disorder. Accurate diagnosis and classification of schizophrenia has always been a major challenge in clinic due to the lack of biomarkers. Therefore, identifying molecular biomarkers, particularly in the peripheral ...
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