AI Medical Compendium Journal:
Psychiatry research. Neuroimaging

Showing 1 to 10 of 18 articles

Optimized attention-enhanced U-Net for autism detection and region localization in MRI.

Psychiatry research. Neuroimaging
Autism spectrum disorder (ASD) is a neurodevelopmental condition that affects a child's cognitive and social skills, often diagnosed only after symptoms appear around age 2. Leveraging MRI for early ASD detection can improve intervention outcomes. Th...

Exploring the significance of the frontal lobe for diagnosis of schizophrenia using explainable artificial intelligence and group level analysis.

Psychiatry research. Neuroimaging
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...

Machine learning based classification of excessive smartphone users via neuronal cue reactivity.

Psychiatry research. Neuroimaging
Excessive Smartphone Use (ESU) poses a significant challenge in contemporary society, yet its recognition as a distinct disorder remains ambiguous. This study aims to address this gap by leveraging functional magnetic resonance imaging (fMRI) data an...

MRI-based deep learning for differentiating between bipolar and major depressive disorders.

Psychiatry research. Neuroimaging
Mood disorders, particularly bipolar disorder (BD) and major depressive disorder (MDD), manifest changes in brain structure that can be detected using structural magnetic resonance imaging (MRI). Although structural MRI is a promising diagnostic tool...

Quantitative assessment of brain structural abnormalities in children with autism spectrum disorder based on artificial intelligence automatic brain segmentation technology and machine learning methods.

Psychiatry research. Neuroimaging
RATIONALE AND OBJECTIVES: To explore the characteristics of brain structure in Chinese children with autism spectrum disorder (ASD) using artificial intelligence automatic brain segmentation technique, and to diagnose children with ASD using machine ...

Schizophrenia diagnosis using the GRU-layer's alpha-EEG rhythm's dependability.

Psychiatry research. Neuroimaging
Verifying schizophrenia (SZ) can be assisted by deep learning techniques and patterns in brain activity observed in alpha-EEG recordings. The suggested research provides evidence of the reliability of alpha-EEG rhythm in a Gated-Recurrent-Unit-based ...

Deep learning based diagnosis of PTSD using 3D-CNN and resting-state fMRI data.

Psychiatry research. Neuroimaging
BACKGROUND: The incidence rate of Posttraumatic stress disorder (PTSD) is currently increasing due to wars, terrorism, and pandemic disease situations. Therefore, accurate detection of PTSD is crucial for the treatment of the patients, for this purpo...

Biased accuracy in multisite machine-learning studies due to incomplete removal of the effects of the site.

Psychiatry research. Neuroimaging
Brain MRI researchers conducting multisite studies, such as within the ENIGMA Consortium, are very aware of the importance of controlling the effects of the site (EoS) in the statistical analysis. Conversely, authors of the novel machine-learning MRI...

Identification of voxel-based texture abnormalities as new biomarkers for schizophrenia and major depressive patients using layer-wise relevance propagation on deep learning decisions.

Psychiatry research. Neuroimaging
Non-segmented MRI brain images are used for the identification of new Magnetic Resonance Imaging (MRI) biomarkers able to differentiate between schizophrenic patients (SCZ), major depressive patients (MD) and healthy controls (HC). Brain texture meas...

Machine-learning based brain age estimation in major depression showing no evidence of accelerated aging.

Psychiatry research. Neuroimaging
Molecular biological findings indicate that affective disorders are associated with processes akin to accelerated aging of the brain. The use of the BrainAGE (brain age estimation gap) framework allows machine-learning based detection of a gap betwee...