Psychiatry

AI depictions of psychiatric diagnoses: a preliminary study of generative image outputs in Midjourney V.6 and DALL-E 3.

OBJECTIVE: This paper investigates how state-of-the-art generative artificial intelligence (AI) image models represent common psychiatric diagnoses. We offer key lessons derived from these representations to inform clinicians, researchers, generative AI companies, policymakers and the public about the potential impacts of AI-generated imagery on mental health discourse.

Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques.

Brain connectivity represents the functional organization of the brain, which is an important indicator for evaluating neuropsychiatric disorders and treatment effects. Schizophrenia is associated with impaired functional connectivity but characterizing the complex abnormality patterns has been challenging. In this work, we used resting-state functional magnetic resonance imaging (fMRI) data to me...

Use of machine learning for simplification of University Personality Inventory (UPI).

BACKGROUND: Rapid diagnosis of mental health problems is crucial for college students. The University Personality Inventory (UPI) is a commonly used t...

Artificial neural network for evaluating sleep spindles and slow waves after transcranial magnetic stimulation in a child with autism.

Sleep spindles (SS) and slow waves (SW) serve as indicators of the integrity of thalamocortical connections, which are often compromised in individual...

The Transformative Power of Digital Breast Tomosynthesis and Artificial Intelligence in Breast Cancer Diagnosis.

The integration of Digital Breast Tomosynthesis (DBT) and Artificial Intelligence (AI) represents a significant advance in breast cancer screening. Th...

Research on mood monitoring and intervention for anxiety disorder patients based on deep learning wearable devices.

BackgroundAnxiety disorders are common mental health issues that have a significant effect on people's quality of life. Conventional techniques for tr...

Development and validation of electronic health record-based, machine learning algorithms to predict quality of life among family practice patients.

Health-related quality of life (HRQol) is a crucial dimension of care outcomes. Many HRQoL measures exist, but methodological and implementation chall...

RNA Editing Signatures Powered by Artificial Intelligence: A New Frontier in Differentiating Schizophrenia, Bipolar, and Schizoaffective Disorders.

Mental health disorders are devastating illnesses, often misdiagnosed due to overlapping clinical symptoms. Among these conditions, bipolar disorder, ...

Enhancing suicidal behavior detection in EHRs: A multi-label NLP framework with transformer models and semantic retrieval-based annotation.

BACKGROUND: Suicide is a leading cause of death worldwide, making early identification of suicidal behaviors crucial for clinicians. Current Natural L...

Using machine learning to predict the probability of incident 2-year depression in older adults with chronic diseases: a retrospective cohort study.

BACKGROUND: Older adults with chronic diseases are at higher risk of depressive symptoms than those without. For the onset of depressive symptoms, the...

Site-Invariant Meta-Modulation Learning for Multisite Autism Spectrum Disorders Diagnosis.

Large amounts of fMRI data are essential to building generalized predictive models for brain disease diagnosis. In order to conduct extensive data ana...

LCGNet: Local Sequential Feature Coupling Global Representation Learning for Functional Connectivity Network Analysis With fMRI.

Analysis of functional connectivity networks (FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) has greatly advanced ou...

Psychosocial effects of a humanoid robot on informal caregivers of people with dementia: A randomised controlled trial with nested interviews.

BACKGROUND: Dementia rates are rising globally, impacting healthcare systems and society. The care of people with dementia is largely provided by info...

Reevaluating feature importances in machine learning models for schizophrenia and bipolar disorder: The need for true associations.

Skorobogatov et al. developed supervised machine learning models to predict diagnoses and illness states in schizophrenia and bipolar disorder. Howeve...

Facial expression analysis using convolutional neural network for drug-naive and chronic schizophrenia.

OBJECTIVE: Facial images have been shown to convey mental conditions as clinical symptoms. This study aimed to use facial images to detect patients wi...

Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.

Thyroid illness encompasses a range of disorders affecting the thyroid gland, leading to either hyperthyroidism or hypothyroidism, which can significa...

A deep learning approach for automated scoring of the Rey-Osterrieth complex figure.

Memory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey-Osterrieth complex figure (ROCF) is the state-of-the...

Public Perception on Artificial Intelligence-Driven Mental Health Interventions: Survey Research.

BACKGROUND: Artificial intelligence (AI) has become increasingly important in health care, generating both curiosity and concern. With a doctor-patien...

STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data.

: Early diagnosis of depression is crucial for effective treatment and suicide prevention. Traditional methods rely on self-report questionnaires and ...

Exploring new scientific innovations in combating suicide: a stress detection wristband.

There is a silent pandemic of suicides around the world, with an exponential increase in suicidality and chronic suicidal ideations. The exact global ...