Psychiatry

GO-MAE: Self-supervised pre-training via masked autoencoder for OCT image classification of gynecology.

Genitourinary syndrome of menopause (GSM) is a physiological disorder caused by reduced levels of oestrogen in menopausal women. Gradually, its symptoms worsen with age and prolonged menopausal status, which gravely impacts the quality of life as well as the physical and mental health of the patients. In this regard, optical coherence tomography (OCT) system effectively reduces the patient's burde...

Evaluation of neonatal nurses' anxiety and readiness levels towards the use of artificial intelligence.

OBJECTIVEC: This is a cross-sectional and descriptive study to determine the levels of artificial intelligence anxiety and readiness of neonatal nurses.

Autism Spectrum Disorder and Atypical Brain Connectivity: Novel Insights from Brain Connectivity-Associated Genes by Combining Random Forest and Support Vector Machine Algorithm.

It is estimated that approximately one in every 100 children is diagnosed with autism spectrum disorder (ASD) around the globe. Currently, there are n...

Investigating the impact of an AI-based play activities intervention on the quality of life of school-aged children with ADHD.

BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder that not only impacts children's behavior, lear...

[Can artificial intelligence help prevent suicide in healthcare workers?].

Identification of cultural conversations in therapy using natural language processing models.

Researchers have historically focused on understanding therapist multicultural competency and orientation through client self-report measures and beha...

Predicting maintenance lithium response for bipolar disorder from electronic health records-a retrospective study.

BACKGROUND: Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and ...

Haves and have-nots: socioeconomic position improves accuracy of machine learning algorithms for predicting high-impact chronic pain.

Lower socioeconomic position (SEP) is associated with increased risk of developing chronic pain, experiencing more severe pain, and suffering greater ...

Federated learning-based prediction of depression among adolescents across multiple districts in China.

Depression in adolescents is a serious mental health condition that can affect their emotional and social well-being. Detailed understanding of depres...

Federated learning and deep learning framework for MRI image and speech signal-based multi-modal depression detection.

Adolescence is a significant period for developing skills and knowledge and learning about managing relationships and emotions by gathering attributes...

Building and testing of a robotic intervention framework to enhancing the social engagement of children with autism spectrum disorder.

PURPOSE: Humanoid robot intervention programmes for children with autism spectrum disorder (ASD) are being developed rapidly. This study aimed to deve...

Predicting remission after acute phase pharmacotherapy in patients with bipolar I depression: A machine learning approach with cross-trial and cross-drug replication.

OBJECTIVES: Interpatient variability in bipolar I depression (BP-D) symptoms challenges the ability to predict pharmacotherapeutic outcomes. A machine...

Video-audio neural network ensemble for comprehensive screening of autism spectrum disorder in young children.

A timely diagnosis of autism is paramount to allow early therapeutic intervention in preschoolers. Deep Learning tools have been increasingly used to ...

Peripheral Blood Mononuclear Cell Biomarkers for Major Depressive Disorder: A Transcriptomic Approach.

Major depressive disorder (MDD) is a complex condition characterized by persistent depressed mood, loss of interest or pleasure, loss of energy or fa...

Using machine learning modeling to identify childhood abuse victims on the basis of personality inventory responses.

Trauma is very common and associated with significant co-morbidity world-wide, particularly PTSD and frequently other mental health disorders. However...

Detection of Low Resilience Using Data-Driven Effective Connectivity Measures.

Conventional thresholding techniques for graph theory analysis, such as absolute, proportional and mean degree, have often been used in characterizing...

Classification of psychosis spectrum disorders using graph convolutional networks with structurally constrained functional connectomes.

This article considers the problem of classifying individuals in a dataset of diverse psychosis spectrum conditions, including persons with subsyndrom...

Fast, variable stiffness-induced braided coiled artificial muscles.

Biomimetic actuation technologies with high muscle strokes, cycle rates, and work capacities are necessary for robotic systems. We present a muscle ty...

Potential promises and perils of artificial intelligence in psychotherapy -The AI Psychotherapist (APT).

OBJECTIVE: Since the release of ChatGPT, popular demand has driven the use of social chatbots as pseudo-AI psychotherapists. With time, it is inevitab...

Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency.

BACKGROUND & OBJECTIVES: Mental health disorders pose an increasing public health challenge worsened by the COVID-19 pandemic. The pandemic highlighte...