INTRODUCTION: Dissociation is associated with clinical severity, increased risk of suicide and self-harm, and disproportionately affects adolescents and young adults. Whilst evidence indicates multiple factors contribute to dissociative experiences, ...
Journal of evaluation in clinical practice
Feb 1, 2025
AIMS: This study aims of determine the mediating role of individual innovativeness in the effect of nursing students' artificial intelligence anxiety on their robotic surgery knowledge level.
Journal of evaluation in clinical practice
Feb 1, 2025
RATIONALE: Limited health literacy (HL) leads to poor health outcomes, psychological stress, and misutilization of medical resources. Although interventions aimed at improving HL may be effective, identifying patients at risk of limited HL in the cli...
Journal of evaluation in clinical practice
Feb 1, 2025
BACKGROUND: Cancer patients experience substantial psychological distress which causes the reduction of the quality of life. However, the risk of psychological distress has not been well predicted especially in young- and middle-aged gynaecological c...
Translational vision science & technology
Jan 2, 2025
PURPOSE: The purpose of this study was to develop a deep learning approach that restores artifact-laden optical coherence tomography (OCT) scans and predicts functional loss on the 24-2 Humphrey Visual Field (HVF) test.
Translational vision science & technology
Jan 2, 2025
PURPOSE: This study assessed objective performance, usability, and acceptance of artificial intelligence (AI) by people with vision impairment. The goal was to provide evidence-based data to enhance technology selection for people with vision loss (P...
Translational vision science & technology
Jan 2, 2025
PURPOSE: To evaluate the refractive differences among school-aged children with macular or peripapillary fundus tessellation (FT) distribution patterns, using fundus tessellation density (FTD) quantified by deep learning (DL) technology.
This study aimed to predict suicidal ideation among youth with autism spectrum disorder (ASD) by applying machine learning techniques. A cross-sectional sample of 368 ASD-diagnosed young people (aged 18-24 years) was recruited, and 34 candidate predi...
INTRODUCTION: The current study aimed to develop and validate a machine learning (ML)-based predictive models for early dyslexia detection in children by integrating neurocognitive, linguistic and behavioural predictors.