INTRODUCTION: Khat chewing is a significant public health issue in Ethiopia, influenced by various demographic factors. Understanding the prevalence and determinants of khat chewing practices is essential to developing targeted interventions. Therefo...
OBJECTIVES: This study aimed to enhance clinical diagnostics for quantitative cervical vertebral maturation (QCVM) staging with precise landmark localization. Existing methods are often subjective and time-consuming, while deep learning alternatives ...
Interest is not only the starting point to begin a wonderful learning journey for students, but also an important driver for deep learning and continuous progress. This study used latent profile analysis (LPA), multiple logistic regression analysis, ...
OBJECTIVE: To develop a stratified screening tool through machine learning approaches for the Center for Epidemiologic Studies Depression Scale (CES-D-20) while maintaining diagnostic accuracy, addressing the efficiency limitations in large-scale app...
Technology dependence has long been a critical public health issue, especially among young people. With the development of AI chatbots, many individuals are integrating these tools into their daily lives. However, we have limited knowledge about the ...
Artificial intelligence (AI) attitude scales can be used to better evaluate the benefit and drawback cons of AI. This article consists of two different studies examining attitudes towards AI. In Study I (N = 370), the four-item Artificial Intelligenc...
This study explores using dual-modal sensory data and machine learning to objectively identify Attention-Deficit/Hyperactivity Disorder (ADHD), a neurodevelopmental disorder traditionally diagnosed through subjective clinical evaluations. Six machine...
BACKGROUND: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/hyperactivity disorder (ADHD). However, these reports can have several pr...
BACKGROUND: This study develops a deep learning-based automated lesion segmentation model for whole-body 3DF-fluorodeoxyglucose (FDG)-Position emission tomography (PET) with computed tomography (CT) images agnostic to disease location and site.
Research with controlled or crossover designs in animal-assisted therapy have largely used control groups receiving no treatment or treatment as usual, which can potentially inflate the effects of these interventions. It is therefore not always clear...