Latest AI and machine learning research in adhd/add for healthcare professionals.
We aim to present recent advancements in predictive markers for lymphomagenesis in SjD, concisely organize existing knowledge, and identify corresponding unmet needs and future perspectives. First, we briefly describe the mechanisms of lymphomagenesis in SjD. Followingly, the reasons justifying the importance of early lymphoma diagnosis in SjD are presented. Subsequently, recent advancements regar...
This study introduces a novel multilingual dataset designed to distinguish auto-tuned musical compositions from authentic recordings, addressing a significant gap in existing resources. The dataset encompasses songs in English, Mandarin, and Japanese, ensuring a diverse representation of linguistic contexts. The data collection process began with aggregating diverse datasets from the Music Informa...
What was done? A review of artificial intelligence (AI) applications for the imaging of uterine fibroids, endometriosis, and adenomyosis. What was fou...
BACKGROUND: Attention-deficit hyperactivity disorder (ADHD) is a multifactorial and complex neurodevelopmental disorder. Prevalence of ADHD in the gen...
The effect of psychostimulant medication in ADHD on the gut microbiome remains unknown. Oral Synbiotic 2000, comprising multiple lactic acid bacteria ...
Developmental dysplasia of the hip (DDH) causes preventable morbidity when diagnosis is delayed. We review advances that address screening gaps: 3-dim...
The application of machine learning algorithms to daily diary data represents a valuable tool for improving dynamic prediction of posttraumatic stress...
OBJECTIVE: Existing deep learning (DL) approaches for assessing temporomandibular disorders (TMD) are limited by underutilization of magnetic resonanc...
Graph neural networks (GNNs) have shown potential in analyzing brain functional networks for neuropsychiatric disorder diagnosis, yet existing GNN-bas...
BACKGROUND: There is increasing global concern about the harms associated with problematic usage of the internet (PUI) affecting young people. Various...
Clinical practice guidelines (CPGs) are essential tools that facilitate the translation of the growing body of scientific evidence into clinical pract...
BACKGROUND: Existing models that use clinical history and cardiac imaging data remain inadequate for accurate prediction of the success of catheter ab...
Faces created by artificial intelligence (AI) are now considered indistinguishable from real faces. Still, humans vary in their ability to detect thes...
Delirium is a common acute neuropsychiatric syndrome, and its early detection may improve clinical outcomes. This narrative review synthesized finding...
INTRODUCTION: Identifying patient characteristics predictive of treatment response is crucial for optimizing type 2 diabetes outcomes. Using data from...
BACKGROUND: Integrating neuroimaging genetics data may enhance the diagnosis and understanding of adults with attention-deficit/hyperactivity disorder...
PURPOSE: In this short communication, we consider the need for explainable AI from the perspective of a large multi-disciplinary research project for ...
BACKGROUND: To elucidate the research progress on acupuncture therapy as a complementary and alternative medicine treatment for depression by reviewin...
BACKGROUND: This study elucidates the intricate relationship between stressful life events and the development of ADHD symptoms in children, acknowled...
Identifying the potential miRNA-disease association (MDA) has a greater impetus to the development of drug prevention, treatment and other fields. For...