ADHD/ADD

The role and utility of artificial intelligence and machine learning for diagnostic prediction in general practice.

Diagnostic prediction models are commonly used in general practice to support clinical decision-making. Traditionally, these models have been developed using statistical methods such as logistic regression. While these approaches have proven useful, they often produce average risk estimates that may not fully account for the complexity of individual patients. In recent years, the use of machine le...

Deep residual network enhanced with multilevel residual-of-residual for automatic classification of radio signals for 5G and beyond systems.

Automatic Modulation Classification (AMC) plays a critical role in the design of intelligent receivers for next-generation wireless systems, particularly in the context of 5G and beyond networks characterized by diverse multicarrier waveform technologies. This paper proposes a novel AMC framework based on a Deep Residual Network (DRN) architecture enhanced with multilevel Residual-of-Residual (RoR...

Classification and discrimination of emotion dysregulation disorders using machine learning.

Attention-deficit/hyperactivity (ADHD), bipolar (BD) and borderline personality (BPD) disorders are severe psychiatric illnesses often presenting with...

Machine learning prediction of food addiction in university students using demographic, anthropometric and personality traits.

People's eating habits are influenced by psychological, social, cultural, and behavioral factors. Research shows that certain personality types expose...

Development and multi-center validation of a school-home integrated machine learning model for early screening of attention-deficit/hyperactivity disorder in school-aged children.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is frequently under-identified in community settings, delaying necessary intervention. Thi...

Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.

Artificial intelligence (AI) is entering routine radiology practice, but most studies evaluate algorithms in isolation rather than their interaction w...

Trust at risk: Why public health must lead the use of AI in pandemic preparedness.

Artificial intelligence (AI) is poised to play a transformative role in pandemic preparedness, with the potential to enhance surveillance, risk assess...

Syntactic complexity of written text as a structural linguistic marker of depressive symptoms.

BACKGROUND: Language analysis methods have been increasingly explored for the detection of depressive symptoms. However, current approaches have large...

White matter microstructure predicts effort and reward sensitivity.

From rodents to humans, animals constantly face a central question: is the reward worth the effort? Effort and reward sensitivity in such situations v...

On machine learning based QSPR analysis of amphetamine derivatives using regression models.

In this study, we show the quantitative structure-property relationship (QSPR) for amphetamine derivatives based on neighborhood degree-based topologi...

Markers predicting lymphoma development in Sjögren disease: current status and future perspectives.

We aim to present recent advancements in predictive markers for lymphomagenesis in SjD, concisely organize existing knowledge, and identify correspond...

ATDD: Multi-lingual dataset for auto-tune detection in music recordings.

This study introduces a novel multilingual dataset designed to distinguish auto-tuned musical compositions from authentic recordings, addressing a sig...

Artificial Intelligence in Gynecologic Imaging.

What was done? A review of artificial intelligence (AI) applications for the imaging of uterine fibroids, endometriosis, and adenomyosis. What was fou...

Examining attention- deficit/ hyperactivity disorder in endurance and ultra-endurance runners.

BACKGROUND: Attention-deficit hyperactivity disorder (ADHD) is a multifactorial and complex neurodevelopmental disorder. Prevalence of ADHD in the gen...

Altered gut microbiome function in ADHD: More Prevotella, less vitamin B12 biosynthesis, and beneficial modulation by synbiotic treatment.

The effect of psychostimulant medication in ADHD on the gut microbiome remains unknown. Oral Synbiotic 2000, comprising multiple lactic acid bacteria ...

Game-Changing Advances in Infant Hip Dysplasia Evaluation With Ultrasound.

Developmental dysplasia of the hip (DDH) causes preventable morbidity when diagnosis is delayed. We review advances that address screening gaps: 3-dim...

Precision prediction of posttraumatic stress disorder symptom surges: A pilot study integrating real-time daily data with supervised learning.

The application of machine learning algorithms to daily diary data represents a valuable tool for improving dynamic prediction of posttraumatic stress...

M44TMD: A multimodal, multi-task deep learning framework for comprehensive assessment of TMD-related abnormalities.

OBJECTIVE: Existing deep learning (DL) approaches for assessing temporomandibular disorders (TMD) are limited by underutilization of magnetic resonanc...

ADHD diagnosis and biomarker detection based on multimodal graph attention convolutional neural network.

Graph neural networks (GNNs) have shown potential in analyzing brain functional networks for neuropsychiatric disorder diagnosis, yet existing GNN-bas...

Research protocol for BootStRaP assessment phase: A nine-nation study on boosting societal adaptation and mental health in a rapidly digitalising, post-pandemic Europe.

BACKGROUND: There is increasing global concern about the harms associated with problematic usage of the internet (PUI) affecting young people. Various...