ADHD/ADD

Measuring Accuracy (Classification Probabilities, Positive, and Negative Predictive Values) of Executive Function Electroencephalogram Metrics in Attention-Deficit/Hyperactivity Disorder Diagnosis: Protocol for and Perspectives From the SINCRONIA Study.

BACKGROUND: Attention deficit/hyperactivity disorder (ADHD) is the most prevalent neurodevelopmental disorder worldwide, affecting approximately 5%-7% of school-aged children and 2%-5% of adults worldwide. However, there is still no reliable diagnostic tool for it. The lack of specific biomarkers further complicates the accurate diagnosis of ADHD. OBJECTIVE: The SINCRONIA study seeks to develop an...

Comparison of cognitive ability and its distribution between men with autism spectrum disorder and attention-deficit/hyperactivity disorder.

OBJECTIVES: Clarification of the strengths and weaknesses of cognitive ability is essential to our understanding of the characteristics of autism spectrum disorder and attention deficit/hyperactivity disorder. However, whether individuals with these conditions exhibit distinct patterns of cognitive ability remains unclear. To address this point, we aimed to compare the cognitive profiles of patien...

3D ADHD-Net and DeepTrace: Decoding ADHD from EEG with neurophysiological insights.

EEG-based ADHD diagnosis models suffer from two persistent issues: data leakage and the lack of physiologically grounded interpretability, limiting cl...

Optimized Complex-Valued Spatio-Temporal Graph Convolutional Networks for attention deficit hyperactivity disorder detection in pediatric EEG signals.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition affecting mood, anxiety, learning, and sleep. Electroencephalogram (...

Neuroanatomy reflects individual variability in impulsivity in youth.

Individual differences in neural circuits underlying emotional regulation, motivation, and decision-making are implicated in many psychiatric illnesse...

Digital diagnostics, biomarkers and therapeutics in an evolving healthcare system: From promise to practice.

Health care is shifting towards a digital-guided system, integrating digital diagnostics, biomarkers and therapeutics in many care pathways. However, ...

High diagnostic accuracy of a resnet50-based deep learning model for osteochondral lesions of the talus on magnetic resonance imaging.

OBJECTIVES: This study aims to evaluate the diagnostic performance of a ResNet50-based convolutional neural network (CNN) in detecting osteochondral l...

Targeting autophagy in Duchenne muscular dystrophy: mechanistic insights and emerging therapeutic strategies.

Duchenne muscular dystrophy (DMD) is a severe X-linked myopathy characterised by progressive skeletal and cardiac muscle degeneration, loss of ambulat...

Better together: what can state-of-the-art ML models add to thyroid nodule diagnostics?

Perspectives on How Sociology Can Advance Theorizing About Human-Chatbot Interaction and Developing Chatbots for Social Good.

Recently, research into chatbots (also known as conversational agents, artificial intelligence agents, or voice assistants), which are computer applic...

Do machine learning models add value in assessing operative stress?

A neurofeedback-guided EEG and BCI framework for personalized attention rehabilitation in ADHD.

The integration of game-based cognitive training with electroencephalography (EEG)-based brain-computer interaction (BCI) has demonstrated potential f...

Sleep awake detection from leg-worn wearables using deep sensor fusion.

Restful sleep is essential for health, yet many children with Attention Deficit Hyperactivity Disorder (ADHD) experience disturbances such as delayed ...

BRAF inhibitor resistance in melanoma: from resistance mechanisms to therapeutic innovations.

BRAF inhibitors (BRAFi) have transformed the treatment of BRAF mutant melanoma, but inherent and acquired resistance remains a major barrier to curati...

Locally Linear Continual Learning for Time Series based on VC-Theoretical Generalization Bounds.

Most machine learning methods assume fixed probability distributions, limiting their applicability in nonstationary real-world scenarios. While contin...

GOLD 2026: Transforming COPD Management with Early Intervention, Multi-dimensional Assessment, and Personalized Care.

The 2026 report from the Global Initiative for Chronic Obstructive Lung Disease (GOLD) introduces substantial conceptual and practical updates to the ...

Deep Brain Stimulation Induces Antidepressant Effects by Restoring High-Fidelity Communication in the BNST-NAc Circuit.

Deep brain stimulation (DBS) for treatment-resistant depression (TRD) is challenged by significant individual variability in efficacy and unclear neur...

Machine learning approaches to identifying neurodevelopmental disorders using social media data: a systematic review.

BACKGROUND: Neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), often eme...

SMDRnet: Saliency multiscale dense residual network for multimodal medical image fusion.

BACKGROUND: Medical image fusion methods can provide more comprehensive and precise details about the internal structures of the human body by integra...

Integrating the Glycemia Risk Index Into Clinical Practice and Research: A Consensus Report.

A panel of experts in the use of continuous glucose monitoring (CGM) data in the treatment of diabetes met in Burlingame, California on October 27, 20...