ADHD/ADD

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 for enhancing attention among individuals with attention-deficit hyperactivity disorder (ADHD). However, existing systems often lack adaptive difficulty regulation and rely solely on single-modal assessments, thereby limiting personalization and susta...

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...

ADBrainNet: a deep neural network for Autism Spectrum Disorder (ASD) and Attention Deficit and Hyperactivity Disorder (ADHD) classification using resting-state fMRI images based on explainable artificial intelligence.

Autism Spectrum Disorder (ASD) and Attention Deficit and Hyperactivity Disorder (ADHD) are two psychiatric disorders frequently encountered in childre...

Leveraging generative AI to transform statistical analysis plan authoring in clinical trials.

BackgroundStatistical analysis plans are critical regulatory documents that define the statistical methodology, objectives, and data-handling procedur...

AI-Powered Ambient Scribe Technology Experiences Among Emergency Physicians: Cross-Sectional, Mixed Methods Pilot Survey Study.

BACKGROUND: Health care organizations have started to implement artificial intelligence-powered ambient scribe technology in clinical documentation wo...

Computational frameworks for automated detection and quantification of paroxysmal sympathetic hyperactivity among traumatic brain injury patients.

Paroxysmal sympathetic hyperactivity (PSH) is a syndrome that occurs in a large subset of critically ill traumatic brain injury (TBI) patients and is ...

[Cerebral neuromodulation in neurodevelopmental disorders].

Neurodevelopmental disorders comprise a heterogeneous group of conditions characterized by earlyonset impairments in cognitive, behavioral, emotional,...

Single-nucleus multi-omics dissection of dysregulated trophoblast development and disrupted immune microenvironment in complete hydatidiform moles.

Hydatidiform mole (HM) is driven by aberrant trophoblast proliferation, disrupting embryonic development and leading to pregnancy loss with increased ...

Artificial intelligence for personalized multiple micronutrient supplementation in maternal health.

Maternal undernutrition and micronutrient deficiencies remain pervasive, contributing to adverse pregnancy outcomes and long-term health risks for mot...

Voices of change: associations between vocal markers and symptoms of ADHD - Findings from the LIFE child study.

In the context of precision psychiatry, voice characteristics in psychiatric disorders have significant potential as diagnostic markers. However, most...

Using machine learning to identify the strongest personal, school, and family correlates of youth mental health and well-being.

Research has identified numerous personal, school, and family factors associated with youth mental health, yet it remains unclear which correlates bes...

Attention-Enhanced Temporal and Spatial Feature Extraction Network for ADHD Diagnosis based on fMRI.

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder, and accurate diagnosis is critical for ensuring timely int...