ADHD/ADD

Mortality Risk Prediction from Dense PICU Data from Patients with Suspected Infection: Data-Derived Physiological Trajectories Outperform Expert Assessments When Temporal Resolution Is High.

OBJECTIVE: Early identification of mortality risk in critically ill children with suspected infection remains challenging. Whether severity scores (SIRS, pSOFA, Phoenix) add incremental value beyond data-derived physiologic features in machine-learning models is uncertain. We compared the predictive value of physiologic dynamics, treatment features, and continuous laboratory trends expert-derived ...

Prenatal exposure to residential greenness and artificial light at night and neurodevelopmental outcomes in children aged 4-12 years: A retrospective cohort study in China.

BACKGROUND: Neurodevelopmental outcomes may be shaped by modifiable residential environmental exposures during pregnancy; however, in rapidly urbanizing contexts, evidence on prenatal greenness and artificial light at night (ALAN) across multiple neurodevelopmental outcomes, including critical exposure windows and spatial characteristics, remains limited. METHODS: We analyzed 814 children aged 4-1...

A customized AI-based machine learning model for evaluating participants' activities under a workshop setting.

Human action recognition (HAR) in the workshop-style environment poses unique challenges due to class imbalance, fused action boundaries, and context ...

LIAUNet: Rethinking the Application of Loss Information in Medical Image Segmentation.

In recent years, with the development of medical imaging and deep learning technologies, medical image segmentation has played a crucial role in assis...

ADHD medications and preadolescent brain structure: patterns of cortical attenuation from the ABCD study.

Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in the U.S. Amphetamine (AMP), methylphenidate (MPH), a...

Fine-grained classroom behavior monitoring for students with special education needs: an audiovisual fusion approach to posture analysis.

A core challenge of inclusive education lies in the difficulty teachers face in effectively identifying and responding to the diverse behavioral manif...

Simulation-Enhanced Learning in Nuclear Medicine: Counterpoint.

Simulation-based education has become widely embedded in health professional training and is increasingly positioned as a solution to clinical placeme...

Prediction of treatment response and adverse events in pediatric ADHD treated with acupuncture combined with conventional treatment.

INTRODUCTION: To develop and externally validate machine learning (ML) models for predicting treatment response and adverse events in children with at...

Behavioral Inhibition Network Predicts Alcohol Use in Men and Stress in Women.

OBJECTIVE: Impulsivity, a complex construct linked to addictions, is often inconsistently assessed and conceptualized, making it difficult to effectiv...

Language network functional connectivity in infancy predicts developmental language trajectories.

Although developmental language delays affect approximately 10% of children in the general population, the neurodevelopmental mechanisms that support ...

How to quickly determine whether patients with chronic cough need corticosteroid treatment--construction of a predictive model for corticosteroid-responsive cough in chronic cough.

Patients with chronic cough need to undergo a wide range of tests and rely on empirical medication to determine the underlying cause. Corticosteroid-r...

Analysis of electroencephalogram microstates and spectral power in children and adolescents with attention deficit hyperactivity disorder.

Attention-Deficit/Hyperactivity Disorder (ADHD) is a widely recognized neurodevelopmental disorder characterized by inattention, hyperactivity, and im...

Machine learning-based screening model for Tic disorders comorbid with attention-deficit/hyperactivity disorder in children and SHAP value interpretation: a retrospective observational study.

Tic disorders (TD) are common neurodevelopmental conditions in children and adolescents, characterized primarily by motor and vocal tics, with heterog...

Deep chemical structure graph learning deciphers the lipotoxicity code of hypertriglyceridemic pancreatitis.

The discrepancy between serum triglyceride levels and the clinical severity of hyperlipidemic acute pancreatitis (HLAP) complicates risk stratificatio...

Artificial Intelligence in Neuroradiology: A Review of FDA-regulated Algorithms.

Artificial intelligence (AI) is increasingly integrated into neuroradiology practice, with a growing number of FDA-cleared algorithms now supporting t...

Classification of familial and non-familial ADHD using auto-encoding network and binary hypothesis testing.

Family heritage is one of the most powerful risk factors for attention-deficit/hyperactivity disorder (ADHD). Children with familial ADHD (ADHD-F) and...

Vision-language models as an integrative layer for clinical artificial intelligence in radiology: A systems-level perspective.

Clinical adoption of artificial intelligence (AI) in radiology has matured through task-specific tools for detection, segmentation, triage, and quanti...

Gated edge-node interaction graph convolutional network for ADHD classification.

Objective.Attention deficit hyperactivity disorder (ADHD) remains challenging to diagnose objectively and often relies on subjective clinical assessme...

Stabilizing neuromorphic ECG processors via adaptive fractional fatigue.

Spiking Neural Networks (SNNs) deployed on wearable devices can exhibit runaway firing when processing noisy electrocardiogram (ECG) signals, increasi...

Altered EEG markers of reward learning during abstinence in alcohol dependence: A probabilistic reversal learning study.

OBJECTIVE: This study investigated neurophysiological and behavioural adaptations in reward learning and decision making which may contribute to the d...