Pediatrics

Multicellular, Biochemical, and Perfusion Effects on Vessel Network Morphogenesis in a Microfluidic Vasculature-on-a-Chip.

Microvascular networks (MVNs) formed via endothelial cell self-assembly in 3D hydrogels have emerged as a widely used platform for modeling vascularized tissues and studying vascular pathophysiology. Conventional MVN systems incorporate supporting fibroblasts and may include biochemical cues such as VEGF, FGF, or S1P, as well as mechanical stimuli like luminal flow, yet the impact of these variabl...

Adolescents with non-suicidal self-injury exhibit increased pain empathic neural reactivity and personal distress to physical but not affective pain.

BACKGROUND: Non-suicidal self-injury (NSSI) in adolescents represents a critical public health issue. While symptomatic links between NSSI and alterations in pain and social processing have been established, changes in neural responses and everyday reactivity to others' pain remain unknown. METHODS: This pre-registered study examined pain empathic processing in unmedicated adolescents with NSSI (n...

ADHTransNet-based radiomics on multimodal pituitary MRI for non-invasive hormone prediction in children.

BACKGROUND AND OBJECTIVE: Growth hormone deficiency (GHD) and idiopathic central precocious puberty (ICPP) are typically diagnosed through invasive st...

Identifying diagnostic neuroimaging biomarkers for adolescent major depressive disorder.

BACKGROUND: The increasing incidence of adolescent depression represents a serious public health concern. Despite clear diagnostic criteria, the wide ...

Development and preliminary evaluation of a machine learning model for predicting low birth weight using placental IVIM-MRI and maternal clinical characteristics.

OBJECTIVE: To identify key placental intravoxel incoherent motion (IVIM) MRI parameters and maternal factors associated with low birth weight (LBW), a...

A Review on the Design, Preparation, and Performance Control of Low Dielectric Constant Polybenzoxazine-Based Materials.

With the rapid development of technologies such as 5G/6G communication, artificial intelligence, and high-performance computing, electronic devices ar...

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...

A Fully Automated Multistage Deep Learning System for Lenke Classification: Enhanced Diagnostic Precision in Adolescent Idiopathic Scoliosis.

BACKGROUND: The Lenke classification for adolescent idiopathic scoliosis (AIS) has interobserver variability due to subjective clinical assessment. We...

Global eye care frameworks: a review of strategies, gaps, and recommendations for equitable access.

Access to eye care remains a global health priority, particularly for underserved populations in rural, Indigenous, and low-income communities. Despit...

Neurodevelopmental disabilities among children and adolescents: perspectives and priorities in low- and middle-income countries.

PURPOSE OF REVIEW: To synthesize recent evidence on neurodevelopmental disorders (NDDs) among children and adolescents in low- and middle-income count...

Artificial Intelligence in Adolescent and Young Adult Cancer Care: A Discussion Paper.

PURPOSE: The number of adolescents and young adults (AYAs) with cancer has increased over the past 30 years. Fundamental to this process has been the ...

Closed Loop Computer-based Artificial Intelligence Model "Maverik" to Help Diagnose and Differential Diagnoses of Childhood Onset Chronic Nonbacterial Osteomyelitis: Pilot Study.

BACKGROUND: Childhood-onset chronic nonbacterial osteomyelitis (CNO) is an inflammatory bone disease that has become better defined in the last 2 deca...

Creation of Radiology Teaching Content with STELLA-A STandardized Electronic Learning Library and Application Platform.

PURPOSE: Collections of interesting cases are at the heart of radiology education, but efficient saving and sharing of cases has always been a challen...

Advanced Fetal Cardiac Disease Detection Using Optimized Gegenbauer Graph Neural Networks on Ultrasound Images to Facilitate Early Diagnosis and Clinical Assessment.

Early detection of fetal cardiac diseases can dramatically improve neonatal outcomes by enabling timely intervention and informed clinical management....

Machine learning-based diagnosis of autism spectrum disorder in children and adolescents using eye-tracking data: a systematic review and meta-analysis.

OBJECTIVE: Eye-tracking technology has been increasingly investigated as an objective approach for distinguishing individuals with Autism Spectrum Dis...

Identifying the optimal predictors for adolescent mental and physical health using machine learning methods.

BACKGROUND: Adolescent health is a worldwide concern. Previous research identified many factors of mental and physical health issues, which typically ...

Pediatric asthma management via integration of a remote spirometry device into an EHR-based artificial intelligence-powered clinical decision support system: A feasibility pragmatic clinical trial.

BACKGROUND: Asthma is the most common chronic disease in children. Suboptimal asthma control is prevalent and causes significant health care costs. El...

A Machine Learning Strategy to Predict the Number of High-Acuity Children Who Leave Without Being Seen From the Emergency Department.

OBJECTIVES: The purpose of this study was to create an operationally useful machine learning model that predicts the number of high-acuity left withou...

GenEEG: Improving epileptic EEG detection through patient-adaptive latent diffusion and continual learning.

Automated seizure detection systems face significant challenges due to the limited availability of clinical EEG data, a substantial class imbalance be...

Artificial Intelligence-Based Prediction of Recurrence and Personalized Treatment Planning in Pediatric Pilonidal Sinus Disease.

PurposeThis study aimed to assess the feasibility of artificial intelligence (AI)-based models for predicting recurrence risk and supporting individua...