Pediatrics

Visual representation bias in artificial intelligence-generated depictions of anesthesia, pain, and intensive care professionals.

ObjectiveTo evaluate the demographic and hierarchical representation biases in artificial intelligence-generated depictions of healthcare professionals in anesthesiology, pain medicine, and intensive care, with a focus on sex, age group, skin tone, ethnicity, and professional role hierarchy.MethodsThis cross-sectional comparative study analyzed 4400 artificial intelligence-generated images produce...

Can LLMs reliably perform epidemiological extraction in elite football?

Anterior cruciate ligament (ACL) injuries remain a critical burden in professional football, resulting in prolonged absence and high economic costs. While physical demands have increased, traditional epidemiological surveillance relies on manual data extraction, which is resource-intensive and subjective. Large Language Models (LLMs) offer a potential solution for automated screening, but their re...

Non-invasive differentiation of diffuse midline glioma and midline glioblastoma using DCE-MRI perfusion parameters and machine learning classification in pediatric and young adult patients.

PURPOSE: Diffuse midline glioma (DMG) and midline glioblastoma (mGBM) are aggressive WHO grade 4 tumors with comparable median survival of 12-18 month...

Are artificial intelligence systems ready for pediatric surgical decision-making? A comparative evaluation of large language models versus pediatric surgeons.

PURPOSE: This study aimed to compare the diagnostic, investigative, and treatment decisions generated by five contemporary large language models (LLMs...

High-resolution 3D micro-CT reveals an additional muscle layer in esophageal atresia.

BACKGROUND: Esophageal atresia (EA) is a congenital malformation intrinsically associated with esophageal dysmotility. Its etiology is poorly understo...

Pediatric Use of Artificial Intelligence for Support and Advice-Developmental Practice or Help-Seeking Substitution?

Evolution of Scientific Trends in Oral and Maxillofacial Surgery in the Last Decade.

BACKGROUND: Oral and maxillofacial surgery (OMFS) has undergone rapid scientific evolution over the past decade, driven by technological innovation an...

Advancements in Pediatric Moyamoya Disease: A Critical Review of Surgical Decision-Making, Outcomes, and Future Directions.

Pediatric moyamoya disease (MMD) is a progressive steno-occlusive arteriopathy that carries a high risk of recurrent ischemic stroke and neurocognitiv...

Early-Life Exposure to Organophosphorus Pesticide Mixtures and Impaired Neonatal Neurodevelopment: A Cohort Study Integrating Comprehensive Multi-Omics and Machine Learning.

Early-life exposure to low-dose organophosphorus pesticide (OPP) mixtures and their impact on neonatal neurodevelopment remain insufficiently characte...

Accelerated Cardiac MRI Using Deep Learning Cine Imaging: Validation in Pediatric Patients both with and without Congenital Heart Disease.

BACKGROUND: Conventional breath-held 2D bSSFP cine CMR requires multiple breath holds and prolonged acquisition time, posing challenges for children a...

Artificial Intelligence in Neuromuscular Diseases: Opportunities for a Data-Scarce Field.

Neuromuscular diseases (NMDs) encompass over 800 distinct entities affecting approximately one in 1000 individuals worldwide, with progressive muscle ...

Artificial intelligence for translational personalized neoantigen cancer vaccine development.

Personalized neoantigen cancer vaccine is a promising strategy for precision immunotherapy by targeting patient-specific and mutation-derived tumor an...

Cell surface CD11c as a neutrophil aging marker molecule.

Neutrophils exhibit substantial functional heterogeneity shaped by both developmental stage and cellular maturation, but their mechanisms remain incom...

Integration of Bulk RNA Sequencing and Single-Cell Sequencing to Identify Prognostic Genes Associated With MCDRGs in Neuroblastoma.

Neuroblastoma (NB) is a common pediatric tumor that exhibits significant clinical heterogeneity and substantial prognostic variability. However, there...

Machine learning for prediction of large-for-gestational-age neonate with adverse outcome at routine ultrasound examination at 35-37 weeks.

OBJECTIVES: To evaluate the performance of a machine-learning (ML) model compared with traditional logistic regression models for predicting a large-f...

Using Artificial Intelligence to Improve Question Asking in Autistic Adolescents and Adults.

This research assessed whether an artificial intelligence (AI) program could support autistic adolescents and adults with question asking, and whether...

Clinical and Metabolomic Panel for Noninvasive Screening of Biopsy-Confirmed MASH in Children and Adolescents.

BACKGROUND & AIMS: Noninvasive tests to identify pediatric metabolic dysfunction-associated steatohepatitis (MASH) remain a critical need. We aimed to...

Development of a machine-learning risk stratification tool for vasoactive medication need after two-bolus fluid resuscitation in pediatric suspected sepsis.

BACKGROUND: Timely vasopressor initiation is critical in fluid-refractory pediatric septic shock, yet clinicians lack objective tools to identify chil...

Myocardial Fibrosis in Pediatric Heart Disease: Mechanisms, Imaging Biomarkers, Clinical Consequences, and Emerging Antifibrotic Therapies.

Myocardial fibrosis is increasingly recognized as a central determinant of adverse cardiac remodeling and long-term cardiovascular outcomes in childre...

Structural Heart Interventions in Women with Congenital Heart Disease.

PURPOSE OF REVIEW: To provide a review of current transcatheter interventions in women with congenital heart disease, with a focus on sex-specific con...