Pediatrics

Deep Learning-Based Classification for Grading of Respiratory Distress Syndrome on Neonatal Chest Radiographs.

OBJECTIVE: To develop and validate a deep learning-based multi-class classification model for automated grading of respiratory distress syndrome (RDS) severity on neonatal chest radiographs. METHODS: A total of 23,210 radiographs, including normal and RDS cases, manually annotated by trained neonatologists, were divided into training, validation, and external test sets using patient-level splittin...

Coming of Age With Generative Artificial Intelligence: The High Stakes of Automating Adolescent Development.

Generative artificial intelligence (GenAI) is rapidly transforming the context in which adolescents develop, presenting both opportunities and risks. Adolescence is a sensitive life period during which the accomplishment of key psychosocial developmental tasks-including social belonging, emotion regulation, autonomy, and identity formation-leads to lifelong consequences. Here, we synthesize interd...

Community-based paediatric eye care in low- and middle-income countries: bridging the gap between policy and practice.

Childhood visual impairment and blindness remain major public health concerns in low- and middle-income countries (LMICs), despite a substantial propo...

Artificial Intelligence Answering Patient Questions About Developmental Dysplasia of the Hip: Accuracy, Readability, and Clinical Utility.

INTRODUCTION: Artificial intelligence (AI) tools are increasingly used by patients and caregivers seeking medical information. Developmental dysplasia...

Machine learning-assisted design of a dendritic cell nanovaccine inducing twin immunity against monkeypox.

The global emergence of monkeypox virus (MPXV) has created an urgent need for effective vaccines that induce robust immune responses. Here, we report ...

Associations of Phthalate, Organophosphate Flame Retardant, and Bisphenol Exposures with Lifestyle Behaviors and Residential Environmental Factors Among Early Adolescents: The Hokkaido Study.

Humans are widely exposed to environmental chemicals, yet evidence integrating multiple behaviors and environmental factors remains limited, particula...

Machine Learning-Based Risk Stratification Tool for Hearing Loss in High-Risk Neonates.

OBJECTIVE: This study aims to develop and evaluate a machine learning-based risk stratification tool for predicting hearing loss in high-risk neonates...

When Deepfakes Meet Herd Immunity: Artificial Intelligence, Vaccine Avoidance, and the Next Infodemic Threat.

Vaccination remains among the most effective public health interventions, but its impact increasingly depends on public trust and a reliable informati...

Completely free-breathing cardiac MRI using deep learning reconstruction reduces sedation and scan time in children.

BACKGROUND: Traditional cardiac magnetic resonance (CMR) imaging scan times are long and require breath-holds, often necessitating intubation and mech...

Beyond Accuracy: Defining the Role of Artificial Intelligence in Pediatric Electrocardiography.

Naïve adaptive immune receptor repertoires in celiac disease assessed by machine learning; impact of the HLA-DQ2.5 allotype on the TCR repertoire.

The adaptive immune receptor repertoire (AIRR) - the collection of an individual's B-cell and T-cell receptors (BCRs and TCRs, respectively) - encodes...

Trends and hotspots in artificial intelligence applications for atherosclerosis research: A bibliometric analysis.

BACKGROUND: Atherosclerosis (AS) is a complex systemic, immune-inflammatory vascular disease, and there is an urgent need to innovate its diagnostic a...

Model-guided design of defined microbial community reveals interactions underpinning plant growth and stress tolerance.

Defined microbial communities (DMCs; also known as SynComs) offer a promising strategy to enhance plant growth and stress tolerance by harnessing bene...

Machine Learning-Based First-Trimester Antenatal Risk Prediction for Adverse Maternal and Neonatal Outcomes: Multicenter Model Development Study.

BACKGROUND: Maternal outcomes remain inequitable worldwide. Severe morbidity persists, and current risk assessment tools are largely arbitrary, focusi...

Machine learning-based prediction model for postoperative blood transfusion in pediatric patients with Hirschsprung's disease.

BACKGROUND: Children undergoing Hirschsprung's disease (HD) surgery are at risk of postoperative transfusion, which may cause complications and increa...

From Human-Centered Results to Real-World Impact: Medical Humanities Cross-disciplinary Research.

This collaboratively written paper presents three case studies where researchers from the humanities and from biomedicine collaborated on topics relat...

AI-Based Approaches to Refugee Mental Health Care: Systematic Integrative Review.

BACKGROUND: Refugees and forcibly displaced populations experience elevated rates of mental health conditions, including posttraumatic stress disorder...

Can host genetics transform the sustainable control of tropical theileriosis? Insights from the Tick-Theileria interface.

Tropical theileriosis, caused by the tick-transmitted apicomplexan parasite Theileria annulata, remains a major constraint on cattle production across...

Improved Deep Learning Segmentation of Pediatric Diffuse Midline Gliomas After Treatment.

PURPOSE: To develop and validate a pediatric diffuse midline glioma (DMG) auto-segmentation tool optimized for longitudinal treatment response assessm...

Machine Learning Modeling for Predicting Mortality in Pediatric Patients Undergoing Elective Noncardiac Surgery: Comparison to a Regression Model.

BACKGROUND: Perioperative mortality in children is relatively rare; however, accurate preoperative risk stratification is critical, as it enables anti...