Pediatrics

Automating Motivational Interviewing Coding in Adolescent Substance Use Prevention: Human-AI Agreement Study.

BACKGROUND: Motivational interviewing (MI) is widely used in preventive interventions, yet coding MI techniques and monitoring intervention adherence remain resource-intensive due to the reliance on manual transcription and expert review. Large language models (LLMs) offer a promising approach to automate these tasks, but their agreement with human coders in the context of prevention interventions...

Development and internal validation of an interpretable machine learning model for predicting vancomycin-induced nephrotoxicity in hospitalized children.

INTRODUCTION: Vancomycin is widely used for severe Gram‑positive infections in children, but vancomycin‑induced nephrotoxicity (VIN) limits its safe application. Existing prediction models rely primarily on traditional logistic regression with limited discriminative performance in pediatric populations, and machine learning (ML) approaches have not been systematically evaluated. AIM: This study ai...

Machine Learning-Based Prediction of Cycloplegic Refraction Using the Eyerobo Vision Screener: Design-A Cross-Sectional Comparative Device Study.

INTRODUCTION: A cross-sectional comparative device study to compare the Eyerobo Vision Screener (VS), a portable handheld photorefractor, against a co...

Edge-centric brain connectomes reveal two molecularly distinct neurobiological subtypes of adolescent major depressive disorder.

Adolescent major depressive disorder (MDD) is a heterogeneous disorder that complicates diagnosis and treatment. However, the mechanisms underlying th...

A Scalable High-Density Microwell Assay for Single-Cell Clonal Expansion Profiling.

Traditional clonogenic assays remain central to evaluating the self-renewal capacity of tumor cells. However, the assay relies on subjective endpoint ...

MicroRNA profiles as diagnostic tools in pediatric acute lymphoblastic leukemia.

Acute lymphoblastic leukemia (ALL) represents the most common malignancy diagnosed in children, accounting for approximately 25-30% of all pediatric c...

Advancing Apex Detection: a Comparative Study of Deep Learning Models on Panoramic Radiographs.

The open or closed root apex identification is one of the most important factors determining endodontic treatment options in pediatric dentistry. This...

Clinical translation of artificial intelligence in hypospadias: a systematic review and meta-analysis.

Artificial intelligence (AI) has emerged as a promising tool to improve the objectivity and reproducibility of hypospadias assessment. However, eviden...

Unpacking posttraumatic growth: Identifying predictors in trauma-affected individuals using machine learning.

OBJECTIVE: Emotion regulation influences psychological responses to trauma, with event-related rumination affecting the development of posttraumatic s...

Dynamics of infant preshaping: Longitudinal analysis using automated hand pose tracking from home video.

Infant reaching and grasping scaffold opportunities for exploration, learning, and communication, making them central to early development. Yet most p...

Structure-Aware Deep Learning for Pediatric Echocardiographic Standard-View Classification in Ultrasonic Imaging.

Accurate standard-view classification is essential for pediatric echocardiographic image analysis and downstream automated interpretation. This task r...

Digital Health Competence and Attitudes Toward AI Among Health Care Professionals: Convergent Mixed Methods Study.

BACKGROUND: Digital transformation is reshaping health care systems and requires health care professionals to develop advanced digital health competen...

Pre-pandemic preventive healthcare engagement and all-cause excess mortality during COVID-19: a county-level ecological study in the United States.

OBJECTIVE: Community resilience during COVID-19 has been linked to social conditions, public-health capacity, and acute-care resources, but the contri...

Left ventricular hypertrophy as a predictor of adverse maternal and neonatal outcomes in chronic hypertension in pregnancy: a multicenter study.

OBJECTIVE: The impact of chronic hypertension (CHTN) combined with left ventricular hypertrophy (LVH) on adverse maternal and fetal pregnancy outcomes...

Machine learning for precision obstetrics: A comprehensive review of risk prediction models for hypertensive disorders and pregnancy-related syndromes.

Gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), preterm birth, and intrauterine growth restriction represent major con...

MRI Evaluation of Axial Spondyloarthritis: From Early Diagnosis to Therapeutic Response Assessment.

Magnetic resonance imaging (MRI) has reshaped the evaluation of axial spondyloarthritis (axSpA), which comprises radiographic axSpA (historically anky...

Pediatric Cardiology Editors: Insights, Challenges, and Future Growth Opportunities.

Editors of pediatric cardiology journals have an important role at the intersection of clinical science, education, ethics, and academic leadership. T...

Risk Factors for Pediatric Acute Chest Syndrome Utilizing Machine Learning Techniques.

Acute chest syndrome (ACS) causes significant morbidity and mortality in both adult and pediatric patients with sickle cell disease (SCD). Despite kno...

Co-development of AI literacy, agency, enjoyment, and anxiety in EFL learners: A complex dynamic systems perspective.

Artificial Intelligence (AI) tools have been found to influence English language learning. However, there is scant research on the role of AI literacy...

Multimodal Transformer Fusion of Clinical Information, Medical Text, and Pituitary MRI 2.5D Deep Learning Features for Differentiating Growth Hormone Deficiency and Idiopathic Short Stature.

RATIONALE AND OBJECTIVES: To develop a non-invasive, efficient and accurate auxiliary tool for the precise differential diagnosis between pediatric gr...