Pediatrics

Predicting Adolescent Response to School-Based Mindfulness: A Secondary Analysis of the MYRIAD Trial.

IMPORTANCE: Depression most commonly first emerges during adolescence, making early prevention criti...

Ready-to-Use Polymerization Simulations Combining Universal Machine Learning Interatomic Potential with Time-Dependent Bond Boosting for Polymer and Interface Design.

Although polymerization and curing reactions govern the performance of advanced materials, their sim...

Developing a Service Quality Index System for AI Health Care Chatbots: Mixed Methods Study.

BACKGROUND: Artificial intelligence (AI) health care chatbots are gaining widespread adoption worldw...

Examining Artificial Intelligence Chatbots' Responses in Providing Human Papillomavirus Vaccine Information for Young Adults: Qualitative Content Analysis.

BACKGROUND: The growing use of artificial intelligence (AI) chatbots for seeking health-related info...

Machine learning-driven flavoromics: Decoding stage-specific volatile compound dynamics and sensory deterioration in stored infant formula.

Infant formula (IF) is a vital nutritional source for infants. However, its sensory quality deterior...

Abnormal Lipid Signaling Characterizes Diastolic Dysfunction in Pediatric Cardiomyopathy.

Pediatric cardiomyopathy is a major cause of diastolic heart failure; yet, its mechanisms remain unc...

Predicting mycophenolic acid exposure in pediatric lupus nephritis: a real-world study across three clinical scenarios.

OBJECTIVES: Childhood-onset systemic lupus erythematosus (cSLE) with lupus nephritis (LN) presents t...

Artificial intelligence meets point-of-care ultrasound: implications for pediatric emergency and critical care.

PURPOSE OF REVIEW: Artificial intelligence has been integrated in nearly all aspects of clinical car...

Barriers to implementing AI in pediatric cancer imaging.

Gross motor performance of infants with an at-home wearable measurement and the Alberta Infant Motor Scale: A concurrent validity study.

AIM: To evaluate the concurrent validity of assessing infants' gross motor performance with an at-ho...

Machine Learning Methods to Identify Pediatric Shunt Malfunction in the Acute Care Setting and the Development of ShuntGPT.

BACKGROUND AND OBJECTIVES: Identifying predictors of shunt failure in acute care settings is challen...

Epidemiological Trajectories and Quality of Care Disparities of MASLD in Asia, 1990-2023.

BACKGROUND: To characterize temporal and geographic patterns of metabolic dysfunction-associated ste...

Leveraging Machine Learning to Uncover Ethnic-Specific Predictors of Maternal Postpartum Depression.

Maternal postpartum depression (PPD) is a significant mental health issue that affects over 15% of m...

Corrigendum to "Deep learning in fetal, infant, and toddler neuroimaging research"[Dev. Cognit. Neurosci. (2026), 101680].

Peripheral Retinal Vessel Density in Children and Adolescents with Myopia: A Deep Learning Assessment.

INTRODUCTION: This study investigated the effect of axial length (AL) on peripheral retinal vessel d...

Development, Validation, and Comparison of Machine Learning Models for Predicting Pediatric Surgical Site Infections Using the NSQIP-P Database.

BACKGROUND: Surgical site infections (SSIs) cause substantial postoperative morbidity in children. D...

Neuroprognostication via Radiomics and Machine Learning Following Neonatal Hypoxic-Ischemic Insult.

OBJECTIVE: To produce objective predictions of neurodevelopmental outcomes after perinatal hypoxic-i...

Minor salivary gland biopsy in Sjogren's disease: a diagnostic and prognostic tool toward precision medicine.

Minor salivary gland biopsy occupies a distinctive position in the evaluation of Sjögren disease (Sj...

Deep learning-based cerebellar segmentation on T2-weighted fluid-attenuated inversion recovery magnetic resonance imaging for detecting cerebellar hypoplasia/atrophy in infants.

BACKGROUND: Accurate measurement of cerebellar volume is crucial for diagnosing cerebellar atrophy o...

A modern radiologist's guide to artificial intelligence.

Artificial intelligence (AI) has the potential to disrupt many fields, and radiology is no exception...

ContraIndicator: A Natural Language Processing-Based Approach to Potential Drug-Drug Interaction Detection in Pediatric Intensive Care.

BACKGROUND AND OBJECTIVES: The intricate nature of pediatric patients' physiology and the frequent a...