Pediatrics

Predicting adolescent suicidal tendency in Chinese secondary school students: a machine learning approach with XGBoost and SHAP interpretation.

BACKGROUND: Adolescent suicide is a critical public health issue globally. Early detection of suicidal tendency remains challenging due to its concealed and multidimensional nature. This study aimed to develop and validate an interpretable machine learning model to predict suicidal tendency among Chinese secondary school students. METHODS: A cross-sectional survey was conducted among 12,063 studen...

Artificial Intelligence-Enhanced Implantable Loop Recorders in Pediatric Patients: Effects on Device Performance and Clinical Workflow in a Single-Center Experience.

INTRODUCTION: Artificial intelligence (AI)-based algorithms have been developed to reduce alert burden in patients with implantable loop recorders (ILRs), but their performance in pediatric populations has not been previously evaluated. METHODS: We retrospectively analyzed pediatric patients implanted with an AI-enhanced ILR (Linq2, Medtronic) incorporating AI-based filtering (AccuRhythm algorithm...

Artificial Intelligence Methods in Early Detection of Autism Spectrum Disorder: A DSM-5 Criterion-Based Systematic Review.

Can Artificial Intelligence (AI) revolutionize early detection of Autism Spectrum Disorder (ASD) by offering a more objective alternative to the subje...

The role of machine learning, deep learning, and MRI findings in the classification of pediatric posterior fossa tumors.

PURPOSE: Pediatric posterior fossa tumors represent a major subset of childhood central nervous system neoplasms; however, overlapping MRI features of...

AI Chatbot Use and Disclosure for Mental Health Among US Adolescents and Young Adults.

IMPORTANCE: The rapid expansion of artificial intelligence (AI) chatbots has coincided with a persistent youth mental health crisis in the US, raising...

Pediatric Clinical Images Without Consent: A Governance Gap in the Long-Term Reuse of Health Data in Digital Health Ecosystems.

Digital health governance frameworks have primarily focused on prospective safeguards, including informed consent at the point of data collection, law...

Bridging Generations: Leveraging Technology and Artificial Intelligence for Better Research, Outreach, and Daily Practice.

The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2025 annual meeting hosted a workshop entitled "Bridging generatio...

Manifold Learning Approaches for Characterizing Photoplethysmographic Signals.

OBJECTIVE: Photoplethysmography (PPG) is widely used for cardiovascular monitoring, but its analysis is challenged by signal variability, inconsistent...

An Active Dry-Contact Continuous EEG Monitoring System for Seizure Detection Applications in Clinical Neurophysiology.

OBJECTIVE: Young children and infants, especially newborns, are highly susceptible to seizures, which, if undetected and untreated, can lead to severe...

Systematic review of foundation models for structured electronic health records.

PURPOSE: Foundation models pretrained on structured electronic health record (EHR) data promise improved predictive performance, sample efficiency and...

Evaluation of a Collaborative Telehealth Model for Eye Care Between Ophthalmology and Optometry in Western Australia.

OBJECTIVE: Recent advances in technology and the impact of COVID-19 have expanded the adoption of digital health. Synchronous collaborative telehealth...

Machine learning-based risk warning for adolescents' prosocial behavior.

Prosocial behavior plays a positive role in promoting adolescents' mental health and behavioral performance. Guided in ecological systems theory and s...

A Comparative Evaluation of Large Language Models on Pediatric Board-Style Examinations.

OBJECTIVE: To evaluate the performance of 4 leading large language models (LLMs) on pediatric board-style examinations and to assess whether question ...

Pandemic emergency: A multi-level AI framework for analyzing psychological impact through public discourse.

Emergency management has become a widely discussed topic in recent years, particularly in relation to different types of emergencies such as climate c...

How Artificial Intelligence Is Changing Neonatal Care.

Integrated analysis of mRNA, circRNA, and miRNA reveals a ceRNA network underlying growth rate differences in the longissimus dorsi muscle of Saba pigs.

The growth rate of pigs is a key factor that determines production efficiency and economic benefits. Nevertheless, the regulatory mechanisms underlyin...

[Artificial intelligence in diabetic retinopathy screening].

INTRODUCTION: Visual impairment and blindness continue to represent a substantial disease burden in Hungary. According to national epidemiological dat...

Unified comparison of machine learning paradigms for blood transfusion prediction in pediatric congenital heart surgery.

Blood transfusion prediction studies in pediatric cardiac surgery have employed direct regression, two-stage, and multi-class classification paradigms...

Prediction of low birth weight using machine learning-based analysis of environmental and maternal risk factors: insights from the Korean CHildren's ENvironmental health Study (Ko-CHENS).

Adverse birth outcomes such as low birth weight (LBW) increase the risk of metabolic disorders and hypertension later in life. Although previous studi...

Guideline Adherence in the Diagnosis of PCOS Among Adolescents: Comparing Obstetricians and Gynecologists With Large Language Models.

STUDY OBJECTIVE: Polycystic ovary syndrome in adolescence is a condition associated with long-term cardiovascular, metabolic, and psychological outcom...