Pediatrics

Machine Learning-Based Prediction of Critical Deterioration in the PICU.

BACKGROUND: Existing PICU early warning systems lack sufficient accuracy and timeliness for effective preparation. Machine learning approaches may improve prediction of critical deterioration events (CDEs), but their operational utility relative to existing tools remains unclear. OBJECTIVES: To develop a machine learning model for early detection of CDEs and evaluate operational utility against ex...

Explainable machine learning using urinary metabolomics to predict pediatric sepsis-associated acute kidney injury: a two-center prospective observational study.

Sepsis-induced acute kidney injury (S-AKI) is a common and serious complication in critically ill children with a poor prognosis, and its early and accurate prediction remains challenging due to the lack of reliable biomarkers. Urinary small-molecule metabolomics offers a promising approach to capture the dynamic metabolic changes during the progression of S-AKI. In this two-center prospective obs...

Few-shot learning for surgical phase recognition: Performance and generalization in cholecystectomy.

BACKGROUND AND OBJECTIVE: The automated recognition of surgical phases in intraoperative videos represents a critical milestone in the digital transfo...

The Effect of Age, Sex, and Body Mass Index on Spinopelvic Balance Throughout Childhood and Adolescence: An Automated 3D-Computed Tomography Study of 9,721 Pediatric Patients.

BACKGROUND: Spinopelvic assessment is critical for surgical planning. It is unknown if age, sex, and body mass index (BMI) influence pelvic orientatio...

Decoding viral evolution through integrative bioinformatics: From genomes to global health.

Bioinformatics has transformed modern virology by linking genomic variation to epidemiology, protein structure, and public health action. This review ...

Machine learning and meningitis prediction in pediatric invasive pneumococcal disease: a retrospective single-center study.

Performance and reliability of large language models on the European Board of Hand Surgery examination: a multi-model evaluation study.

INTRODUCTION: Artificial intelligence (AI) has demonstrated transformative potential in medical education and assessment, with large language models a...

A Comparative Assessment of Large Language Models in Congenital Hypothyroidism: Reliability, Quality and Readability.

OBJECTIVE: To comparatively evaluate the reliability, quality, and readability of responses generated by widely used large language model (LLM)-based ...

Automated Deep Learning-Based Demyelination Load Segmentation in Metachromatic Leukodystrophy.

PURPOSE: Metachromatic leukodystrophy (MLD) is a rare lysosomal storage disorder characterized by progressive white matter demyelination. Quantificati...

Engaging Hospital Staff to Identify Levers for Adoption of Clinical Decision Support: Protocol for a Single-Site Case Study Using System Dynamics Group Model Building.

BACKGROUND: Clinical decision support (CDS) tools that provide patient-specific and evidence-based information to clinicians and care managers regardi...

[Prevention in Gastroenterology].

Gastroenterology offers a broad, evidence-based range of preventive measures that goes beyond colorectal cancer screening. As a specialty of systemic ...

Correction: Artificial intelligence in pediatric myopia-a narrative review.

Perceptions of Health Care Professionals on the Integration and Use of AI in Clinical Cancer Care: Interview Study.

BACKGROUND: Artificial intelligence (AI) is increasingly recognized for its potential to transform cancer care. However, much of the existing evidence...

Ultrafast Infant Brain Quantitative MRI Using Overlapping-Echo Acquisition with Volumetric Physical Simulation of Slice-level Non-Idealities.

OBJECTIVE: Quantitative MRI (qMRI) is sensitive to brain microstructural and metabolic changes; however, existing techniques often unsuitable for asse...

The future of follow-up: technology-enabled medical and developmental follow-up care for NICU graduates.

Premature infants are at higher risk of long-term clinical and developmental sequelae compared to their term counterparts. Specialized, multidisciplin...

Optimizing Pichia Pastoris Cell-Free Protein Synthesis to Improve Economics.

Cell-free protein synthesis (CFPS) is a powerful and versatile platform that supports a wide range of applications, from fundamental studies of the ge...

AraBLA: cross-layer enhanced AraBERT with BiLSTM and attention for Arabic fake news detection.

The rapid growth of misinformation in Arabic digital media has created an urgent need for more accurate detection systems. Many existing approaches, w...

Robust prediction of Listeria monocytogenes, Listeria innocua and aerobic spoilage bacteria growth in food systems using Multilayer Perceptron Artificial Neural Networks.

Spoilage microorganisms and pathogenic bacteria can negatively impact food safety and stability, leading to potential health risks and economic losses...

Enhancing Fetal Cardiac Care: Addressing Unwarranted Variation in Prenatal Congenital Heart Disease Detection and Counseling.

Ensemble learning for predicting birth trauma using high-dimensional data in the neonatal intensive care unit.

Birth trauma remains a significant global health challenge and contributes substantially to neonatal morbidity and mortality. Although machine learnin...