Pediatrics

A hybrid framework for notebook market analysis: Integrating social media sentiment mining with expert knowledge for feature prioritization.

The increasing complexity of consumer preferences in the notebook market requires advanced methodologies to effectively analyze user sentiments and prioritize product features for strategic decision-making. Traditional market research methods often fail to capture real-time, spontaneous consumer feedback while lacking integration with expert knowledge of actual purchasing behavior. This study intr...

Development of a rapid metal oxide semiconductor-based sensory system for noninvasive neonatal sepsis detection.

Machine learning model predicts prognosis in SARS-CoV-2-infected autoimmune encephalitis patients.

OBJECTIVE: To assess the neurological prognosis of patients with autoimmune encephalitis (AE) after severe acute respiratory syndrome coronavirus 2 (S...

Simultaneous Segmentation of Geographic Atrophy in Longitudinally Acquired Fundus Autofluorescence Images.

PURPOSE: Accurately measuring geographic atrophy (GA) progression in clinical trials is challenging, owing to its slow and variable nature. This study...

Quantitative Assessment of Fluorescein Angiography Leakage via Deep Learning in Pediatric Uveitis: Correlation with Clinical Parameters.

PURPOSE: Develop a deep learning algorithm for automated segmentation of retinal vascular and macular edema (ME) leakage in fluorescein angiography (F...

Carbon-phosphorus exchange rate constrains density-speed trade-off in arbuscular mycorrhizal fungal growth.

Symbiotic nutrient exchange between arbuscular mycorrhizal (AM) fungi and their host plants varies widely depending on their physical, chemical, and b...

Identifying genome-by-childhood trauma interactions for depression using a forest-based approach in the UK Biobank and Adolescent Brain Cognitive Development Study.

Depression is shaped by both genetic and environmental factors, but genome-wide interaction studies (GWIS) often lack power to detect complex gene-env...

The diagnostic value of enhanced CT radiomics and deep learning in differentiating pediatric peripheral neuroblastoma from ganglioneuroblastoma.

Synergistic DBNDD1-GDF15 Signaling Activates the NF-κB Pathway to Promote Colorectal Cancer Progression.

UNLABELLED: Colorectal cancer is a highly lethal gastrointestinal tract malignancy whose pathogenesis and molecular drivers are not fully understood. ...

Implementation of artificial intelligence in palliative and supportive care for people with cancer: A scoping review.

BACKGROUND: Cancer remains a leading global health burden. Artificial intelligence offers new opportunities to address complex physical and psychologi...

Seasonal variation in BMI outcomes at 6 months: secondary analyses of a multidisciplinary healthy lifestyle programme for children and adolescents with obesity.

BACKGROUND: While international evidence suggests seasonal variations may influence outcomes of interventions for pediatric obesity, data for Aotearoa...

Perceived Potential and Challenges of Supporting Coronary Artery Disease Treatment Decisions With AI: Qualitative Study.

BACKGROUND: Coronary revascularization decision-making for patients with coronary artery disease (CAD) can be complex and challenging. Artificial inte...

Deep phenotyping of suicidal ideation after discharge from psychiatric inpatient care: study protocol for an interdisciplinary, multicentre prospective observational study in Psychiatric University Hospitals.

INTRODUCTION: Suicidal thoughts and behaviours (STB) are a critical public health concern, with 700 000 deaths by suicide each year. The period immedi...

Machine learning to infer neurocognitive testing scores among adolescents and young adults with congenital heart disease.

BACKGROUND: Congenital heart disease (CHD) affects about 1% of births and is linked to differences in thinking and learning. Understanding how birth, ...

Evaluation and application of electrocardiographic age model for children.

ECG-age, derived from ECG signals using deep neural networks (DNNs), correlates with health status but has been predominantly studied in adults, negle...

Validation of a machine-learning-based algorithm to predict preeclampsia-related adverse outcomes on a real-world dataset.

PURPOSE: Preeclampsia is a major obstetric disorder. Machine learning (ML) models incorporating angiogenic biomarkers show promise in predicting relat...

Offline AI-assisted fundus imaging for preliminary screening of childhood glaucoma.

PURPOSE: To evaluate the feasibility and performance of AI-assisted portable fundus photography in children with glaucoma. METHODS: This case series d...

Microvascular phenotypes in pediatric sepsis identified by machine learning: prognostic implications for organ dysfunction and mortality.

Breaking the 2-nm Barrier in Hard Disk Drives Using Monolayer Amorphous Carbon Overcoats.

The rapid growth of artificial intelligence (AI) has increased the demand for large-scale data storage, making hard disk drives (HDDs) indispensable i...

Exploring Clinician Perspectives on Artificial Intelligence in Primary Care: Qualitative Systematic Review and Meta-Synthesis.

BACKGROUND: Recent advances have highlighted the potential of artificial intelligence (AI) systems to assist clinicians with administrative and clinic...