Pediatrics

Lesion network mapping in pediatric epilepsy.

OBJECTIVE: To describe the current use, limitations, and future directions of lesion network mapping...

Current and future use of artificial intelligence in valvular heart disease imaging.

Valvular heart disease (VHD) remains significantly underdiagnosed and undertreated. This review exam...

Performance of large language models in answering frequently-asked questions on celiac disease.

OBJECTIVES: Celiac disease (CeD) is a common autoimmune condition requiring lifelong adherence to a ...

Use of Machine Learning to Predict Hospital Admission for EMS-Treated Infants After a Suspected BRUE.

OBJECTIVES: This study explored the use of different applied machine learning (ML) classification al...

Evaluating GPT-4 Performance on Plastic Surgery Oral Examination Vignettes: A Quantitative and Qualitative Analysis.

BACKGROUND: The use of artificial intelligence (AI) in medicine is rapidly evolving. However, its ro...

Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance.

BACKGROUND: Synthetic positron emission tomography (PET) imaging, enabled by deep learning, represen...

Perceptions and knowledge of machine learning for paediatric related decision support in emergency care - A UK and Ireland network survey study of clinician leaders.

This study explores clinician leaders understanding and perception at site level towards machine lea...

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 methodolo...

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 ...

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

PURPOSE: Accurately measuring geographic atrophy (GA) progression in clinical trials is challenging,...

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 macula...

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 w...

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 ...

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

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 opport...

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 inte...

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 (CA...

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 ...

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 i...

Evaluation and application of electrocardiographic age model for children.

ECG-age, derived from ECG signals using deep neural networks (DNNs), correlates with health status b...