Pediatrics

Anaesthesia in 2050: how emerging technologies will transform our practice: The Sir Robert Macintosh Lecture 2025.

In the next 25 years, we will see unprecedented technological progress due to the artificial intelligence (AI) revolution. AI-driven advances will be made in five strongly interconnected domains: reenergised drug discovery, continuous real-time monitoring, personalised medicine, automation and decentralised care. Drug discovery will move from in vitro to in silico approaches. We will identify mole...

Machine learning for the prediction of spontaneous preterm birth using early second and third trimester maternal blood gene expression: A cautionary tale.

Spontaneous preterm birth (sPTB) remains a significant global health challenge and a leading cause of neonatal mortality and morbidity. Despite advancements in neonatal care, the prediction of sPTB remains elusive, in part due to complex etiologies and heterogeneous patient populations. This study aimed to validate and extend information on gene expression biomarkers previously described for predi...

Improving Clinical Utility of Fetal Cine CMR Using Deep Learning Super-Resolution.

BACKGROUND: Fetal cardiovascular magnetic resonance is an emerging tool for prenatal congenital heart disease assessment, but long acquisition times a...

On the usage of artificial intelligence in leprosy care: A systematic literature review.

Leprosy, or Hansen's disease, is a Neglected Tropical Disease (NTD) caused by Mycobacterium leprae that mainly affects the skin and peripheral nerves,...

Development of an artificial intelligence-assisted system for tracheal intubation using a video laryngoscope in infants and neonates.

BACKGROUND: The larynx of infants and neonates is occasionally challenging to identify using video laryngoscope. This study aimed to develop an artifi...

A computer vision-based approach for high-throughput automated analysis of Arabidopsis seedling phenotypes.

Phenotype observations are common methodologies in plant biology studies, ranging from recording growth parameters to flowering dates. Identifying mut...

Death One Hour After Terminal Extubation in Children: Validation of a Machine Learning Model to Predict Cardiac Death After Withdrawal of Life-Sustaining Treatment in a Multicenter Cohort, 2009-2021.

OBJECTIVES: In the PICU, predicting death within 1 hour after terminal extubation (TE) is valuable in augmenting family counseling and in identifying ...

Novel Application of Connectomics to the Surgical Management of Pediatric Arteriovenous Malformations.

Introduction The emergence of connectomics in neurosurgery has allowed for construction of detailed maps of white matter connections, incorporating bo...

In silico & in vitro approaches suggest osteoclastogenesis induction underlying fractures in Entrectinib-treated children.

Entrectinib, a potent, CNS-active, TRK and ROS1 inhibitor is associated with occurrence of bone fractures, particularly in pediatric patients. We emba...

Comprehensive analysis and experimental validation of BST1 as a novel diagnostic biomarker for pediatric sepsis using multiple machine learning algorithms.

Bone marrow stromal cell antigen-1 (BST1) expression is elevated in a variety of human diseases, but its relationship with pediatric sepsis is unclear...

Currently Available Large Language Models Are Moderately Effective in Improving Readability of English and Spanish Patient Education Materials in Pediatric Orthopaedics.

INTRODUCTION: Patient education materials (PEMs) consistently exceed the recommended sixth-grade reading level. Poor health literacy and limited Engli...

N.U.R.S.E.S. embracing artificial intelligence: A guide to artificial intelligence literacy for the nursing profession.

Artificial intelligence (AI) is reshaping health care, making AI literacy vital for nursing professionals. The Navigate AI basics, Utilize AI strategi...

Harnessing Generative Artificial Intelligence in Pediatric Anesthesia: Enhancing Learning, Patient Care, and Family Communication.

Machine learning model for the prediction of postoperative acute kidney injury (AKI) in neonates undergoing digestive surgery.

OBJECTIVE: The aim of our study is to determine the main predictors of postoperative AKI in neonates using machine learning models compared with the l...

Neural network-based multi-task learning to assist planning of posterior spinal fusion surgery for adolescent idiopathic scoliosis.

PURPOSE: Posterior spinal instrumentation and fusion (PSF) is the gold standard for severe adolescent idiopathic scoliosis (AIS), yet instrumentation ...

A Comparative Assessment of Large Language Models in Pediatric Dialysis: Reliability, Quality and Readability.

INTRODUCTION: This study evaluated the reliability, quality, and readability of ChatGPT (OpenAI, San Francisco, CA), Gemini (Google, Mountain View, CA...

The early prediction of neonatal necrotizing enterocolitis in high-risk newborns based on two medical center clinical databases.

: Early identification and timely preventive interventions play an essential role for improving the prognosis of newborns with necrotizing enterocolit...

Physiological Response of Tissue-Engineered Vascular Grafts to Vasoactive Agents in an Ovine Model.

Tissue-engineered vascular grafts (TEVGs) are emerging as promising alternatives to synthetic grafts, particularly in pediatric cardiovascular surgery...

AI in Medical Questionnaires: Innovations, Diagnosis, and Implications.

This systematic review aimed to explore the current applications, potential benefits, and issues of artificial intelligence (AI) in medical questionna...

Axial Skeletal Assessment in Osteoporosis Using Radiofrequency Echographic Multi-spectrometry: Diagnostic Performance, Clinical Utility, and Future Directions.

Osteoporosis, a prevalent skeletal disorder, necessitates accurate and accessible diagnostic tools for effective disease management and fracture preve...