Pediatrics

Machine learning algorithms for the early detection of bloodstream infection in children with osteoarticular infections.

BACKGROUND: Bloodstream infection (BSI) poses a significant life-threatening risk in pediatric patients with osteoarticular infections. Timely identification of BSI is crucial for effective management and improved patient outcomes. This study aimed to develop a machine learning (ML) model for the early identification of BSI in children with osteoarticular infections.

Artificial intelligence-assisted echocardiographic monitoring in pediatric patients on extracorporeal membrane oxygenation.

BACKGROUND: Percutaneous extracorporeal membrane oxygenation (ECMO) is administered to pediatric patients with cardiogenic shock or cardiac arrest. The traditional method uses focal echocardiography to complete the left ventricular measurement. However, echocardiographic determination of the ejection fraction (EF) by manual tracing of the endocardial borders is time consuming and operator dependen...

Rhabdomyosarcoma Classification: Refining Our Understanding.

Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children, representing approximately 40% of all pediatric soft tissue sarcomas and 3%...

Automated Neuroprognostication Via Machine Learning in Neonates with Hypoxic-Ischemic Encephalopathy.

OBJECTIVES: Neonatal hypoxic-ischemic encephalopathy is a serious neurologic condition associated with death or neurodevelopmental impairments. Magnet...

A neural network to create super-resolution MR from multiple 2D brain scans of pediatric patients.

BACKGROUND: High-resolution (HR) 3D MR images provide detailed soft-tissue information that is useful in assessing long-term side-effects after treatm...

Machine learning-enhanced assessment of potential probiotics from healthy calves for the treatment of neonatal calf diarrhea.

Neonatal calf diarrhea (NCD) remains a significant contributor to calf mortality within the first 3 weeks of life, prompting widespread antibiotic use...

ChatGPT-4o's performance on pediatric Vesicoureteral reflux.

INTRODUCTION: Vesicoureteral reflux (VUR) is a common congenital or acquired urinary disorder in children. Chat Generative Pre-trained Transformer (Ch...

Development of multistage crop yield estimation model using machine learning and deep learning techniques.

In this research paper, machine learning techniques were applied to a multivariate meteorological time series data for estimating the wheat yield of f...

Understanding the role of hormones in pediatric growth: Insights from a double-debiased machine learning approach.

This study investigates the causal relationships between hormone levels and growth and development of children, focusing specifically on height dispar...

Artificial intelligence applied to the study of human milk and breastfeeding: a scoping review.

BACKGROUND: Breastfeeding rates remain below the globally recommended levels, a situation associated with higher infant and neonatal mortality rates. ...

Identifying Infant Body Position from Inertial Sensors with Machine Learning: Which Parameters Matter?

The efficient classification of body position is crucial for monitoring infants' motor development. It may fast-track the early detection of developme...

ESR Essentials: how to get to valuable radiology AI: the role of early health technology assessment-practice recommendations by the European Society of Medical Imaging Informatics.

AI tools in radiology are revolutionising the diagnosis, evaluation, and management of patients. However, there is a major gap between the large numbe...

Multi-Task Learning for Audio-Based Infant Cry Detection and Reasoning.

Infant cry is a crucial indicator that offers valuable insights into their physical and mental conditions, such as hunger and pain. However, the scarc...

Predicting adverse pregnancy outcome in Rwanda using machine learning techniques.

BACKGROUND: Adverse pregnancy outcomes pose significant risk to maternal and neonatal health, contributing to morbidity, mortality, and long-term deve...

Deep learning based super-resolution for CBCT dose reduction in radiotherapy.

BACKGROUND: Cone-beam computed tomography (CBCT) is a crucial daily imaging modality in image-guided and adaptive radiotherapy. However, the use of io...

Predictive analysis of COVID-19 occurrence and vaccination impacts across the 50 US states.

OBJECTIVE: This study aimed to outline a machine learning model to assess the effectiveness of vaccination in COVID-19 confirmed cases and fatalities....

Artificial intelligence and pediatric surgery: where are we?

Here, we explore the transformative effects of artificial intelligence (AI) and large language models (LLMs), such as ChatGPT and GEMINI, on pediatric...

Employing machine learning models to predict pregnancy termination among adolescent and young women aged 15-24 years in East Africa.

Pregnancy termination is still a sensitive and continuing public health issue due to several political, economic, religious, and social concerns. This...

Development of machine learning-based models to predict congenital heart disease: A matched case-control study.

BACKGROUND: The current congenital heart disease (CHD) prediction tools lack adequate interpretability and convenience, hindering the development of p...

Machine Learning-based Prediction of Blood Stream Infection in Pediatric Febrile Neutropenia.

OBJECTIVES: This study aimed to develop machine learning (ML) prediction models for identifying bloodstream infection (BSI) and septic shock (SS) in p...