Pediatrics

Revolutionizing Intensive Care Unit Care: A Scoping Review of Multimodal Family Engagement Technologies.

This scoping review systematically examines the emerging field of multimodal family engagement technologies in intensive care units (ICUs). Despite significant advancements in medical technology, family engagement remains an underutilized resource in many ICU settings. This review consolidates current evidence by evaluating the effectiveness of these systems in improving patient outcomes and famil...

Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data.

Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder (ASD). ASD is typically detected using a case-finding approach based on symptoms and family history, resulting in many delayed or missed diagnoses. While population-based screening would be ideal for early identification, available screening tools ha...

Federated learning with integrated attention multiscale model for brain tumor segmentation.

Brain tumors are an extremely deadly condition and the growth of abnormal cells that have formed inside the brain causes the illness. According to stu...

Novel hybrid transfer neural network for wheat crop growth stages recognition using field images.

Wheat is one of the world's most widely cultivated cereal crops and is a primary food source for a significant portion of the population. Wheat goes t...

Chatbots for conducting systematic reviews in pediatric dentistry.

OBJECTIVES: The performance of chatbots for discrete steps of a systematic review (SR) on artificial intelligence (AI) in pediatric dentistry was eval...

Prediction of first attempt of suicide in early adolescence using machine learning.

BACKGROUND: Suicide is the second leading cause of death among early adolescents, yet the first onset of suicide attempts during this critical develop...

Artificial intelligence-based non-invasive bilirubin prediction for neonatal jaundice using 1D convolutional neural network.

Neonatal jaundice, characterized by elevated bilirubin levels causing yellow discoloration of the skin and eyes in newborns, is a critical condition r...

Transforming physical fitness and exercise behaviors in adolescent health using a life log sharing model.

INTRODUCTION: This study investigates the potential of a deep learning-based Life Log Sharing Model (LLSM) to enhance adolescent physical fitness and ...

Learning-Based Models for Predicting IVIG Resistance and Coronary Artery Lesions in Kawasaki Disease: A Review of Technical Aspects and Study Features.

Kawasaki disease (KD) is a common pediatric vasculitis, with coronary artery lesions (CALs) representing its most severe complication. Early identific...

Prediction of IUGR condition at birth by means of CTG recordings and a ResNet model.

OBJECTIVE: Sub-optimal uterine-placental perfusion and fetal nutrition can lead to intrauterine growth restriction (IUGR), also called fetal growth re...

Evaluation of correctness and reliability of GPT, Bard, and Bing chatbots' responses in basic life support scenarios.

Timely recognition and initiation of basic life support (BLS) before emergency medical services arrive significantly improve survival rates and neurol...

Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study.

BACKGROUND: Delirium in intensive care unit (ICU) patients poses a significant challenge, affecting patient outcomes and health care efficiency. Devel...

Interpretable machine learning model for early prediction of disseminated intravascular coagulation in critically ill children.

Disseminated intravascular coagulation (DIC) is a thrombo-hemorrhagic disorder that can be life-threatening in critically ill children, and the quest ...

Forecasting motion trajectories of elbow and knee joints during infant crawling based on long-short-term memory (LSTM) networks.

BACKGROUND: Hands-and-knees crawling is a promising rehabilitation intervention for infants with motor impairments, while research on assistive crawli...

Solving Complex Pediatric Surgical Case Studies: A Comparative Analysis of Copilot, ChatGPT-4, and Experienced Pediatric Surgeons' Performance.

The emergence of large language models (LLMs) has led to notable advancements across multiple sectors, including medicine. Yet, their effect in pediat...

Evaluating how different balancing data techniques impact on prediction of premature birth using machine learning models.

Premature birth can be defined as birth before 37 weeks of gestation, which is a significant global health issue, being the main cause for neonatal de...

Development and validation of a machine learning model for predicting pediatric metabolic syndrome using anthropometric and bioelectrical impedance parameters.

OBJECTIVE: Metabolic syndrome (MS) is a risk factor for cardiovascular diseases, and its prevalence is increasing among children and adolescents. This...

Trade-off of different deep learning-based auto-segmentation approaches for treatment planning of pediatric craniospinal irradiation autocontouring of OARs for pediatric CSI.

BACKGROUND: As auto-segmentation tools become integral to radiotherapy, more commercial products emerge. However, they may not always suit our needs. ...

Updates in Neonatal Seizures.

Neonatal seizures are a common medical emergency, necessitating prompt treatment. The most common etiologies include hypoxic-ischemic encephalopathy, ...

AI-Enabled Screening for Retinopathy of Prematurity in Low-Resource Settings.

IMPORTANCE: Retinopathy of prematurity (ROP) is the leading cause of preventable childhood blindness worldwide. If detected and treated early, ROP-ass...