Pediatrics

Advancing newborn care: Precise time of birth detection using ai-driven thermal imaging with adaptive normalization.

Around 5%-10% of newborns need assistance to start breathing. Currently, there is a lack of evidence-based research, objective data collection, and opportunities for learning from real newborn resuscitation emergency events. Generating and evaluating automated newborn resuscitation algorithm activity timelines relative to the Time of Birth (ToB) offers a promising opportunity to enhance newborn ca...

Feasibility of generating sagittal radiographs from coronal views using GAN-based deep learning framework in adolescent idiopathic scoliosis.

BACKGROUND: Minimizing radiation exposure is crucial in monitoring adolescent idiopathic scoliosis (AIS). Generative adversarial networks (GANs) have emerged as valuable tools being able to generate high-quality synthetic images. This study explores the use of GANs to generate synthetic sagittal radiographs from coronal views in AIS patients.

Graph Neural Network Learning on the Pediatric Structural Connectome.

PURPOSE: Sex classification is a major benchmark of previous work in learning on the structural connectome, a naturally occurring brain graph that has...

A pediatric emergency prediction model using natural language process in the pediatric emergency department.

This study developed a predictive model using deep learning (DL) and natural language processing (NLP) to identify emergency cases in pediatric emerge...

A novel clinical investigation using deep learning and human-in-the-loop approach in orbital volume measurement.

Orbital volume assessment is crucial for surgical planning. Traditional methods lack efficiency and accuracy. Recent studies explore AI-driven techniq...

Deep learning based decision-making and outcome prediction for adolescent idiopathic scoliosis patients with posterior surgery.

With the emergence of numerous classifications, surgical treatment for adolescent idiopathic scoliosis (AIS) can be guided more effectively. However, ...

Acute effect of endurance exercise on human milk insulin concentrations: a randomised cross-over study.

INTRODUCTION: Insulin is present in human milk and its concentration correlates with maternal circulating levels. Studies on the association between h...

Deep learning for forensic age estimation using orthopantomograms in children, adolescents, and young adults.

OBJECTIVES: Forensic age estimation from orthopantomograms (OPGs) can be performed more quickly and accurately using convolutional neural networks (CN...

Machine Learning-Based predictive model for adolescent metabolic syndrome: Utilizing data from NHANES 2007-2016.

Metabolic syndrome (Mets) in adolescents is a growing public health issue linked to obesity, hypertension, and insulin resistance, increasing risks of...

Low-cost and scalable machine learning model for identifying children and adolescents with poor oral health using survey data: An empirical study in Portugal.

This empirical study assessed the potential of developing a machine-learning model to identify children and adolescents with poor oral health using on...

Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.

BACKGROUND: Stunting is a vital indicator of chronic undernutrition that reveals a failure to reach linear growth. Investigating growth and nutrition ...

Novel machine learning applications in peripartum care: a scoping review.

OBJECTIVE: Machine learning (ML), a subtype of artificial intelligence (AI), presents predictive modeling and dynamic diagnostic tools to facilitate e...

Artificial Intelligence In Health And Health Care: Priorities For Action.

The field of artificial intelligence (AI) has entered a new cycle of intense opportunity, fueled by advances in deep learning, including generative AI...

Subway opening enables urban green development: Evidence from difference-in-differences and double dual machine learning methods.

Urban rail transport is an important part of transport infrastructure, which is significant in empowering green-oriented urban development. However, f...

AI Can Be a Powerful Social Innovation for Public Health if Community Engagement Is at the Core.

There is a critical need for community engagement in the process of adopting artificial intelligence (AI) technologies in public health. Public health...

Identification of depressive symptoms in adolescents using machine learning combining childhood and adolescence features.

BACKGROUND: Depressive symptoms in adolescents can significantly affect their daily lives and pose risks to their future development. These symptoms m...

Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized care.

Autism Spectrum Disorder (ASD) affects millions of individuals worldwide, presenting challenges in social communication, repetitive behaviors, and sen...

Pediatric Predictive Artificial Intelligence Implemented in Clinical Practice from 2010 to 2021: A Systematic Review.

To review pediatric artificial intelligence (AI) implementation studies from 2010 to 2021 and analyze reported performance measures.We searched PubMed...

From social media to artificial intelligence: improving research on digital harms in youth.

In this Personal View, we critically evaluate the limitations and underlying challenges of existing research into the negative mental health consequen...

Deep Learning for Pediatric Sleep Staging From Photoplethysmography: A Transfer Learning Approach From Adults to Children.

BACKGROUND: Sleep staging is critical for diagnosing sleep disorders. Traditional methods in clinical settings involve time-intensive scoring procedur...