Pediatrics

Glottic opening detection using deep learning for neonatal intubation with video laryngoscopy.

OBJECTIVE: This study aimed to develop an artificial intelligence (AI) method to augment video laryngoscopy (VL) by automating the detection of the glottic opening in neonates, as a step toward future studies on improving intubation outcomes.

Predicting craniofacial fibrous dysplasia growth status: an exploratory study of a hybrid radiomics and deep learning model based on computed tomography images.

OBJECTIVE: This study aimed to develop 3 models based on computed tomography (CT) images of patients with craniofacial fibrous dysplasia (CFD): a radiomics model (Model Rad), a deep learning (DL) model (Model DL), and a hybrid radiomics and DL model (Model Rad+DL), and evaluate the ability of these models to distinguish between adolescents with active lesion progression and adults with stable lesi...

Machine learning-based models for prediction of survival in medulloblastoma: a systematic review and meta-analysis.

BACKGROUND: Medulloblastoma (MB) is the pediatric population's most frequent malignant intracranial lesions. Prognostication plays a crucial role in o...

Enhancing Spanish Patient Education Materials: Comparing the Readability of Artificial Intelligence-Generated Spanish Patient Education Materials to the Society of Pediatric Dermatology Spanish Patient Brochures.

Patient education materials (PEMs) are crucial for improving patient adherence and outcomes; however, they may not be accessible due to high reading l...

MultiADE: A Multi-domain benchmark for Adverse Drug Event extraction.

OBJECTIVE: Active adverse event surveillance monitors Adverse Drug Events (ADE) from different data sources, such as electronic health records, medica...

A comparative study to elucidate factors explaining willingness to use home-care robots in Japan, Ireland, and Finland.

The implementation of home-care robots is sometimes unsuccessful. This study aimed to explore factors explaining people's willingness to use home-care...

Subjective well-being of children with special educational needs: Longitudinal predictors using machine learning.

Children with special educational needs (SEN) are a diverse group facing numerous challenges related to well-being and mental health. Understanding th...

Assessing COVID-19 Vaccine Effectiveness and Risk Factors for Severe Outcomes through Machine Learning Techniques: A Real-World Data Study in Andalusia, Spain.

BACKGROUND: COVID-19 vaccination has become a pivotal global strategy in managing the pandemic. Despite COVID-19 no longer being classified as a Publi...

Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays.

BACKGROUND: Correctly diagnosing and accurately distinguishing mycoplasma pneumonia in children has consistently posed a challenge in clinical practic...

Improving methylmalonic acidemia (MMA) screening and MMA genotype prediction using random forest classifier in two Chinese populations.

BACKGROUND: Methylmalonic acidemia (MMA) is one of the most common hereditary organic acid metabolism disorders that endangers the lives and health of...

Height prediction of individuals with osteogenesis imperfecta by machine learning.

BACKGROUND: Osteogenesis imperfecta (OI) is a genetic disorder characterized by low bone mass, bone fragility and short stature. There is a significan...

Machine learning-based rational design for efficient discovery of allatostatin analogs as promising lead candidates for novel IGRs.

BACKGROUND: Insect neuropeptide allatostatins (ASTs) play a vital role in regulating insect growth, development, and reproduction, making them potenti...

A multi-layer neural network approach for the stability analysis of the Hepatitis B model.

In the present study, we explore the dynamics of Hepatitis B virus infection, a significant global health issue, through a newly developed dynamics sy...

Synergistic biophysics and machine learning modeling to rapidly predict cardiac growth probability.

Computational models that can predict growth and remodeling of the heart could have important clinical applications. However, the time it takes to cal...

Exploring the spatial effects influencing the EGFR/ERK pathway dynamics with machine learning surrogate models.

The fate of cells is regulated by biochemical reactions taking place inside of them, known as intracellular pathways. Cells display a variety of chara...

Microfluidic Optical Aptasensor for Small Molecules Based on Analyte-Tuned Growth of Gold Nanoseeds and Machine Learning-Enhanced Spectrum Analysis: Rapid Detection of Mycotoxins.

Natural toxins, mainly small molecules, are a category of chemical hazards in agri-food systems that pose threats to both public health and food secur...

How Artificial Intelligence is altering the nursing workforce.

This paper focuses on the implications of Artificial Intelligence (AI) for the nursing workforce, examining both the opportunities presented by AI in ...

Deep learning approaches for automated classification of neonatal lung ultrasound with assessment of human-to-AI interrater agreement.

Neonatal respiratory disorders pose significant challenges in clinical settings, often requiring rapid and accurate diagnostic solutions for effective...

Machine learning for forecasting initial seizure onset in neonatal hypoxic-ischemic encephalopathy.

OBJECTIVE: This study was undertaken to develop a machine learning (ML) model to forecast initial seizure onset in neonatal hypoxic-ischemic encephalo...

Artificial intelligence and deep learning algorithms for epigenetic sequence analysis: A review for epigeneticists and AI experts.

Epigenetics encompasses mechanisms that can alter the expression of genes without changing the underlying genetic sequence. The epigenetic regulation ...