Pediatrics

What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care.

Primary care physicians are likely both excited and apprehensive at the prospects for artificial intelligence (AI) and machine learning (ML). Complexity science may provide insight into which AI/ML applications will most likely affect primary care in the future. AI/ML has successfully diagnosed some diseases from digital images, helped with administrative tasks such as writing notes in the electro...

Deep-learning model for prenatal congenital heart disease screening generalizes to community setting and outperforms clinical detection.

OBJECTIVES: Despite nearly universal prenatal ultrasound screening programs, congenital heart defects (CHD) are still missed, which may result in severe morbidity or even death. Deep machine learning (DL) can automate image recognition from ultrasound. The main aim of this study was to assess the performance of a previously developed DL model, trained on images from a tertiary center, using fetal ...

A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac MRI Registry of Patients with Single Ventricle Physiology.

Purpose To develop an end-to-end deep learning (DL) pipeline for automated ventricular segmentation of cardiac MRI data from a multicenter registry of...

Comparative Analysis of Machine Learning Algorithms Evaluating the Single Nucleotide Polymorphisms of Metabolizing Enzymes with Clinical Outcomes Following Intravenous Paracetamol in Preterm Neonates with Patent Ductus Arteriosus.

AIMS: Pharmacogenomics has been identified to play a crucial role in determining drug response. The present study aimed to identify significant geneti...

Deep learning-based differentiation of ventricular septal defect from tetralogy of Fallot in fetal echocardiography images.

BACKGROUND: Congenital heart disease (CHD) seriously affects children's health and quality of life, and early detection of CHD can reduce its impact o...

Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics.

Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance in entire dimensions of science including ...

[[Applications] 13. Segmentation of Infant Brain Ventricles with Hydrocephalus Using Deep Learning].

Data-driven Machine Learning Models for Risk Stratification and Prediction of Emergence Delirium in Pediatric Patients Underwent Tonsillectomy/Adenotonsillectomy.

AIM: In the pediatric surgical population, Emergence Delirium (ED) poses a significant challenge. This study aims to develop and validate machine lear...

Comprehensive Overview of Computational Modeling and Artificial Intelligence in Pediatric Neurosurgery.

In this chapter, we give an overview of artificial intelligence tools and their use thus far in pediatric neurosurgery. We discuss different machine l...

Untrained Network for Super-resolution for Non-contrast-enhanced Wholeheart MRI Acquired using Cardiac-triggered REACT (SRNN-REACT).

BACKGROUND: Three-dimensional (3D) whole-heart magnetic resonance imaging (MRI) is an excellent tool to check the heart anatomy of patients with conge...

Evaluation of Thoracic Morphological Changes Using Chest CT in Patients with Congenital Pectus Carinatum Treated by the Modified Abramson Technique.

BACKGROUND: The Pectus carinatum is an outward protrusion deformity of the sternum and adjacent costal cartilages. It is the second most common congen...

Predicting Antiseizure Medication Treatment in Children with Rare Tuberous Sclerosis Complex-Related Epilepsy Using Deep Learning.

BACKGROUND AND PURPOSE: Tuberous sclerosis complex disease is a rare, multisystem genetic disease, but appropriate drug treatment allows many pediatri...

New Horizons in artificial intelligence in the healthcare of older people.

Artificial intelligence (AI) in healthcare describes algorithm-based computational techniques which manage and analyse large datasets to make inferenc...

What Legal Frameworks Should Govern Use of Genetic Test Results by Private Health Insurers in New Zealand?

The rising cost of private health insurance and constraints within public health systems are global concerns. Genetic testing presents a transformativ...

[Experimental study of APB-DOCK8 transgenic tomato vaccine for caries prevention].

PURPOSE: To observe the anti-caries effect of transgenic tomato anti-caries vaccine after immunization with SD rats by gavage and to explore its immun...

Self-supervised machine learning using adult inpatient data produces effective models for pediatric clinical prediction tasks.

OBJECTIVE: Development of electronic health records (EHR)-based machine learning models for pediatric inpatients is challenged by limited training dat...

[Intelligent rehabilitation platform in intensive care unit].

As the development of rehabilitation medicine and critical care medicine, intensive care rehabilitation has become the focus of attention. With the de...

Development of a New Korean Standard Bone Age Model: A Paradigm for AI-Powered Pediatric Imaging.

Bone Age Assessment Using Artificial Intelligence in Korean Pediatric Population: A Comparison of Deep-Learning Models Trained With Healthy Chronological and Greulich-Pyle Ages as Labels.

OBJECTIVE: To develop a deep-learning-based bone age prediction model optimized for Korean children and adolescents and evaluate its feasibility by co...

Pilot study of machine learning in the task of distinguishing high and low-grade pediatric hydronephrosis on ultrasound.

PURPOSE: Hydronephrosis is a common pediatric urological condition, characterized by dilation of the renal collecting system. Accurate identification ...