Pediatrics

What Can We Learn from the RSNA Pediatric Bone Age Machine Learning Challenge?

CbpM and CbpG of Streptococcus Pneumoniae Elicit a High Protection in Mice Challenged with a Serotype 19F Pneumococcus.

Among many pneumococcal antigens, choline-binding proteins (CPBs) display a high immunogenicity in animal models. This study aims to determine the immunogenicity of CbpM, CbpG and CbpL proteins of Streptococcus pneumoniae in a mice model. The genes were cloned into pET21a expression vector and the recombinant proteins were produced. Mice were immunized with the purified recombinant proteins. Subse...

Coreflood Study of Effect of Surfactant Concentration on Foam Generation in Porous Media.

The propagation of foam in an oil reservoir depends on the creation and stability of the foam in the reservoir, specifically the creation and stabilit...

A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage.

OBJECTIVES: Pediatric asthma is a leading cause of emergency department (ED) utilization and hospitalization. Earlier identification of need for hospi...

Comparison of del Nido and histidine-tryptophan-ketoglutarate cardioplegia solutions in pediatric patients undergoing open heart surgery: A prospective randomized clinical trial.

OBJECTIVES: We conducted a prospective randomized controlled trial to compare del Nido (DN) and histidine-tryptophan-ketoglutarate (HTK) cardioplegia ...

The RSNA Pediatric Bone Age Machine Learning Challenge.

Purpose The Radiological Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge was created to show an application of machine l...

Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification.

This paper explores cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scal...

Supervised machine learning quality control for magnetic resonance artifacts in neonatal data sets.

Quality control (QC) of brain magnetic resonance images (MRI) is an important process requiring a significant amount of manual inspection. Major artif...

Robotically assisted long bone biopsy under MRI: cadaver study results.

RATIONALE AND OBJECTIVES: We have designed and constructed an MR-safe robot made entirely of nonmetallic components with pneumatic actuators and optic...

Culture of Mesenchymal Stem Cells in a Hydrogel Model of Vocal Fold Lamina Propria.

Stem cell injection has been proposed as an alternative approach for the restoration of vocal fold (VF) function in patients with VF scarring. To asse...

Ossification area localization in pediatric hand radiographs using deep neural networks for object detection.

BACKGROUND: Detection of ossification areas of hand bones in X-ray images is an important task, e.g. as a preprocessing step in automated bone age est...

Machine learning algorithms for activity recognition in ambulant children and adolescents with cerebral palsy.

BACKGROUND: Cerebral palsy (CP) is the most common physical disability among children (2.5 to 3.6 cases per 1000 live births). Inadequate physical act...

Choline: The Underconsumed and Underappreciated Essential Nutrient.

Choline has been recognized as an essential nutrient by the Food and Nutrition Board of the National Academies of Medicine since 1998. Its metabolites...

Frameless robot-assisted stereoelectroencephalography for refractory epilepsy in pediatric patients: accuracy, usefulness, and technical issues.

BACKGROUND: Stereoelectroencephalography (SEEG) is an effective technique to help to locate and to delimit the epileptogenic area and/or to define rel...

Estimation of Neonatal Intestinal Perforation Associated with Necrotizing Enterocolitis by Machine Learning Reveals New Key Factors.

Intestinal perforation (IP) associated with necrotizing enterocolitis (NEC) is one of the leading causes of mortality in premature neonates; with majo...

Shared Nearest-Neighbor Quantum Game-Based Attribute Reduction With Hierarchical Coevolutionary Spark and Its Application in Consistent Segmentation of Neonatal Cerebral Cortical Surfaces.

The unprecedented increase in data volume has become a severe challenge for conventional patterns of data mining and learning systems tasked with hand...

Using an artificial neural network to predict traumatic brain injury.

In BriefPediatric traumatic brain injury (TBI) is common, but not all injuries require hospitalization. A computational tool for ruling-in patients wh...

Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features.

INTRODUCTION: Anatomic characteristics of kidneys derived from ultrasound images are potential biomarkers of children with congenital abnormalities of...

HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation.

Recently, dense connections have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervisio...

Gene Essentiality Analyzed by Transposon Mutagenesis and Machine Learning in a Stable Haploid Isolate of .

Knowing the full set of essential genes for a given organism provides important information about ways to promote, and to limit, its growth and surviv...