Pediatrics

Keratoconus Severity Classification Using Features Selection and Machine Learning Algorithms.

Keratoconus is a noninflammatory disease characterized by thinning and bulging of the cornea, generally appearing during adolescence and slowly progressing, causing vision impairment. However, the detection of keratoconus remains difficult in the early stages of the disease because the patient does not feel any pain. Therefore, the development of a method for detecting this disease based on machin...

Simulation and beyond - Principles of effective obstetric training.

Simulation training provides a safe, non-judgmental environment where members of the multi-professional team can practice both their technical and non-technical skills. Poor teamwork and communication are recurring contributing factors to adverse maternal and neonatal outcomes. Simulation can improve outcomes and is now a compulsory part of the national training matrix. Components of successful tr...

Hydration Status and Fluid Replacement Strategies of High-Performance Adolescent Athletes: An Application of Machine Learning to Distinguish Hydration Characteristics.

There are limited data on the fluid balance characteristics and fluid replenishment behaviors of high-performance adolescent athletes. The heterogenei...

Design and Simulation Analysis of a Robot-Assisted Gait Trainer with the PBWS System.

In response to the ever-increasing demand of lower limb rehabilitation, this paper presents a novel robot-assisted gait trainer (RGT) to assist the el...

Factors That Influence Risk Perceptions and Successful COVID-19 Vaccination Communication Campaigns With American Indians.

COVID-19 vaccinations are the primary tool to end the pandemic. However, vaccine hesitancy continues to be a barrier to herd-immunity in the United St...

Aren't Pediatric Patients Just Little Adults? Artificial Intelligence Ultrasound Algorithms Beg to Differ.

Robust seed germination prediction using deep learning and RGB image data.

Achieving seed germination quality standards poses a real challenge to seed companies as they are compelled to abide by strict certification rules, wh...

Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease.

BACKGROUND & AIMS: Malnutrition is persistent in 50%-75% of children with congenital heart disease (CHD) after surgery, and early prediction is crucia...

Robotic, totally endoscopic atrial septal defect repair.

Atrial septal defect accounts for 10-15% of congenital heart disease cases. Small-diameter atrial septal defects diagnosed during infancy or early adu...

A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study.

INTRODUCTION: Outcome predictions of patients with congenital diaphragmatic hernia (CDH) still have some limitations in the prenatal estimate of postn...

Development and Validation of a Deep Learning Strategy for Automated View Classification of Pediatric Focused Assessment With Sonography for Trauma.

OBJECTIVE: Pediatric focused assessment with sonography for trauma (FAST) is a sequence of ultrasound views rapidly performed by clinicians to diagnos...

Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease.

Medicines and agricultural biocides are often discovered using large phenotypic screens across hundreds of compounds, where visible effects of whole o...

Deep learning is widely applicable to phenotyping embryonic development and disease.

Genome editing simplifies the generation of new animal models for congenital disorders. However, the detailed and unbiased phenotypic assessment of al...

Changes in outcomes and operative trends with pediatric robot-assisted resection of choledochal cyst.

BACKGROUND: This study aimed to report our experience with a robot-assisted resection of choledochal cysts (CCs) in pediatric patients, especially foc...

Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach.

There is increasing evidence that patient heterogeneity significantly hinders advancement in clinical trials and individualized care. This study aimed...

The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review.

Machine learning uses historical data to make predictions about new data. It has been frequently applied in healthcare to optimise diagnostic classifi...

Robot-assisted stereoelectroencephalography in young children: technical challenges and considerations.

Robot-assisted stereoelectroencephalography (sEEG) is frequently employed to localize epileptogenic zones in patients with medically refractory epilep...

Machine Learning-Based Single Cell and Integrative Analysis Reveals That Baseline mDC Predisposition Correlates With Hepatitis B Vaccine Antibody Response.

Vaccination to prevent infectious disease is one of the most successful public health interventions ever developed. And yet, variability in individual...

Using deep learning to classify pediatric posttraumatic stress disorder at the individual level.

BACKGROUND: Children exposed to natural disasters are vulnerable to developing posttraumatic stress disorder (PTSD). Previous studies using resting-st...

An Intervention by and for Transgender Women Living With HIV: Study Protocol for a Two-Arm Randomized Controlled Trial Testing the Efficacy of " to Improve HIV Care Outcomes.

Transgender women (assigned "male" at birth but who do not identify as male) are disproportionately impacted by HIV and experience unique barriers an...