Pediatrics

A Deep Shared Multi-Scale Inception Network Enables Accurate Neonatal Quiet Sleep Detection With Limited EEG Channels.

In this paper, we introduce a new variation of the Convolutional Neural Network Inception block, called Sinc, for sleep stage classification in premature newborn babies using electroencephalogram (EEG). In practice, there are many medical centres where only a limited number of EEG channels are recorded. Existing automated algorithms mainly use multi-channel EEGs which perform poorly when fewer num...

Artificial Intelligence in Pediatric Pathology: The Extinction of a Medical Profession or the Key to a Bright Future?

Artificial Intelligence (AI) has become of increasing interest over the past decade. While digital image analysis (DIA) is already being used in radiology, it is still in its infancy in pathology. One of the reasons is that large-scale digitization of glass slides has only recently become available. With the advent of digital slide scanners, that digitize glass slides into whole slide images, many...

Defining Normal Ranges of Skeletal Muscle Area and Skeletal Muscle Index in Children on CT Using an Automated Deep Learning Pipeline: Implications for Sarcopenia Diagnosis.

Skeletal muscle area (SMA), representing skeletal muscle cross-sectional area at the L3 vertebral level, and skeletal muscle index (SMI), representin...

Incorporating Radiomics into Machine Learning Models to Predict Outcomes of Neuroblastoma.

Neuroblastoma is one of the most common pediatric cancers. This study used machine learning (ML) to predict the mortality and a few other investigated...

Pediatric age estimation from radiographs of the knee using deep learning.

OBJECTIVES: Age estimation, especially in pediatric patients, is regularly used in different contexts ranging from forensic over medicolegal to clinic...

A robotic magnetic resonance-guided high-intensity focused ultrasound platform for neonatal neurosurgery: Assessment of targeting accuracy and precision in a brain phantom.

BACKGROUND: Intraventricular hemorrhage (IVH) is one of the most serious neurovascular complications resulting from premature birth. It can result in ...

Research on Human Behavior Modeling of Sports Culture Communication in Industrial 4.0 Intelligent Management.

With the advent of the information age, Internet technology and computer technology are gradually applied in various fields. Human daily life is insep...

Autonomous artificial intelligence in pediatric radiology: the use and perception of BoneXpert for bone age assessment.

BACKGROUND: The autonomous artificial intelligence (AI) system for bone age rating (BoneXpert) was designed to be used in clinical radiology practice ...

Classification and Detection of Autism Spectrum Disorder Based on Deep Learning Algorithms.

Autism spectrum disorder (ASD) is a type of mental illness that can be detected by using social media data and biomedical images. Autism spectrum diso...

Construction of a Physical and Medical Care Integrated Model for the Elderly in the Community Based on Artificial Intelligence and Machine Learning.

With the increasingly serious population aging, economic system transformation, and social transformation, the elderly have an increasingly strong dem...

Impact of Age and Sex on COVID-19 Severity Assessed From Radiologic and Clinical Findings.

BACKGROUND: Data on the epidemiological characteristics and clinical features of COVID-19 in patients of different ages and sex are limited. Existing ...

Care robot research and development plan for disability and aged care in Korea: A mixed-methods user participation study.

The population of Korea is aging rapidly, and this has led to a care burden for caregivers. Without adequate caregivers to address the increased burde...

Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants.

This study aimed to develop quantitative assessments of spontaneous movements in high-risk preterm infants based on a deep learning algorithm. Video i...

Prediction of clinical trial enrollment rates.

Clinical trials represent a critical milestone of translational and clinical sciences. However, poor recruitment to clinical trials has been a long st...

Radiation Dose Reduction for 80-kVp Pediatric CT Using Deep Learning-Based Reconstruction: A Clinical and Phantom Study.

Deep learning-based reconstruction (DLR) may facilitate CT radiation dose reduction, but a paucity of literature has compared lower-dose DLR images w...

Prediction of Neonatal Respiratory Distress Biomarker Concentration by Application of Machine Learning to Mid-Infrared Spectra.

The authors of this study developed the use of attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) combined with machine l...

Deep learning forecasting using time-varying parameters of the SIRD model for Covid-19.

Accurate epidemiological models are necessary for governments, organizations, and individuals to respond appropriately to the ongoing novel coronaviru...

Evolution of hospitalized patient characteristics through the first three COVID-19 waves in Paris area using machine learning analysis.

Characteristics of patients at risk of developing severe forms of COVID-19 disease have been widely described, but very few studies describe their evo...

Robot-Assisted Laparoscopic Ureterocalicostomy in the Setting of Ureteropelvic Junction Obstruction: A Multi-Institutional Cohort.

PURPOSE: Recurrent ureteropelvic junction obstruction (UPJO) after failed pyeloplasty is a complex surgical dilemma. The robot-assisted laparoscopic u...

Development and Evaluation of an Automated Approach to Detect Weight Abnormalities in Pediatric Weight Charts.

Inaccurate body weight measures can cause critical safety events in clinical settings as well as hindering utilization of clinical data for retrospect...