Pediatrics

Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD.

To identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely during pregnancy and birth. We used a supervised machine learning algorithm with a cross-validation technique to classify NT and ASD babies and performed...

What makes AI 'intelligent' and 'caring'? Exploring affect and relationality across three sites of intelligence and care.

This paper scrutinises how AI and robotic technologies are transforming the relationships between people and machines in new affective, embodied and relational ways. Through investigating what it means to exist as human 'in relation' to AI across health and care contexts, we aim to make three main contributions. (1) We start by highlighting the complexities of philosophical issues surrounding the ...

One-shot object parsing in newborn chicks.

Controlled-rearing studies provide the unique opportunity to examine which psychological mechanisms are present at birth and which mechanisms emerge f...

Image-based deep learning in diagnosing the etiology of pneumonia on pediatric chest X-rays.

PURPOSE: Comparing the efficacy of a deep-learning model in classifying the etiology of pneumonia on pediatric chest X-rays (CXRs) with that of human ...

Image processing and machine learning for telehealth craniosynostosis screening in newborns.

OBJECTIVE: The authors sought to evaluate the accuracy of a novel telehealth-compatible diagnostic software system for identifying craniosynostosis wi...

Robot-assisted stereoelectroencephalography electrode placement in twenty-three pediatric patients: a high-resolution analysis of individual lead placement time and accuracy at a single institution.

PURPOSE: We describe a detailed evaluation of predictors associated with individual lead placement efficiency and accuracy for 261 stereoelectroenceph...

Identifying Supportive Student Factors for Mindset Interventions: A Two-model Machine Learning Approach.

Growth mindset interventions foster students' beliefs that their abilities can grow through effort and appropriate strategies. However, not every stud...

Machine Learning for Accurate Intraoperative Pediatric Middle Ear Effusion Diagnosis.

OBJECTIVES: Misdiagnosis of acute and chronic otitis media in children can result in significant consequences from either undertreatment or overtreatm...

Socially Assistive Robots in Aged Care: Ethical Orientations Beyond the Care-Romantic and Technology-Deterministic Gaze.

Socially Assistive Robots (SARs) are increasingly conceived as applicable tools to be used in aged care. However, the use carries many negative and po...

Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation.

BACKGROUND: Social media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, li...

Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease.

BACKGROUND: Annual influenza vaccination is an important public health measure to prevent influenza infections and is strongly recommended for cardiov...

GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images.

The placenta is the first organ to form and performs the functions of the lung, gut, kidney, and endocrine systems. Abnormalities in the placenta caus...

Individualized prediction of COVID-19 adverse outcomes with MLHO.

The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led t...

Generation of Dual functional Nanobody-Nanoluciferase Fusion and its potential in Bioluminescence Enzyme Immunoassay for trace Glypican-3 in Serum.

Glypican-3 (GPC3) is a serological biomarker for the diagnosis of Hepatocellular carcinoma (HCC), but it is a challenging task to develop a bioassay f...

Prediction of newborn's body mass index using nationwide multicenter ultrasound data: a machine-learning study.

BACKGROUND: This study introduced machine learning approaches to predict newborn's body mass index (BMI) based on ultrasound measures and maternal/del...

Diffusion histology imaging differentiates distinct pediatric brain tumor histology.

High-grade pediatric brain tumors exhibit the highest cancer mortality rates in children. While conventional MRI has been widely adopted for examining...

A Myocardial Segmentation Method Based on Adversarial Learning.

Congenital heart defects (CHD) are structural imperfections of the heart or large blood vessels that are detected around birth and their symptoms vary...

Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission.

A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subseq...

Predicting presumed serious infection among hospitalized children on central venous lines with machine learning.

BACKGROUND: Presumed serious infection (PSI) is defined as a blood culture drawn and new antibiotic course of at least 4 days among pediatric patients...

Genetics and voice production in childhood and adolescence - a review.

Adolescence is a challenging time of change in voicing, normally and in pathology. An increased focus on voice production in relation to genetics can ...