AI Medical Compendium Topic:
Child

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PedBotLab: A Novel Video Game-Based Robotic Ankle Platform Created for Therapeutic Exercise for Children With Neurological Impairments.

Physical & occupational therapy in pediatrics
AIMS: Assess the potential benefits of using PedBotLab, a clinic based robotic ankle platform with integrated video game software, to improve ankle active and passive range of motion, strength, selective motor control, gait efficiency, and balance.

Development of a deep learning model that predicts critical events of pediatric patients admitted to general wards.

Scientific reports
Early detection of deteriorating patients is important to prevent life-threatening events and improve clinical outcomes. Efforts have been made to detect or prevent major events such as cardiopulmonary resuscitation, but previously developed tools ar...

Advances in pediatric perioperative care using artificial intelligence.

Current opinion in anaesthesiology
PURPOSE OF THIS REVIEW: This article explores how artificial intelligence (AI) can be used to evaluate risks in pediatric perioperative care. It will also describe potential future applications of AI, such as models for airway device selection, contr...

Assisted Robots in Therapies for Children with Autism in Early Childhood.

Sensors (Basel, Switzerland)
Children with autism spectrum disorder (ASD) have deficits that affect their social relationships, communication, and flexibility in reasoning. There are different types of treatment (pharmacological, educational, psychological, and rehabilitative). ...

Early childhood caries detection using smartphone artificial intelligence.

European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry

Machine learning algorithms to predict outcomes in children and adolescents with COVID-19: A systematic review.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVES: We aimed to analyze the study designs, modeling approaches, and performance evaluation metrics in studies using machine learning techniques to develop clinical prediction models for children and adolescents with COVID-19.

Bone age assessment based on three-dimensional ultrasound and artificial intelligence compared with paediatrician-read radiographic bone age: protocol for a prospective, diagnostic accuracy study.

BMJ open
INTRODUCTION: Radiographic bone age (BA) assessment is widely used to evaluate children's growth disorders and predict their future height. Moreover, children are more sensitive and vulnerable to X-ray radiation exposure than adults. The purpose of t...

Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths.

Journal of autism and developmental disorders
PURPOSE: Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are conditions that similarly alter cognitive functioning ability and challenge the social interaction, attention, and communication skills of affected indivi...

Prescription eyeglasses as a forensic physical evidence: Prediction of age based on refractive error measures using machine learning algorithm.

Journal of forensic sciences
Refractive errors (RE) are commonly reported visual impairment problems worldwide. Previous clinical studies demonstrated age-related changes in human eyes. We hypothesized that the binocular RE metrics including sphere and cylinder power, axis orien...

Deep learning-based, fully automated, pediatric brain segmentation.

Scientific reports
The purpose of this study was to demonstrate the performance of a fully automated, deep learning-based brain segmentation (DLS) method in healthy controls and in patients with neurodevelopmental disorders, SCN1A mutation, under eleven. The whole, cor...