AIMC Topic: Child

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Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial.

PloS one
RATIONALE: Clinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma care but remain under-studied through clinical trials.

Documentation of Shared Decisionmaking in the Emergency Department.

Annals of emergency medicine
STUDY OBJECTIVE: While patient-centered communication and shared decisionmaking are increasingly recognized as vital aspects of clinical practice, little is known about their characteristics in real-world emergency department (ED) settings. We constr...

Affective Communication for Socially Assistive Robots (SARs) for Children with Autism Spectrum Disorder: A Systematic Review.

Sensors (Basel, Switzerland)
Research on affective communication for socially assistive robots has been conducted to enable physical robots to perceive, express, and respond emotionally. However, the use of affective computing in social robots has been limited, especially when s...

Ensuring Adequate Development and Appropriate Use of Artificial Intelligence in Pediatric Medical Imaging.

AJR. American journal of roentgenology
Of over 100 FDA-cleared artificial intelligence (AI) tools for triage, detection, or diagnosis in medical imaging, only one is cleared for use in children. Thus, children may be unable to benefit from the advances that AI provides to adults. Furtherm...

Graph-theory based degree centrality combined with machine learning algorithms can predict response to treatment with antiepileptic medications in children with epilepsy.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND AND PURPOSE: The purpose of the current study is to detect changes of graph-theory-based degree centrality (DC) and their relationship with the clinical treatment effects of anti-epileptic drugs (AEDs) for patients with childhood absence e...

Future of machine learning in paediatrics.

Archives of disease in childhood
Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn without being explicitly programmed, through a combination of statistics and computer science. It encompasses a variety of techniques used to analyse an...

Comparative Outcomes of Double-J and Cutaneous Pyeloureteral Stents in Pediatric Robot-Assisted Laparoscopic Pyeloplasty.

Journal of endourology
Comparative outcome studies investigating internal Double-J (DJ) and externalized stents have primarily been performed for open and laparoscopic pyeloplasty, with a paucity of literature surrounding outcomes in robot-assisted laparoscopic pyeloplast...

Value of Rehabilitation Training for Children with Cerebral Palsy Diagnosed and Analyzed by Computed Tomography Imaging Information Features under Deep Learning.

Journal of healthcare engineering
To analyze the brain CT imaging data of children with cerebral palsy (CP), deep learning-based electronic computed tomography (CT) imaging information characteristics were used, thereby providing help for the rehabilitation analysis of children with ...

MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks.

NeuroImage
Magnetoencephalography (MEG) is a functional neuroimaging tool that records the magnetic fields induced by neuronal activity; however, signal from non-neuronal sources can corrupt the data. Eye-blinks, saccades, and cardiac activity are three of the ...

Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine.

Brain and behavior
OBJECTIVE: Autism spectrum disorder (ASD) is a neurodevelopmental condition with a heterogeneous phenotype. The role of biomarkers in ASD diagnosis has been highlighted; cortical thickness has proved to be involved in the etiopathogenesis of ASD core...