AIMC Topic: Child

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Behavioral Data Analysis of Robot-Assisted Autism Spectrum Disorder (ASD) Interventions Based on Lattice Computing Techniques.

Sensors (Basel, Switzerland)
Recent years have witnessed the proliferation of social robots in various domains including special education. However, specialized tools to assess their effect on human behavior, as well as to holistically design social robot applications, are often...

Using the Social Robot NAO for Emotional Support to Children at a Pediatric Emergency Department: Randomized Clinical Trial.

Journal of medical Internet research
BACKGROUND: Social robots (SRs) have been used for improving anxiety in children in stressful clinical situations, such as during painful procedures. However, no studies have yet been performed to assess their effect in children while waiting for eme...

Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology.

Journal of the American Society of Nephrology : JASN
BACKGROUND: Untargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding of pediatric CKD causes. We sought to identify metabolomic signatures in pediatric CKD b...

Bone suppression on pediatric chest radiographs via a deep learning-based cascade model.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Bone suppression images (BSIs) of chest radiographs (CXRs) have been proven to improve diagnosis of pulmonary diseases. To acquire BSIs, dual-energy subtraction (DES) or a deep-learning-based model trained with DES-based BSI...

Fine motor impairment in children with epilepsy: Relations with seizure severity and lateralizing value.

Epilepsy & behavior : E&B
Motor skill deficits are common in epilepsy. The Grooved Pegboard Test (GPT) is the most commonly used fine motor task and is included in the NIH Common Data Elements Battery for the assessment of epilepsy. However, there are limited data on its util...

A pilot study of machine-learning based automated planning for primary brain tumours.

Radiation oncology (London, England)
PURPOSE: High-quality radiotherapy (RT) planning for children and young adults with primary brain tumours is essential to minimize the risk of late treatment effects. The feasibility of using automated machine-learning (ML) to aid RT planning in this...

Deep Learning-Assisted Diagnosis of Pediatric Skull Fractures on Plain Radiographs.

Korean journal of radiology
OBJECTIVE: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children.

Derivation of a natural language processing algorithm to identify febrile infants.

Journal of hospital medicine
BACKGROUND: Diagnostic codes can retrospectively identify samples of febrile infants, but sensitivity is low, resulting in many febrile infants eluding detection. To ensure study samples are representative, an improved approach is needed.