AIMC Topic: Child

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Changes in outcomes and operative trends with pediatric robot-assisted resection of choledochal cyst.

Surgical endoscopy
BACKGROUND: This study aimed to report our experience with a robot-assisted resection of choledochal cysts (CCs) in pediatric patients, especially focusing on changes in outcomes and operative trends.

Natural Language Processing and Machine Learning Methods to Characterize Unstructured Patient-Reported Outcomes: Validation Study.

Journal of medical Internet research
BACKGROUND: Assessing patient-reported outcomes (PROs) through interviews or conversations during clinical encounters provides insightful information about survivorship.

The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review.

Cardiology in the young
Machine learning uses historical data to make predictions about new data. It has been frequently applied in healthcare to optimise diagnostic classification through discovery of hidden patterns in data that may not be obvious to clinicians. Congenita...

The pilot study of group robot intervention on pediatric inpatients and their caregivers, using 'new aibo'.

European journal of pediatrics
The study on robot-assisted therapy in a pediatric field has not been applied sufficiently in clinical settings. The purpose of this pilot study is to explore the potential therapeutic effects of a group robot intervention (GRI), using dog-like socia...

Robot-assisted stereoelectroencephalography in young children: technical challenges and considerations.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
Robot-assisted stereoelectroencephalography (sEEG) is frequently employed to localize epileptogenic zones in patients with medically refractory epilepsy (MRE). Its methodology is well described in adults, but less so in children. Given the limited in...

Machine Learning for Urodynamic Detection of Detrusor Overactivity.

Urology
OBJECTIVE: To develop a machine learning algorithm that identifies detrusor overactivity (DO) in Urodynamic Studies (UDS) in the spina bifida population. UDS plays a key role in assessment of neurogenic bladder in patients with spina bifida. Due to s...

Differential power of placebo across major psychiatric disorders: a preliminary meta-analysis and machine learning study.

Scientific reports
The placebo effect across psychiatric disorders is still not well understood. In the present study, we conducted meta-analyses including meta-regression, and machine learning analyses to investigate whether the power of placebo effect depends on the ...

Using deep learning to classify pediatric posttraumatic stress disorder at the individual level.

BMC psychiatry
BACKGROUND: Children exposed to natural disasters are vulnerable to developing posttraumatic stress disorder (PTSD). Previous studies using resting-state functional neuroimaging have revealed alterations in graph-based brain topological network metri...

Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children.

Nutrients
INTRODUCTION: Accurate assessment of resting energy expenditure (REE) can guide optimal nutritional prescription in critically ill children. Indirect calorimetry (IC) is the gold standard for REE measurement, but its use is limited. Alternatively, RE...

Enriching Human-Robot Interaction with Mobile App in Interventions of Children with Autism Spectrum Disorder.

Prilozi (Makedonska akademija na naukite i umetnostite. Oddelenie za medicinski nauki)
: Autism spectrum disorder (ASD) is a group of complex lifelong neurodevelopmental disorders, characterized by difficulties in social communication and stereotyped behaviours. Due to the increasing number of children with ASD, it is important to cont...