AIMC Topic: Child, Preschool

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Development and validation of an integrated residual-recurrent neural network model for automated heart murmur detection in pediatric populations.

Scientific reports
Congenital heart disease affects approximately 1% of children worldwide, with a number of cases in resource-limited settings remaining undiagnosed through school age. While cardiac auscultation is a key screening method, its effectiveness varies wide...

Using machine learning models based on cardiac magnetic resonance parameters to predict the prognostic in children with myocarditis.

BMC pediatrics
OBJECTIVE: To develop machine learning (ML) models incorporating explanatory cardiac magnetic resonance (CMR) parameters for predicting the prognosis of myocarditis in pediatric patients.

Artificial intelligence-guided distal radius fracture detection on plain radiographs in comparison with human raters.

Journal of orthopaedic surgery and research
BACKGROUND: The aim of this study was to compare the performance of artificial intelligence (AI) in detecting distal radius fractures (DRFs) on plain radiographs with the performance of human raters.

Metabolomics and machine learning identify urine metabolic characteristics and potential biomarkers for severe Mycoplasma pneumoniae pneumonia.

Scientific reports
To study the differences in the urine metabolome between pediatric patients with severe Mycoplasma pneumoniae pneumonia (SMPP) and those with general Mycoplasma pneumoniae pneumonia (GMPP) via non-targeted metabolomics method, and potential biomarker...

Predicting sepsis treatment decisions in the paediatric emergency department using machine learning: the AiSEPTRON study.

BMJ paediatrics open
BACKGROUND: Early identification of children at risk of sepsis in emergency departments (EDs) is crucial for timely treatment and improved outcomes. Existing risk scores and criteria for paediatric sepsis are not well-suited for early diagnosis in ED...

Single-cell RNA sequencing reveals immunological link between house dust mite allergy and childhood asthma.

Scientific reports
Allergic asthma in children is typically associated with house dust mites (HDM) as the key allergen. Nevertheless, the diagnostic rate remains below 60% due to the absence of specific symptoms and diagnostic markers, which hinders the implementation ...

Development and validation of an early diagnosis model for severe mycoplasma pneumonia in children based on interpretable machine learning.

Respiratory research
BACKGROUND: Pneumonia is a major threat to the health of children, especially those under the age of five. Mycoplasma  pneumoniae infection is a core cause of pediatric pneumonia, and the incidence of severe mycoplasma pneumoniae pneumonia (SMPP) has...

Characteristics of lung sounds in early infants using automated analysis.

European journal of pediatrics
UNLABELLED: A new lung sound analysis software program has been developed. It can automatically select a typical lung sound spectrogram and calculate lung sound parameters using machine learning programs. This study aimed to clarify lung sound charac...

Explainable predictive models of short stature and exploration of related environmental growth factors: a case-control study.

BMC endocrine disorders
BACKGROUND: Short stature is a prevalent pediatric endocrine disorder for which early detection and prediction are pivotal for improving treatment outcomes. However, existing diagnostic criteria often lack the necessary sensitivity and specificity be...

Children's attribution of mental states to humans and social robots assessed with the Theory of Mind Scale.

Scientific reports
The present work examined children's attribution of psychological properties to inanimate agents in two experiments. In Study 1, an Interview Task and the Theory of Mind Scale (ToM Scale) were administered to 4-year-olds with either a human or a huma...