AIMC Topic: Child, Preschool
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From marker to markerless: Validating DeepLabCut for 2D sagittal plane gait analysis in adults and newly walking toddlers.
Journal of biomechanics
Apr 18, 2025
The use of 3D marker-based motion analysis systems is considered the gold standard for tracking limb movements. However, these systems are expensive, limited to laboratory settings, and difficult to apply when studying paediatric populations. Therefo...
Integrating Machine Learning and Follow-Up Variables to Improve Early Detection of Hepatocellular Carcinoma in Tyrosinemia Type 1: A Multicenter Study.
International journal of molecular sciences
Apr 18, 2025
Hepatocellular carcinoma (HCC) is a major complication of tyrosinemia type 1 (HT-1), an inborn error of metabolism affecting tyrosine catabolism. The risk of HCC is higher in late diagnoses despite treatment. Alpha-fetoprotein (AFP) is widely used to...
Alkaline Phosphatase
Adult
Alanine Transaminase
Biomarkers, Tumor
Tyrosinemias
Male
Humans
Middle Aged
Carcinoma, Hepatocellular
Retrospective Studies
Adolescent
Machine Learning
Infant
Longitudinal Studies
Female
Follow-Up Studies
Child, Preschool
Infant, Newborn
Early Detection of Cancer
Child
Young Adult
alpha-Fetoproteins
Liver Neoplasms
MALDI-TOF mass spectrometry combined with machine learning algorithms to identify protein profiles related to malaria infection in human sera from Côte d'Ivoire.
Malaria journal
Apr 18, 2025
BACKGROUND: In sub-Saharan Africa, Plasmodium falciparum is the most prevalent species of malaria parasites. In endemic areas, malaria is mainly diagnosed using microscopy or rapid diagnostic tests (RDTs), which have limited sensitivity, and microsco...
A deep learning approach for blood glucose monitoring and hypoglycemia prediction in glycogen storage disease.
Scientific reports
Apr 15, 2025
Glycogen storage disease (GSD) is a group of rare inherited metabolic disorders characterized by abnormal glycogen storage and breakdown. These disorders are caused by mutations in G6PC1, which is essential for proper glucose storage and metabolism. ...
A retrospective study using machine learning to develop predictive model to identify rotavirus-associated acute gastroenteritis in children.
PeerJ
Apr 14, 2025
BACKGROUND: Rotavirus is the leading cause of severe dehydrating diarrhea in children under 5 years worldwide. Timely diagnosis is critical, but access to confirmatory testing is limited in hospital settings. Machine learning (ML) models have shown p...
Using machine learning to investigate the influence of the prenatal chemical exposome on neurodevelopment of young children.
Neurotoxicology
Apr 11, 2025
Research investigating the prenatal chemical exposome and child neurodevelopment has typically focused on a limited number of chemical exposures and controlled for sociodemographic factors and maternal mental health. Emerging machine learning approac...
Explainable AI for enhanced accuracy in malaria diagnosis using ensemble machine learning models.
BMC medical informatics and decision making
Apr 11, 2025
BACKGROUND: Malaria, an infectious disease caused by protozoan parasites belonging to the Plasmodium genus, remains a significant public health challenge, with African regions bearing the heaviest burden. Machine learning techniques have shown great ...
Children on wheels: Identifying crash determinants using cluster correspondence analysis.
Accident; analysis and prevention
Apr 10, 2025
Child bicyclists (14 years old and younger) are among the most vulnerable road users, facing significant risks of crashes that often result in severe injuries or fatalities. This study aims to identify key factors influencing child bicyclist crashes ...
Using fuzzy decision support to create a positive mental health environment for preschoolers.
Scientific reports
Apr 10, 2025
The preschool period is a crucial time for behavioural and social-emotional development and the cultivation of mental well-being. Preschoolers may be affected by various traumatic problems. During this process, preschoolers may develop hazardous beha...
Exploring cortical excitability in children with cerebral palsy through lower limb robot training based on MI-BCI.
Scientific reports
Apr 10, 2025
This study aims to compare brain activity differences under the motor imagery-brain-computer interface (MI-BCI), motor imagery (MI), and resting (REST) paradigms through EEG microstate and functional connectivity (FC) analysis, providing a theoretica...
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