AIMC Topic: Child, Preschool

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Discrimination of Dengue Diseases in Children Using Surface-Enhanced Raman Spectroscopy Coupled with Machine Learning Approaches.

Analytical chemistry
This study introduces a novel approach to dengue diagnostics by leveraging surface-enhanced Raman spectroscopy (SERS) coupled to machine learning. This method addresses the critical need for rapid and accurate identification of dengue virus (DENV) in...

Developing age-specific protocols for pediatric voice databases in artificial intelligence research.

International journal of pediatric otorhinolaryngology
INTRODUCTION: Children's voice and communication abilities evolve with age, necessitating tailored protocols for accurate analysis. Distinct vocal properties and communication styles across developmental stages require specific tasks. The absence of ...

Employing machine learning for early detection of poly-victimization in rural children: a survey study in China's Chaoshan region.

BMC public health
BACKGROUND: Poly-victimization (PV), encompassing multiple forms of victimization including physical abuse, emotional maltreatment, neglect, and peer violence, poses a significant public health challenge among children, particularly in rural areas wi...

Predictive analysis of pediatric gastroenteritis risk factors and seasonal variations using VGG Dense HybridNetClassifier a novel deep learning approach.

Scientific reports
Pediatric gastroenteritis is a major reason for sickness and death among children worldwide, especially in places where healthcare and clean sanitation are scarce. Conventional methods of diagnosis overlook possible risks and seasonal trends, which r...

Identification of proliferating neural progenitors in the adult human hippocampus.

Science (New York, N.Y.)
Continuous adult hippocampal neurogenesis is involved in memory formation and mood regulation but is challenging to study in humans. Difficulties finding proliferating progenitor cells called into question whether and how new neurons may be generated...

Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI.

Scientific reports
Automated segmentation of pediatric brain tumors (PBTs) can support precise diagnosis and treatment monitoring, but it is still poorly investigated in literature. This study proposes two different Deep Learning approaches for semantic segmentation of...

The construction of HMME-PDT efficacy prediction model for port-wine stain based on machine learning algorithms.

Scientific reports
Hematoporphyrin monomethyl ether-photodynamic therapy (HMME-PDT) is a safe and effective treatment for port-wine stain (PWS). Comprehensive methods for predicting HMME-PDT efficacy based on clinical factors are lacking. This study aims to develop and...

Attentional responses in toddlers: A protocol for assessing the impact of a robotic animated animal and a real dog.

PloS one
BACKGROUND: Attentional processes in toddlers are characterized by a state of alertness in which they focus their waking state for short periods. It is essential to develop assessment and attention stimulation protocols from an early age to improve t...

Expert-augmented machine learning for predicting extubation readiness in the pediatric intensive care unit.

BMC medical informatics and decision making
BACKGROUND: Determining extubation readiness in pediatric intensive care units (PICU) is challenging. We used expert-augmented machine learning (EAML), a method that combines machine learning with human expert knowledge, to predict successful extubat...

Decoding the diagnostic biomarkers of mitochondrial dysfunction related gene variants in pediatric T cell acute lymphoblastic leukemia.

Scientific reports
Mitochondrial dysfunction is crucial in the pathogenesis and drug resistance of pediatric T-cell acute lymphoblastic leukemia (T-ALL), a malignant hematological disorder with unrestrained proliferation of immature T-cells. Therefore, the primary obje...