AIMC Topic: Child

Clear Filters Showing 2811 to 2820 of 3433 articles

Using Artificial Intelligence and Machine Learning to Promote Child Health Equity.

Pediatrics
Artificial intelligence (AI) and machine learning (ML), used injudiciously, have the potential to exacerbate health inequalities. Conversely, there is a potential to use ML to give insight into the impact of socioeconomic factors, which allows us to ...

[The alliance of cybersecurity and artificial intelligence in digital healthcare: challenges and solutions from the EU CYLCOMED RWD project.].

Recenti progressi in medicina
The availability of health technologies has facilitated improvements in the quality of care, playing a vital role in both hospital environments and remote patient monitoring. However, the growing complexity of these technologies has also led to an in...

Comparison of dengue, chikungunya, and Zika among children in Nicaragua across 18 years: a single-centre, prospective cohort study.

The Lancet. Child & adolescent health
BACKGROUND: Dengue, chikungunya, and Zika are diseases of major human concern. Differential diagnosis of these three diseases is complicated in children and adolescents due to overlapping clinical features (signs, symptoms, and complete blood count r...

Time-series X-ray image prediction of dental skeleton treatment progress via neural networks.

Computers in biology and medicine
Accurate prediction of skeletal changes during orthodontic treatment in growing patients remains challenging due to significant individual variability in craniofacial growth and treatment responses. Conventional methods, such as support vector regres...

Accurate Paediatric Brain Tumour Classification Through Improved Quantitative Analysis of H MR Imaging and Spectroscopy.

NMR in biomedicine
Multimodality imaging is an emerging research topic in neuro-oncology for its potential of being able to demonstrate tumours in a more comprehensive manner. Diffusion-weighted magnetic resonance imaging (dMRI) and proton magnetic resonance spectrosco...

Machine learning-assisted tacrolimus dose optimization in childhood- onset systemic lupus erythematosus through population pharmacokinetic modeling.

Computers in biology and medicine
OBJECTIVE: This study aimed to improve treatment effectiveness in childhood-onset systemic lupus erythematosus (cSLE) by developing machine learning algorithms integrated with pharmacokinetic parameters to predict individualized tacrolimus dosing for...

Impact of environmental pollution on human health: Investigating the role of Polycyclic Aromatic Hydrocarbons in pediatric osteosarcoma.

Ecotoxicology and environmental safety
BACKGROUND: Polycyclic aromatic hydrocarbons (PAHs), widely emitted through industrial processes and vehicular exhaust, are recognized environmental carcinogens. Although PAH exposure has been linked to various malignancies, the specific molecular me...

Leveraging machine learning and single-cell RNA sequencing strategies to develop a risk prognosis scoring based on liquid-liquid phase separation feature genes in pediatric hepatoblastoma.

Computers in biology and medicine
BACKGROUND: Considerable evidence highlights the intricate association between liquid-liquid phase separation (LLPS) and tumorigenesis, progression, and therapy resistance. However, there has been limited exploration of the role of LLPS in hepatoblas...

Identification of inflammation-related biomarkers and therapeutic targets for neurogenic bladder fibrosis via multi-omics analysis.

Computers in biology and medicine
Inflammatory responses play a crucial role in the progression of pediatric neurogenic bladder (NB)-associated fibrosis; however, their specific contributions remain poorly understood. This study aimed to identify inflammation-related biomarkers for d...

Dosing prediction of valproic acid in pediatric patients with epilepsy: population pharmacokinetic model or machine learning model?

European journal of clinical pharmacology
PURPOSE: This study develops and compares population pharmacokinetics (PopPK) models and machine learning methods, including neural networks, to predict steady-state trough concentrations in pediatric patients and provide improved dosing recommendati...