AIMC Topic: Infant, Newborn
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Modeling the number of new cases of childhood type 1 diabetes using Poisson regression and machine learning methods; a case study in Saudi Arabia.
PloS one
Apr 25, 2025
Diabetes mellitus stands out as one of the most prevalent chronic conditions affecting pediatric populations. The escalating incidence of childhood type 1 diabetes (T1D) globally is a matter of increasing concern. Developing an effective model that l...
Prediction of significant congenital heart disease in infants and children using continuous wavelet transform and deep convolutional neural network with 12-lead electrocardiogram.
BMC pediatrics
Apr 24, 2025
BACKGROUND: Congenital heart disease (CHD) affects approximately 1% of newborns and is a leading cause of mortality in early childhood. Despite the importance of early detection, current screening methods, such as pulse oximetry and auscultation, hav...
Birth weight prediction using artificial intelligence-based placental assessment from macroscopic photo: a retrospective study.
Placenta
Apr 23, 2025
BACKGROUND: This study aimed to predict newborn birth weight through multifactorial analysis of macroscopic placental images using artificial intelligence (AI).
Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study.
Journal of medical Internet research
Apr 23, 2025
BACKGROUND: Retinopathy of prematurity (ROP) is the leading preventable cause of childhood blindness. A timely intravitreal injection of antivascular endothelial growth factor (anti-VEGF) is required to prevent retinal detachment with consequent visi...
Using deep learning for estimation of time-since-injury in pediatric accidental fractures.
Pediatric radiology
Apr 22, 2025
BACKGROUND: Estimating time-since-injury of healing fractures is imprecise, encompassing excessively wide timeframes. Most injured children are evaluated at non-children's hospitals, yet pediatric radiologists can disagree with up to one in six skele...
Development and external validation of a machine learning model to predict bronchopulmonary dysplasia using dynamic factors.
Scientific reports
Apr 19, 2025
We hypothesized that incorporating postnatal dynamic factors would enhance the prediction accuracy of bronchopulmonary dysplasia in preterm infants. This retrospective cohort study included neonates born before 32 weeks of gestation at Seoul National...
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...
Tyrosinemias
Adult
Early Detection of Cancer
Biomarkers, Tumor
alpha-Fetoproteins
Humans
Liver Neoplasms
Male
Infant
Alkaline Phosphatase
Retrospective Studies
Machine Learning
Carcinoma, Hepatocellular
Middle Aged
Adolescent
Alanine Transaminase
Female
Longitudinal Studies
Child, Preschool
Infant, Newborn
Follow-Up Studies
Child
Young Adult
Validation of a machine learning algorithm for identifying infants at risk of hypoxic ischaemic encephalopathy in a large unseen data set.
Archives of disease in childhood. Fetal and neonatal edition
Apr 17, 2025
OBJECTIVE: To validate a hypoxic ischaemic encephalopathy (HIE) prediction algorithm to identify infants at risk of HIE immediately after birth using readily available clinical data.
Prediction of retinopathy of prematurity development and treatment need with machine learning models.
BMC ophthalmology
Apr 10, 2025
BACKGROUND: To evaluate the effectiveness of machine learning (ML) models in predicting the occurrence of retinopathy of prematurity (ROP) and treatment need.
Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data.
Scientific reports
Apr 7, 2025
Early diagnosis and access to resources, support and therapy are critical for improving long-term outcomes for children with autism spectrum disorder (ASD). ASD is typically detected using a case-finding approach based on symptoms and family history,...
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