AIMC Topic: Infant, Newborn

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Dose-response association between OGTT and adverse perinatal outcomes after IVF treatment: A cohort study based on a twin population.

Journal of endocrinological investigation
BACKGROUND: Investigate the association between Oral Glucose Tolerance Test (OGTT) after in vitro fertilization (IVF) treatment and adverse maternal and neonatal outcomes in twin pregnancies.

Development and external validation of a machine learning model to predict bronchopulmonary dysplasia using dynamic factors.

Scientific reports
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
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...

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
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.

Exclusive breastfeeding is associated with the gut microbiome maturation in infants according to delivery mode.

Gut microbes
Exclusive breastfeeding (EBF) plays a crucial role in infant gut microbiome assembly and development. However, few studies have investigated the effects of EBF in restoring a perturbed microbiome. In this study, we applied whole metagenomic sequencin...

Prediction of retinopathy of prematurity development and treatment need with machine learning models.

BMC ophthalmology
BACKGROUND: To evaluate the effectiveness of machine learning (ML) models in predicting the occurrence of retinopathy of prematurity (ROP) and treatment need.

Improving image quality on pediatric and neonatal radiography using AI-based compensation for image degradation.

Japanese journal of radiology
PURPOSE: To evaluate the impact of an AI-based, noise reduction technique for compensation of image degradation on pediatric and neonatal chest and abdomen radiography using a visual grading analysis.

Transformer-based deep learning ensemble framework predicts autism spectrum disorder using health administrative and birth registry data.

Scientific reports
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,...

DUAL VALIDATION ANALYSIS OF SERUM CYP3A4 IN PREDICTING NEC IN PRETERM INFANTS.

Shock (Augusta, Ga.)
Objective: Necrotizing enterocolitis (NEC) is a life-threatening condition in premature infants, where timely diagnosis and intervention are crucial. This study investigated the potential of serum CYP3A4 as an early predictive biomarker for NEC and d...

Artificial intelligence-based non-invasive bilirubin prediction for neonatal jaundice using 1D convolutional neural network.

Scientific reports
Neonatal jaundice, characterized by elevated bilirubin levels causing yellow discoloration of the skin and eyes in newborns, is a critical condition requiring accurate and timely diagnosis. This study proposes a novel approach using 1D Convolutional ...