AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Infant, Newborn

Showing 121 to 130 of 704 articles

Clear Filters

Uncovering early predictors of cerebral palsy through the application of machine learning: a case-control study.

BMJ paediatrics open
OBJECTIVE: Cerebral palsy (CP) is a group of neurological disorders with profound implications for children's development. The identification of perinatal risk factors for CP may lead to improved preventive and therapeutic strategies. This study aime...

Next-generation pediatric care: nanotechnology-based and AI-driven solutions for cardiovascular, respiratory, and gastrointestinal disorders.

World journal of pediatrics : WJP
BACKGROUND: Global pediatric healthcare reveals significant morbidity and mortality rates linked to respiratory, cardiac, and gastrointestinal disorders in children and newborns, mostly due to the complexity of therapeutic management in pediatrics an...

High-level feature-guided attention optimized neural network for neonatal lateral ventricular dilatation prediction.

Medical physics
BACKGROUND: Periventricular-intraventricular hemorrhage can lead to posthemorrhagic ventricular dilatation or even posthemorrhagic hydrocephalus if not detected promptly. Sequential cranial ultrasound scans are typically used for their diagnoses. Non...

Exploring the determinants of under-five mortality and morbidity from infectious diseases in Cambodia-a traditional and machine learning approach.

Scientific reports
Cambodia has made progress in reducing the under-five mortality rate and burden of infectious diseases among children over the last decades. However the determinants of child mortality and morbidity in Cambodia is not well understood, and no recent a...

Risk factors for the time to development of retinopathy of prematurity in premature infants in Iran: a machine learning approach.

BMC ophthalmology
BACKGROUND: Retinopathy of prematurity (ROP), is a preventable leading cause of blindness in infants and is a condition in which the immature retina experiences abnormal blood vessel growth. The development of ROP is multifactorial; nevertheless, the...

Separating group- and individual-level brain signatures in the newborn functional connectome: A deep learning approach.

NeuroImage
Recent studies indicate that differences in cognition among individuals may be partially attributed to unique brain wiring patterns. While functional connectivity (FC)-based fingerprinting has demonstrated high accuracy in identifying adults, early s...

Machine learning-causal inference based on multi-omics data reveals the association of altered gut bacteria and bile acid metabolism with neonatal jaundice.

Gut microbes
Early identification of neonatal jaundice (NJ) appears to be essential to avoid bilirubin encephalopathy and neurological sequelae. The interaction between gut microbiota and metabolites plays an important role in early life. It is unclear whether th...

Identification of key metabolism-related genes and pathways in spontaneous preterm birth: combining bioinformatic analysis and machine learning.

Frontiers in endocrinology
BACKGROUND: Spontaneous preterm birth (sPTB) is a global disease that is a leading cause of death in neonates and children younger than 5 years of age. However, the etiology of sPTB remains poorly understood. Recent evidence has shown a strong associ...

Modeling the relationship between maternal health and infant behavioral characteristics based on machine learning.

PloS one
This study investigates the impact of maternal health on infant development by developing a mathematical model that delineates the relationship between maternal health indicators and infant behavioral characteristics and sleep quality. The main contr...