AIMC Topic: Infant, Very Low Birth Weight

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Early prediction of intraventricular hemorrhage in very low birth weight infants using deep neural networks with attention in low-resource settings.

Scientific reports Mar 24, 2025
Early prediction of intraventricular hemorrhage (IVH) in very low-birthweight infants (VLBWIs) remains challenging because of multifactorial risk factors. IVH often occurs within a few hours after birth, yet its onset cannot be reliably predicted usi...
Cerebral Intraventricular Hemorrhage Infant, Very Low Birth Weight Deep Learning Cerebral Hemorrhage Birth Weight Gestational Age Humans Neural Networks, Computer Female Infant, Newborn Risk Factors Male
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Early prediction of mortality and morbidities in VLBW preterm neonates using machine learning.

Pediatric research Oct 8, 2024
BACKGROUND: Predicting mortality and specific morbidities before they occur may allow for interventions that may improve health trajectories.
Humans Machine Learning Male Bronchopulmonary Dysplasia ROC Curve Morbidity Infant Mortality Algorithms Retrospective Studies Female Infant, Newborn Infant, Premature Infant, Premature, Diseases Enterocolitis, Necrotizing Infant, Very Low Birth Weight
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Machine-learning-based evaluation of the usefulness of lactate for predicting neonatal mortality in preterm infants.

Pediatrics and neonatology Sep 25, 2024
BACKGROUND: Unlike in adult and pediatric patients, the usefulness of lactate in preterm infants has not been thoroughly discussed. This study aimed to evaluate whether the lactate level in the first hours of life is an important factor associated wi...
Humans Machine Learning Retrospective Studies Hospital Mortality Infant Mortality Lactic Acid Infant, Premature Infant, Very Low Birth Weight Republic of Korea Female Infant, Newborn Male
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Predicting severe intraventricular hemorrhage or early death using machine learning algorithms in VLBWI of the Korean Neonatal Network Database.

Scientific reports May 15, 2024
Severe intraventricular hemorrhage (IVH) in premature infants can lead to serious neurological complications. This retrospective cohort study used the Korean Neonatal Network (KNN) dataset to develop prediction models for severe IVH or early death in...
Female Infant, Newborn Male Cerebral Hemorrhage Infant, Very Low Birth Weight Infant, Premature Cerebral Intraventricular Hemorrhage Algorithms Databases, Factual Retrospective Studies Republic of Korea Humans Machine Learning
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Predicting early mortality and severe intraventricular hemorrhage in very-low birth weight preterm infants: a nationwide, multicenter study using machine learning.

Scientific reports May 12, 2024
Our aim was to develop a machine learning-based predictor for early mortality and severe intraventricular hemorrhage (IVH) in very-low birth weight (VLBW) preterm infants in Taiwan. We collected retrospective data from VLBW infants, dividing them int...
Humans Machine Learning Birth Weight Infant Mortality Infant, Premature Gestational Age Male Infant, Very Low Birth Weight Infant, Premature, Diseases Prognosis Infant Cerebral Intraventricular Hemorrhage Taiwan Retrospective Studies Cerebral Hemorrhage Female Infant, Newborn
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Machine learning-based analysis for prediction of surgical necrotizing enterocolitis in very low birth weight infants using perinatal factors: a nationwide cohort study.

European journal of pediatrics Mar 30, 2024
Early prediction of surgical necrotizing enterocolitis (sNEC) in preterm infants is important. However, owing to the complexity of the disease, identifying infants with NEC at a high risk for surgical intervention is difficult. We developed a machine...
Enterocolitis, Necrotizing Infant, Very Low Birth Weight Machine Learning Retrospective Studies Cohort Studies Infant, Newborn Republic of Korea Registries Infant, Premature Risk Factors Female Gestational Age Male Humans Infant, Premature, Diseases Risk Assessment
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Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort.

Scientific reports Nov 16, 2021
Despite the many comorbidities and high mortality rate in preterm infants with patent ductus arteriosus (PDA), therapeutic strategies vary depending on the clinical setting, and most studies of the related risk factors are based on small sample popul...
Infant, Premature Humans Risk Factors Models, Cardiovascular Artificial Intelligence Ductus Arteriosus, Patent Male Female Infant, Very Low Birth Weight Cohort Studies Infant, Newborn Registries
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A heterogeneity analysis of health-related quality of life in early adults born very preterm or very low birthweight across the sociodemographic spectrum.

Social science & medicine (1982) Sep 1, 2025
Preterm birth and very low birthweight (VP/VLBW) are associated with poorer health-related quality of life (HRQoL) outcomes extending into adulthood, yet it remains unclear how these effects differ across sociodemographic subgroups. This study aimed ...
Infant, Very Low Birth Weight Europe Bayes Theorem Sociodemographic Factors Maternal Age Educational Status Humans Adolescent Young Adult Infant, Extremely Premature Female Socioeconomic Factors Adult Quality of Life Longitudinal Studies Male Infant, Newborn Premature Birth
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Improving Neonatal Care with AI: Class Weight Optimization for Respiratory Distress Syndrome Prediction in Very Low Birth Weight Infants.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
In this study, we developed an AI model to predict Respiratory Distress Syndrome (RDS) in premature infants, aiming to reduce unnecessary treatment with artificial pulmonary surfactant. We analyzed data from 13,120 infants in 76 hospitals, considerin...
Infant, Very Low Birth Weight Respiratory Distress Syndrome, Newborn Infant, Newborn Male Support Vector Machine Artificial Intelligence Humans Infant, Premature Machine Learning Neural Networks, Computer Algorithms Female
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Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants.

Yonsei medical journal Jul 1, 2022
PURPOSE: The aims of the study were to develop and evaluate a machine learning model with which to predict postnatal growth failure (PGF) among very low birth weight (VLBW) infants.
Growth Disorders Humans Support Vector Machine Machine Learning Infant, Very Low Birth Weight Infant, Newborn Infant
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