AIMC Topic: Birth Weight

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A Machine Learning-Based Intrauterine Growth Restriction (IUGR) Prediction Model for Newborns.

Indian journal of pediatrics Aug 9, 2022
Intrauterine growth restriction (IUGR) is a condition in which the fetal weight is below the 10th percentile for its gestational age. Prenatal exposure to metals can cause a decrease in fetal growth during gestation thereby reducing birth weight. The...
Humans Bayes Theorem Birth Weight Gestational Age Fetal Growth Retardation Machine Learning Pregnancy Female Infant, Newborn
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Predicting risks of low birth weight in Bangladesh with machine learning.

PloS one May 26, 2022
BACKGROUND AND OBJECTIVE: Low birth weight is one of the primary causes of child mortality and several diseases of future life in developing countries, especially in Southern Asia. The main objective of this study is to determine the risk factors of ...
Birth Weight Bangladesh Infant, Low Birth Weight Risk Factors Infant Child Logistic Models Humans Machine Learning Infant, Newborn
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Bayesian nonparametric quantile process regression and estimation of marginal quantile effects.

Biometrics Nov 10, 2021
Flexible estimation of multiple conditional quantiles is of interest in numerous applications, such as studying the effect of pregnancy-related factors on low and high birth weight. We propose a Bayesian nonparametric method to simultaneously estimat...
Pregnancy Female Regression Analysis Bayes Theorem Birth Weight Humans Neural Networks, Computer Computer Simulation
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Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach.

European journal of pediatrics Nov 3, 2021
There is increasing evidence that patient heterogeneity significantly hinders advancement in clinical trials and individualized care. This study aimed to identify distinct phenotypes in extremely low birth weight infants. We performed an agglomerativ...
Humans Phenotype Infant, Newborn Infant, Extremely Premature Infant, Extremely Low Birth Weight Unsupervised Machine Learning Birth Weight Gestational Age
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Quantifying the Impacts of Pre- and Post-Conception TSH Levels on Birth Outcomes: An Examination of Different Machine Learning Models.

Frontiers in endocrinology Oct 29, 2021
BACKGROUND: While previous studies identified risk factors for diverse pregnancy outcomes, traditional statistical methods had limited ability to quantify their impacts on birth outcomes precisely. We aimed to use a novel approach that applied differ...
Machine Learning Labor, Induced Adult Pregnancy Apgar Score China Female Humans Thyrotropin Premature Birth Birth Weight
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Hybridized neural networks for non-invasive and continuous mortality risk assessment in neonates.

Computers in biology and medicine Jun 3, 2021
Premature birth is the primary risk factor in neonatal deaths, with the majority of extremely premature babies cared for in neonatal intensive care units (NICUs). Mortality risk prediction in this setting can greatly improve patient outcomes and reso...
Female Infant, Newborn Risk Factors Risk Assessment Humans Neural Networks, Computer Pregnancy Infant Birth Weight Gestational Age Intensive Care Units, Neonatal
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Prediction of newborn's body mass index using nationwide multicenter ultrasound data: a machine-learning study.

BMC pregnancy and childbirth Mar 2, 2021
BACKGROUND: This study introduced machine learning approaches to predict newborn's body mass index (BMI) based on ultrasound measures and maternal/delivery information.
Predictive Value of Tests Birth Weight Gestational Age Pregnancy Trimester, Third Delivery, Obstetric Infant, Newborn Body Size Republic of Korea Humans Adult Pregnancy Prenatal Diagnosis Neural Networks, Computer Ultrasonography, Prenatal Female Fetal Weight Machine Learning Body Mass Index Prognosis Retrospective Studies
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Deep Learning for the Diagnosis of Stage in Retinopathy of Prematurity: Accuracy and Generalizability across Populations and Cameras.

Ophthalmology. Retina Feb 6, 2021
PURPOSE: Stage is an important feature to identify in retinal images of infants at risk of retinopathy of prematurity (ROP). The purpose of this study was to implement a convolutional neural network (CNN) for binary detection of stages 1, 2, and 3 in...
Humans Female Infant, Newborn Reproducibility of Results Sensitivity and Specificity Male Diagnosis, Computer-Assisted Deep Learning Photography North America Datasets as Topic Nepal ROC Curve Reference Standards Image Processing, Computer-Assisted Gestational Age Area Under Curve Retinopathy of Prematurity Birth Weight
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Fetal birthweight prediction with measured data by a temporal machine learning method.

BMC medical informatics and decision making Jan 25, 2021
BACKGROUND: Birthweight is an important indicator during the fetal development process to protect the maternal and infant safety. However, birthweight is difficult to be directly measured, and is usually roughly estimated by the empirical formulas ac...
Infant, Newborn Logistic Models Birth Weight Infant, Small for Gestational Age Machine Learning Pregnancy Female Humans Neural Networks, Computer
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Automated detection of early-stage ROP using a deep convolutional neural network.

The British journal of ophthalmology Aug 23, 2020
BACKGROUND/AIM: To automatically detect and classify the early stages of retinopathy of prematurity (ROP) using a deep convolutional neural network (CNN).
Cross-Sectional Studies Gestational Age Male Retinopathy of Prematurity Image Processing, Computer-Assisted Humans Retrospective Studies Diagnosis, Computer-Assisted Birth Weight Neural Networks, Computer Female ROC Curve Infant, Premature Algorithms Infant, Newborn Infant, Low Birth Weight Sensitivity and Specificity
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