AIMC Topic: Fetal Macrosomia

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Machine learning approaches for predicting fetal macrosomia at different stages of pregnancy: a retrospective study in China.

BMC pregnancy and childbirth Feb 11, 2025
BACKGROUND: Macrosomia presents significant risks to both maternal and neonatal health, however, accurate antenatal prediction remains a major challenge. This study aimed to develop machine learning approaches to enhance the prediction of fetal macro...
Pregnancy Female Infant, Newborn Humans Machine Learning Retrospective Studies Fetal Macrosomia Gestational Weight Gain China ROC Curve Gestational Age Adult Male
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Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy.

BMC medical informatics and decision making Jun 20, 2024
INTRODUCTION: The correlation between radiation exposure before pregnancy and abnormal birth weight has been previously proven. However, for large-for-gestational-age (LGA) babies in women exposed to radiation before becoming pregnant, there is no pr...
Birth Weight Radiation Exposure Fetal Macrosomia Adult China Humans Machine Learning Pregnancy Female Infant, Newborn
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An innovative supervised longitudinal learning procedure of recurrent neural networks with temporal data augmentation: Insights from predicting fetal macrosomia and large-for-gestational age.

Computers in biology and medicine May 27, 2024
BACKGROUND: Longitudinal data in health informatics studies often present challenges due to sparse observations from each subject, limiting the application of contemporary deep learning for prediction. This issue is particularly relevant in predictin...
Fetal Macrosomia Birth Weight Supervised Machine Learning Gestational Age Adult Ultrasonography, Prenatal Humans Neural Networks, Computer Pregnancy Female Infant, Newborn
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Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study.

BMC pregnancy and childbirth Aug 15, 2018
BACKGROUND: While there is increasing interest in identifying pregnancies at risk for adverse outcome, existing prediction models have not adequately assessed population-based risks, and have been based on conventional regression methods. The objecti...
Pregnancy Trimester, Second Statistics as Topic Fetal Development Female Body Mass Index Nova Scotia Machine Learning Infant, Small for Gestational Age Infant, Newborn Smoking Humans Adult Retrospective Studies Young Adult Neural Networks, Computer Pregnancy Fetal Macrosomia Cohort Studies Gestational Weight Gain Logistic Models
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Machine Learning-Based Prediction of Large-for-Gestational-Age Infants in Mothers With Gestational Diabetes Mellitus.

The Journal of clinical endocrinology and metabolism Apr 22, 2025
CONTEXT: Large-for-gestational-age (LGA), one of the most common complications of gestational diabetes mellitus (GDM), has become a global concern. The predictive performance of common continuous glucose monitoring (CGM) metrics for LGA is limited.
Humans Machine Learning Adult Prospective Studies Blood Glucose Gestational Age Pregnancy Predictive Value of Tests Birth Weight Fetal Macrosomia Female Blood Glucose Self-Monitoring Infant, Large for Gestational Age Infant, Newborn Diabetes, Gestational
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Development and validation of a machine-learning model for prediction of shoulder dystocia.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology Oct 1, 2020
OBJECTIVES: To develop a machine-learning (ML) model for prediction of shoulder dystocia (ShD) and to externally validate the model's predictive accuracy and potential clinical efficacy in optimizing the use of Cesarean delivery in the context of sus...
Adult Ultrasonography, Prenatal ROC Curve Humans Predictive Value of Tests Machine Learning Patient Selection Israel Shoulder Dystocia Biometry Cesarean Section Diabetes, Gestational Fetal Weight Pregnancy Reproducibility of Results Gestational Age Fetal Macrosomia Female Risk Factors
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