AIMC Topic: Infant, Small for Gestational Age

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Enhancing Small-for-Gestational-Age Prediction: Multi-Country Validation of Nuchal Thickness, Estimated Fetal Weight, and Machine Learning Models.

Prenatal diagnosis Jan 16, 2025
OBJECTIVE: The first objective is to develop a nuchal thickness reference chart. The second objective is to compare rule-based algorithms and machine learning models in predicting small-for-gestational-age infants.
Female Infant, Newborn Adult Ultrasonography, Prenatal Fetal Weight Malaysia Infant, Small for Gestational Age Predictive Value of Tests Singapore Humans Machine Learning Retrospective Studies Pregnancy
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Constructing small for gestational age prediction models: A retrospective machine learning study.

European journal of obstetrics, gynecology, and reproductive biology Nov 26, 2024
OBJECTIVE: To develop machine learning prediction models for small for gestational age with baseline characteristics and biochemical tests of various pregnancy stages individually and collectively and compare predictive performance.
Pregnancy Female Infant, Newborn Adult Humans Machine Learning Retrospective Studies Birth Weight Gestational Age Infant, Small for Gestational Age
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Deep learning-based segmentation of whole-body fetal MRI and fetal weight estimation: assessing performance, repeatability, and reproducibility.

European radiology Sep 2, 2023
OBJECTIVES: To develop a deep-learning method for whole-body fetal segmentation based on MRI; to assess the method's repeatability, reproducibility, and accuracy; to create an MRI-based normal fetal weight growth chart; and to assess the sensitivity ...
Fetus Gestational Age Fetal Growth Retardation Fetal Weight Deep Learning Magnetic Resonance Imaging Birth Weight Infant, Small for Gestational Age Retrospective Studies Pregnancy Female Infant, Newborn Reproducibility of Results Ultrasonography, Prenatal Infant Humans
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Machine learning improves early prediction of small-for-gestational-age births and reveals nuchal fold thickness as unexpected predictor.

Prenatal diagnosis Feb 12, 2021
OBJECTIVE: To investigate the performance of the machine learning (ML) model in predicting small-for-gestational-age (SGA) at birth, using second-trimester data.
Gestational Age Nuchal Translucency Measurement Infant, Small for Gestational Age Logistic Models Humans Predictive Value of Tests Singapore Machine Learning Retrospective Studies Female Infant, Newborn Male
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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...
Birth Weight Pregnancy Infant, Small for Gestational Age Humans Female Neural Networks, Computer Infant, Newborn Machine Learning Logistic Models
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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 Infant, Small for Gestational Age Machine Learning Cohort Studies Fetal Macrosomia Logistic Models Retrospective Studies Infant, Newborn Humans Gestational Weight Gain Body Mass Index Nova Scotia Pregnancy Neural Networks, Computer Young Adult Smoking Female Adult
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Estimating risk of severe neonatal morbidity in preterm births under 32 weeks of gestation.

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians Jul 18, 2018
A large recent study analyzed the relationship between multiple factors and neonatal outcome and in preterm births. Study variables included the reason for admission, indication for delivery, optimal steroid use, gestational age, and other potential...
Infant Mortality Gestational Age Stillbirth Infant, Premature, Diseases Predictive Value of Tests Premature Birth Diagnostic Techniques, Obstetrical and Gynecological Morbidity Risk Factors Humans Male Infant, Small for Gestational Age Prognosis Adult Machine Learning Probability Risk Assessment Prospective Studies Pregnancy Infant Female Severity of Illness Index Infant, Newborn
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Evaluating Prevalence of Preterm Postnatal Growth Faltering Using Fenton 2013 and INTERGROWTH-21st Growth Charts with Logistic and Machine Learning Models.

Nutrients May 20, 2025
Postnatal growth faltering (PGF) significantly affects premature neonates, leading to compromised neurodevelopment and an increased risk of long-term health complications. This retrospective study at a level III NICU of a tertiary hospital analyzed...
Machine Learning Retrospective Studies Female Infant, Newborn Humans Infant, Premature Male Gestational Age Prevalence Infant, Small for Gestational Age Growth Disorders Growth Charts Logistic Models
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