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Updated: Jul 19, 2025

Fetal Echocardiography and Pulsed-wave Doppler Ultrasound in a Rabbit Model of Intrauterine Growth Restriction

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Published on: June 29, 2013

Machine Learning-Based Approach to Predict Intrauterine Growth Restriction.

Elham Taeidi¹, Amene Ranjbar², Farideh Montazeri¹

¹Mother and Child Welfare Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, IRN.

|August 7, 2023

Summary

Machine learning accurately predicts intrauterine growth restriction (IUGR). Deep learning models show the highest performance in identifying IUGR risk factors, aiding early intervention.

Keywords:

artificial intelligence decision tree classification deep learning fetal growth restriction gradient boost algorithm intrauterine growth restriction intrauterine growth restriction (iugr)machine learning prenatal maternal screening random forest

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Last Updated: Jul 19, 2025

Fetal Echocardiography and Pulsed-wave Doppler Ultrasound in a Rabbit Model of Intrauterine Growth Restriction

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Published on: June 29, 2013

A Rat Model of Mild Intrauterine Hypoperfusion with Microcoil Stenosis

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Published on: January 7, 2018

High Frequency Ultrasound for the Analysis of Fetal and Placental Development In Vivo

High Frequency Ultrasound for the Analysis of Fetal and Placental Development In Vivo

Published on: November 8, 2018

Area of Science:

Maternal-fetal medicine
Artificial intelligence in healthcare
Predictive modeling in obstetrics

Background:

Intrauterine growth restriction (IUGR) poses significant risks to newborns.
Developing accurate prediction models for IUGR is crucial for timely intervention.
Machine learning offers a promising approach to integrate multiple risk factors for IUGR prediction.

Purpose of the Study:

To develop and evaluate machine learning models for predicting intrauterine growth restriction (IUGR).
To compare the performance of different machine learning algorithms in IUGR prediction.
To identify key risk factors contributing to IUGR based on model analysis.

Main Methods:

A cross-sectional study involving 8683 women was conducted.
Four machine learning algorithms were employed: Decision Tree, Random Forest, Deep Learning, and Gradient Boost.
Model performance was assessed using AUROC, accuracy, precision, and sensitivity.

Main Results:

The incidence of intrauterine growth restriction (IUGR) was 8.19%.
Deep Learning demonstrated the highest predictive performance with an AUROC of 0.91.
Key predictors identified included drug addiction, prior IUGR, chronic hypertension, preeclampsia, anemia, and COVID-19.

Conclusions:

Machine learning models are effective for predicting intrauterine growth restriction (IUGR).
The Deep Learning algorithm provides a highly accurate method for IUGR prediction.
Identifying high-risk pregnancies through advanced modeling can improve perinatal outcomes.