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Fetal Circulation

Fetal Circulation

Fetal circulation is a unique system that facilitates the exchange of gases, nutrients, and waste products between the developing fetus and the mother. This intricate process takes place through a special organ called the placenta.
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Updated: Jul 27, 2025

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

A Fetal Well-Being Diagnostic Method Based on Cardiotocographic Morphological Pattern Utilizing Autoencoder and

Haad Akmal¹, Fırat Hardalaç¹, Kubilay Ayturan¹

¹Department of Electrical and Electronics Engineering, Gazi University, Ankara 06570, Turkey.

Diagnostics (Basel, Switzerland)

|June 10, 2023

Summary

A new machine learning model accurately diagnoses fetal conditions using cardiotocography (CTG) data. This tool aids in managing high-risk pregnancies by classifying fetal status and CTG patterns with high precision.

Keywords:

Bayesian optimization cardiotocography classification diagnostics feature extraction feature selection fetal heart rate fetal well-being machine learning

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Last Updated: Jul 27, 2025

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

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Published on: January 7, 2021

Murine Fetal Echocardiography

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Published on: February 15, 2013

Fetal Mouse Cardiovascular Imaging Using a High-frequency Ultrasound 30/45MHZ System

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Published on: May 5, 2018

Area of Science:

Perinatal medicine
Artificial intelligence in healthcare
Signal processing for biomedical applications

Background:

Cardiotocography (CTG) is crucial for monitoring fetal well-being by measuring fetal heart rate (FHR) and uterine contractions (UC).
Diagnosing fetal distress from CTG requires analyzing complex patterns and addressing data imbalances.
Existing methods may benefit from advanced computational approaches for improved accuracy and decision support.

Purpose of the Study:

To develop and evaluate a machine learning model for diagnosing fetal conditions and classifying CTG patterns.
To address the challenge of imbalanced datasets common in CTG analysis.
To create a potential decision support tool for managing high-risk pregnancies.

Main Methods:

Utilized an autoencoder for feature extraction and recursive feature elimination for feature selection.
Employed Bayesian optimization for model tuning.
Integrated Random Forest classifier for fetal status and CTG pattern classification.
Addressed dataset imbalance issues inherent in CTG data.

Main Results:

Achieved 96.62% accuracy in fetal status classification and 94.96% in CTG morphological pattern classification.
Demonstrated high predictive accuracy for specific conditions: 98% for Suspect and 98.6% for Pathologic cases.
The model effectively handled imbalanced CTG data, showing robust performance.

Conclusions:

The proposed machine learning model shows significant potential as a decision support tool in perinatal care.
Accurate classification of fetal status and CTG patterns can enhance the management of high-risk pregnancies.
This approach offers a promising advancement in leveraging AI for improved obstetric monitoring.