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Related Experiment Video

Updated: Jul 9, 2026

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

Transformer-based deep learning for estimating bidirectional maternal-fetal cardiac coupling.

Murad Almadani¹, Leontios Hadjileontiadis^1,2, Ahsan Khandoker¹

¹Healthcare Engineering Innovation Group, Department of Biomedical Engineering and Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates.

Frontiers in Medical Technology

|July 8, 2026

Summary

This summary is machine-generated.

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We developed a deep learning model for bidirectional maternal-fetal cardiac coupling analysis. This approach shows promise for non-invasive prenatal monitoring by identifying fetal autonomic regulation patterns.

Area of Science:

Biomedical Engineering
Cardiology
Fetal Physiology

Background:

Maternal-fetal cardiac coupling is a key indicator of fetal development.
Current methods for assessing this coupling are limited by unidirectional analysis and lack clinical validation.

Purpose of the Study:

To develop a novel deep learning framework for bidirectional maternal-fetal cardiac coupling analysis.
To evaluate the framework's ability to assess fetal autonomic regulation and neurocardiac development.

Main Methods:

A transformer-based deep learning framework utilizing UNETR models for fetal-to-maternal (FTM) and maternal-to-fetal (MTF) interaction analysis.
Analysis incorporated time-delay, gestational, and classification metrics on a clinical dataset.

Keywords:

bidirectional coupling deep learning fetal ECG fetal-maternal coupling support vector machine transformer

Related Experiment Videos

Last Updated: Jul 9, 2026

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

Main Results:

Directional asymmetry in coupling was observed (MTF at 3s, FTM at 5s).
Reduced FTM coupling correlated with abnormal cases.
A classification model achieved high discrimination (ROC-AUC = 0.91 ± 0.08), with coupling-only features showing robust minority-class sensitivity.

Conclusions:

Bidirectional coupling analysis offers valuable insights into maternal-fetal physiological interactions.
The approach shows potential for non-invasive prenatal monitoring.
Further validation on larger datasets is needed to confirm clinical utility.