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Related Experiment Video

Updated: Jan 12, 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

IoT assisted fetal health classification using mother optimization algorithm with deep learning approach on

K Nandini¹, K Rahimunnisa²

¹Department of Robotics and Automation, Easwari Engineering College, Chennai, 600089, India. nandiniresearchscholar@gmail.com.

Scientific Reports

|November 6, 2025

Summary

This summary is machine-generated.

This study introduces an IoT-assisted method using deep learning and a novel optimization algorithm for accurate fetal health classification. The AFHDC MOADL technique enhances prenatal care by classifying fetal well-being into normal, suspect, or pathological states.

Keywords:

Cardiotocogram Deep learning Fetal health detection Internet of things Mother optimization algorithm Signal processing Ultrasound images

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Last Updated: Jan 12, 2026

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

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Published on: January 7, 2021

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Published on: May 5, 2018

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Published on: June 12, 2020

Area of Science:

Medical Technology
Artificial Intelligence
Maternal Health

Background:

Fetal movement is a key indicator of fetal well-being, but current monitoring methods lack accessibility and long-term effectiveness.
The Internet of Things (IoT) offers potential for smart health applications, enabling real-time, remote monitoring of critical health data.
Machine learning (ML) and deep learning (DL) are increasingly utilized for automated health classification, including fetal health assessment.

Purpose of the Study:

To develop and evaluate an IoT-assisted Fetal Health Detection and Classification system using the Mother Optimization Algorithm with Deep Learning (AFHDC MOADL).
To accurately classify fetal health into three categories: normal, suspect, and pathological, improving prenatal care outcomes.
To address limitations in current fetal monitoring technologies by providing an accessible and effective long-term solution.

Main Methods:

Data acquisition using IoT devices to collect fetal health-related information.
Data preprocessing including K-nearest neighbor (KNN) imputation and standard scaling.
Feature selection using the Mother Optimization Algorithm (MOA) to reduce dimensionality, followed by classification using a Graph Convolutional Neural Network (GCN) optimized with RMSProp.

Main Results:

The AFHDC MOADL technique demonstrated significant performance in classifying fetal health.
Experimental validation on the Fetal Health Classification dataset showed superior results compared to existing deep learning approaches.
The method effectively classifies fetal health into normal, suspect, and pathological states.

Conclusions:

The proposed AFHDC MOADL method offers a promising IoT-based solution for real-time fetal health monitoring and classification.
This approach enhances personalized pregnancy monitoring and timely diagnosis of potential fetal distress.
The integration of IoT, MOA, and GCN advances automated diagnostic systems in smart health applications.