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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.
Two umbilical arteries transport blood from the fetus to the placenta. At the placenta, the blood absorbs oxygen and nutrients while simultaneously eliminating waste products. This oxygen-enriched and nutrient-rich blood then returns to the fetus through one...

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Murine Fetal Echocardiography

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FBStrNet: Automatic Fetal Brain Structure Detection in Early Pregnancy Ultrasound Images.

Yirong Lin¹, Shunlan Liu², Zhonghua Liu³

¹School of Medicine, Huaqiao University, Quanzhou 362021, China.

Sensors (Basel, Switzerland)

|August 28, 2025

Summary

A new AI model, Fetal Brain Structures Detection Network (FBStrNet), accurately identifies fetal brain structures in ultrasound images. This technology enhances diagnostic precision and speeds up screening for fetal anomalies in early pregnancy.

Keywords:

anatomical structure detection deep learning early pregnancy fetal brain ultrasound imaging

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Last Updated: May 13, 2026

Murine Fetal Echocardiography

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Published on: February 15, 2013

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Published on: May 5, 2018

Area of Science:

Medical Imaging
Artificial Intelligence
Obstetrics

Background:

Ultrasound imaging is crucial for detecting fetal brain anomalies during early pregnancy.
Diagnostic accuracy is often limited by sonographer experience and environmental factors.
There is a need for advanced methods to improve the efficiency and reliability of fetal anomaly screening.

Purpose of the Study:

To develop and evaluate a novel AI-based approach for automated detection of key fetal brain anatomical structures.
To enhance the accuracy and efficiency of fetal brain anomaly screening using deep learning.

Main Methods:

Proposed a Fetal Brain Structures Detection Network (FBStrNet) based on the YOLOv5 model.
Incorporated a lightweight backbone, modified loss function, and decoupled detection header.
Integrated prior clinical knowledge to reduce false detection rates.

Main Results:

FBStrNet achieved superior performance compared to existing state-of-the-art methods.
Real-time detection of fetal brain structures was accomplished with an inference time of 11.5 ms.
The method demonstrated improved diagnostic precision and streamlined clinical workflows.

Conclusions:

FBStrNet offers an efficient and reliable tool for fetal brain anomaly screening.
The AI model aids sonographers in visualizing critical anatomical features, enhancing diagnostic accuracy.
This approach has the potential to significantly improve prenatal care and outcomes.