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State of the Art Cranial Ultrasound Imaging in Neonates

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Automatic Measurement of Frontomaxillary Facial Angle in Fetal Ultrasound Images Using Deep Learning.

Zhonghua Liu¹, Jin Wang², Guorong Lyu³

¹Department of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 350122, China.

Sensors (Basel, Switzerland)

|February 13, 2025

Summary

This study introduces an AI framework for automatically measuring frontomaxillary facial (FMF) angles in prenatal ultrasounds, improving trisomy 21 screening. The deep learning model offers accurate and reliable FMF angle measurements, surpassing junior expert performance.

Keywords:

automatic measurement deep learning semantic segmentation ultrasound image

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Area of Science:

Medical Imaging
Artificial Intelligence
Prenatal Diagnostics

Background:

Accurate frontomaxillary facial (FMF) angle measurement in prenatal ultrasound (US) is crucial for trisomy 21 screening.
Manual FMF angle measurement is subjective, time-consuming, and dependent on ultrasonographer expertise.

Purpose of the Study:

To develop and validate a deep learning-based framework for automated FMF angle measurement in 2D fetal ultrasound images.
To enhance the accuracy and efficiency of trisomy 21 screening through AI-assisted analysis.

Main Methods:

A deep learning network was trained on 1549 fetal ultrasound images for automatic segmentation of critical facial areas.
A key point detection network identified coordinates for FMF angle calculation.
Computational methods derived FMF angles, with results assessed using Pearson correlation and Bland-Altman plots.

Main Results:

The deep learning framework achieved accurate segmentation and key point detection for FMF angle calculation.
The model demonstrated a mean absolute error of 2.354° in FMF angle measurements.
Performance metrics indicated high accuracy and reliability, outperforming junior expert standards.

Conclusions:

The proposed deep learning framework provides an accurate and reliable method for automated FMF angle measurement in prenatal ultrasound.
This AI-assisted approach has the potential to improve the efficiency and objectivity of trisomy 21 screening.
The study highlights the utility of deep learning in medical imaging for enhancing diagnostic procedures.