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A Conceptual Framework for Fetus Head Analysis Based on Ultrasound Images.

Mahmood Alzubaidi1, Marco Agus1, Uzair Shah1

  • 1College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

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Summary

This study presents an automated framework for analyzing fetal ultrasound images, improving the efficiency of estimating fetal growth parameters like gestational age and weight.

Keywords:
Estimated fetus weightGestational ageImage segmentationUltrasoundfetus abnormalityfetus head

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Area of Science:

  • Medical Imaging
  • Obstetrics
  • Artificial Intelligence in Healthcare

Background:

  • Ultrasound is crucial in obstetrics for fetal monitoring.
  • Expert analysis of fetal head ultrasound is time-consuming and costly.
  • Accurate assessment of fetal growth and anatomy is essential.

Purpose of the Study:

  • To develop an automated framework for fetal head ultrasound analysis.
  • To reduce the time and cost associated with manual obstetric assessments.
  • To accurately predict fetal Gestational Age (GA), Estimated Fetus Weight (EFW), and detect abnormalities.

Main Methods:

  • A hybrid approach combining automatic segmentation, measurement extraction, and feature detection.
  • Segmentation of the Region of Interest (ROI) in fetal head ultrasound images.
  • Utilizing AI for predicting GA, EFW, and abnormality status.

Main Results:

  • The proposed framework automates key aspects of fetal head ultrasound analysis.
  • Enables accurate prediction of fetal Gestational Age (GA) and Estimated Fetus Weight (EFW).
  • Facilitates the detection of fetal abnormalities.

Conclusions:

  • The automated framework offers an efficient and accurate alternative to manual obstetric image analysis.
  • Potential to improve prenatal care by streamlining the assessment of fetal development.
  • Highlights the role of AI in enhancing obstetric diagnostics.