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Related Concept Videos

Ultrasonography01:17

Ultrasonography

Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called a...

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AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Mahmood Alzubaidi1, Ines Abbes1, Raden Muaz1

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

Studies in Health Technology and Informatics
|July 3, 2026
PubMed
Summary
This summary is machine-generated.

The AMAL-For-Qatar project developed an AI ecosystem for fetal ultrasound analysis, creating large datasets and advanced models for improved prenatal diagnostics and maternal-fetal healthcare.

Keywords:
Fetal ultrasoundartificial intelligencedeep learningmedical imagingprenatal diagnostics

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Precision Medicine

Background:

  • Fetal ultrasound analysis is crucial for prenatal diagnostics.
  • Existing AI tools for fetal ultrasound are limited.
  • The AMAL-For-Qatar project aims to advance AI in this field.

Purpose of the Study:

  • To develop an end-to-end artificial intelligence ecosystem for fetal ultrasound analysis.
  • To present the achievements of the AMAL-For-Qatar project.
  • To establish a foundation for AI-assisted prenatal diagnostics.

Main Methods:

  • Creation of the largest publicly available annotated dataset for fetal head biometry (3,832 images).
  • Development of the FetSAM segmentation model achieving state-of-the-art performance (DSC 0.901).
  • Implementation of super-resolution techniques and the FADA automated reporting system.
  • Evaluation of vision-language models and systematic review of fetal ultrasound databases.

Main Results:

  • Largest annotated fetal head biometry dataset.
  • State-of-the-art FetSAM segmentation model (DSC 0.901).
  • FADA automated reporting system and super-resolution techniques.
  • Comprehensive evaluations of AI technologies for ultrasound interpretation.

Conclusions:

  • The project established a robust foundation for AI-assisted prenatal diagnostics.
  • AI integration in fetal ultrasound can improve maternal-fetal healthcare.
  • Precision medicine approaches show significant potential in this domain.