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Related Concept Videos

Ultrasound I: Abdominal Ultrasonography01:20

Ultrasound I: Abdominal Ultrasonography

444
Introduction:
Abdominal ultrasonography, commonly known as abdominal ultrasound, is a vital, non-invasive medical imaging technique widely used in healthcare.
Procedure:
This diagnostic tool allows the clinician to visually inspect internal structures within the abdomen, including vital organs such as the liver, gallbladder, pancreas, kidneys, and spleen.
The abdominal ultrasound process begins with applying a special gel to the patient's skin over the abdomen. This gel enhances the...
444

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Related Experiment Video

Updated: Oct 5, 2025

A Modified Sonographic Algorithm for Image Acquisition in Life-Threatening Emergencies in the Critically Ill Newborn
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Automatic Placenta Localization From Ultrasound Imaging in a Resource-Limited Setting Using a Predefined Ultrasound

Martijn Schilpzand1, Chase Neff2, Jeroen van Dillen3

  • 1Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands; Medical Ultrasound Imaging Centre, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands; Institute for Computing and Information Sciences, Radboud University, Nijmegen, The Netherlands.

Ultrasound in Medicine & Biology
|January 22, 2022
PubMed
Summary

Automated placenta detection using deep learning on 2D ultrasound images can improve prenatal care in low-income countries. This method accurately identifies placenta previa and low-lying placenta, even with low-cost equipment.

Keywords:
Computer-aided diagnosisMachine learningNeural networkObstetricsPlacenta previaPrenatalResource-limited countriesSegmentationUltrasound

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Obstetrics and Gynecology

Background:

  • Placenta localization via ultrasound is crucial for detecting placenta previa but is limited in low-income countries due to a shortage of trained sonographers.
  • This accessibility gap hinders timely diagnosis and management of potential pregnancy complications.

Purpose of the Study:

  • To develop and validate an automated method for detecting low-lying placenta and placenta previa from 2D ultrasound images.
  • To assess the feasibility of this automated system in resource-limited settings using low-cost equipment and smartphone integration.

Main Methods:

  • A deep learning model with a U-Net architecture was employed for 2D ultrasound image segmentation of the placenta.
  • A subsequent classification step distinguished between normal placentas and those identified as low-lying or placenta previa.
  • The model was trained and validated on 2D ultrasound data from 280 pregnant women in Ethiopia.

Main Results:

  • The placenta segmentation model achieved a median Dice coefficient of 0.84.
  • The placenta classification model demonstrated 81% sensitivity and 82% specificity.
  • The automated system performed real-time segmentation (19 ± 2 ms per image) on a smartphone-connected low-cost ultrasound device.

Conclusions:

  • Automated placenta localization using deep learning is feasible and accurate, even in resource-limited settings.
  • This technology holds significant potential to improve prenatal care accessibility and outcomes for pregnant women in underserved regions.
  • The developed method offers a scalable solution to overcome the shortage of trained sonographers for essential obstetric imaging.