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Related Experiment Video

Updated: Dec 31, 2025

State of the Art Cranial Ultrasound Imaging in Neonates
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Using deep-learning algorithms to classify fetal brain ultrasound images as normal or abnormal.

H N Xie1, N Wang2, M He1

  • 1Department of Ultrasonic Medicine, Fetal Medical Center, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.

Ultrasound in Obstetrics & Gynecology : the Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology
|January 8, 2020
PubMed
Summary

Deep learning accurately classifies fetal brain ultrasound images as normal or abnormal. The algorithms achieved high precision in segmentation and lesion localization, paving the way for improved diagnosis of fetal brain abnormalities.

Keywords:
convolutional neural networkdeep learningfetal intracranial structureprenatal ultrasound

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Fetal Medicine

Background:

  • Fetal brain abnormalities require accurate and timely diagnosis.
  • Standard ultrasound interpretation can be challenging and subjective.
  • Advancements in deep learning offer potential for automated image analysis.

Purpose of the Study:

  • To assess the feasibility of deep learning for classifying fetal brain ultrasound images.
  • To evaluate the performance of deep learning algorithms in segmenting and classifying fetal brain images.
  • To explore the utility of deep learning in localizing lesions in abnormal fetal brain scans.

Main Methods:

  • Utilized a large hospital database of 10,251 normal and 2,529 abnormal fetal brain ultrasound images.
  • Trained deep learning algorithms for image segmentation, normal/abnormal classification, and lesion localization.
  • Tested algorithm performance using precision, recall, Dice's coefficient, sensitivity, specificity, and expert evaluation of heat maps.

Main Results:

  • Achieved high segmentation performance: precision (97.9%), recall (90.9%), Dice's coefficient (94.1%).
  • Demonstrated excellent classification accuracy (96.3%) with high sensitivity (96.9%) and specificity (95.9%).
  • Successfully localized lesions in 61.6% of abnormal images precisely, with an area under the ROC curve of 0.989.

Conclusions:

  • Deep learning algorithms are feasible for segmenting and classifying fetal brain ultrasound images.
  • These algorithms can effectively localize lesions, aiding in the diagnosis of fetal intracranial abnormalities.
  • The study provides a foundation for future research in differential diagnosis of fetal brain conditions.