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Automated placental abruption identification using semantic segmentation, quantitative features, SVM, ensemble and

Vahid Asadpour1, Eric J Puttock1, Darios Getahun1,2

  • 1Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.

Heliyon
|February 28, 2023
PubMed
Summary

This study introduces an automated machine learning method for detecting placental abruption in ultrasound images. Optimized ResNet-50 achieved 82.88% accuracy, offering a potential tool for improved fetal health monitoring.

Keywords:
Machine learningPlacental abruptionPlacental segmentationRadiology featureUltrasonography

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Area of Science:

  • Medical Imaging
  • Machine Learning in Healthcare
  • Obstetrics and Gynecology

Background:

  • The placenta is vital for fetal health, and its abnormalities, like placental abruption, require prompt diagnosis.
  • Ultrasound imaging is crucial for investigating suspicious placental conditions during pregnancy.

Purpose of the Study:

  • To develop and evaluate an automated machine learning method for identifying placental abruption using fetal ultrasound images.
  • To compare the performance of various machine learning classifiers, including Support Vector Machines, decision trees, and deep learning models, for placental abruption detection.

Main Methods:

  • Automated identification of placental regions in ultrasound images.
  • Quantitative feature extraction from placental regions.
  • Classification using Support Vector Machine (SVM), decision tree ensembles, ResNet-50, and Inception-V3, optimized with Recursive Feature Elimination (RFE).

Main Results:

  • The optimized ResNet-50 model achieved the highest accuracy of 82.88% ± 1.42% in identifying placental abruption.
  • Comparison of different machine learning algorithms revealed ResNet-50 as the most effective for this task.
  • Recursive Feature Elimination (RFE) was used to optimize feature vectors for improved classifier performance.

Conclusions:

  • An automated method for placental abruption detection using ultrasound images is feasible.
  • Machine learning, particularly deep learning models like ResNet-50, shows promise for improving the accuracy and efficiency of placental abruption diagnosis.
  • The developed method offers a potential tool for enhanced fetal health monitoring and management.