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Neurosonographic Classification in Premature Infants Receiving Omega-3 Supplementation Using Convolutional Neural

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This study introduces a CNN model to accurately classify hypoxic-ischemic encephalopathy (HIE) severity using ultrasound image density. The model achieves high accuracy, enabling faster diagnosis and improved care for newborns with HIE.

Keywords:
brain parenchymachoroid plexusconvolutional neural networkdensity differencehypoxic-ischemic encephalopathyimage classificationintensive caremedical imagingneonatesultrasonography

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Neonatal Neurology

Background:

  • Hypoxic-ischemic encephalopathy (HIE) is a major cause of neonatal mortality and morbidity.
  • Accurate and timely diagnosis of HIE is critical for effective treatment and improved patient outcomes.
  • Current diagnostic methods may lack the speed and precision required for immediate clinical intervention.

Purpose of the Study:

  • To develop and validate a Convolutional Neural Network (CNN) model for precise determination of ultrasound image density.
  • To classify HIE severity into Normal, Moderate, and Intensive categories using the developed CNN model.
  • To enable rapid, timely, and accurate identification of HIE in newborns.

Main Methods:

  • Ultrasound images of the choroid plexus and brain parenchyma were analyzed.
  • Image density was quantified using the Delta E CIE76 value by comparing specific regions of interest.
  • A CNN model was developed, taking combined image density data as input for classification.

Main Results:

  • The CNN model demonstrated high efficiency in classifying HIE severity.
  • Overall accuracy of the classification model was 88.56%.
  • Precision varied by class: 90% for Normal, 85% for Moderate, and 88% for Intensive, with an overall F-measure of 88.40%.

Conclusions:

  • The developed CNN model provides a rapid and accurate method for HIE identification through ultrasound image density analysis.
  • This approach facilitates timely therapeutic interventions, potentially improving long-term outcomes for affected newborns.
  • The study highlights the significance of AI in enhancing neonatal care and managing HIE more efficiently.