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

This study focuses on developing a model for the precise determination of ultrasound image density and classification using convolutional neural networks (CNNs) for rapid, timely, and accurate identification of hypoxic-ischemic encephalopathy (HIE). Image density is measured by comparing two regions...

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Bibliographic Details
Published in:Diagnostics (Basel) 2024-06, Vol.14 (13), p.1342
Main Authors: Zivojinovic, Suzana, Petrovic Savic, Suzana, Prodanovic, Tijana, Prodanovic, Nikola, Simovic, Aleksandra, Devedzic, Goran, Savic, Dragana
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Brain damage
Brain research
convolutional neural network
Cysts
density difference
Human error
Hypothermia
Hypoxia
hypoxic-ischemic encephalopathy
intensive care
Ischemia
Magnetic resonance imaging
Neonatal care
neonates
Nervous system
Neural networks
Newborn babies
Premature babies
Traumatic brain injury
Ultrasonic imaging
ultrasonography
Visualization
Citations: Items that this one cites
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Summary:This study focuses on developing a model for the precise determination of ultrasound image density and classification using convolutional neural networks (CNNs) for rapid, timely, and accurate identification of hypoxic-ischemic encephalopathy (HIE). Image density is measured by comparing two regions of interest on ultrasound images of the choroid plexus and brain parenchyma using the Delta E CIE76 value. These regions are then combined and serve as input to the CNN model for classification. The classification results of images into three groups (Normal, Moderate, and Intensive) demonstrate high model efficiency, with an overall accuracy of 88.56%, precision of 90% for Normal, 85% for Moderate, and 88% for Intensive. The overall F-measure is 88.40%, indicating a successful combination of accuracy and completeness in classification. This study is significant as it enables rapid and accurate identification of hypoxic-ischemic encephalopathy in newborns, which is crucial for the timely implementation of appropriate therapeutic measures and improving long-term outcomes for these patients. The application of such advanced techniques allows medical personnel to manage treatment more efficiently, reducing the risk of complications and improving the quality of care for newborns with HIE.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics14131342