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Updated: Jul 5, 2025
Early Pathological and Magnetic Resonance Detection of Cerebral Injury Using a Rat Model of Neonatal Hypoxic Ischemic Encephalopathy
Published on: October 28, 2022
Rule-based deep learning method for prognosis of neonatal hypoxic-ischemic encephalopathy by using susceptibility
Zhen Tang1, Sasan Mahmoodi2, Di Meng3
1School of Computer Science and Technology, AnHui University of Technology, Maxiang Street, Maanshan, 243032, Anhui, China. zhentang@ahut.edu.cn.
Objective:
Susceptibility weighted imaging (SWI) of neonatal hypoxic-ischemic brain injury can provide assistance in the prognosis of neonatal hypoxic-ischemic encephalopathy (HIE). We propose a convolutional neural network model to classify SWI images with HIE.
Materials And Methods:
Due to the lack of a large dataset, transfer learning method with fine-tuning a pre-trained ResNet 50 is introduced. We randomly select 11 datasets from patients with normal neurology outcomes (n = 31) and patients with abnormal neurology outcomes (n = 11) at 24 months of age to avoid bias in classification due to any imbalance in the data.
Results:
We develop a rule-based system to improve the classification performance, with an accuracy of 0.93 ± 0.09. We also compute heatmaps produced by the Grad-CAM technique to analyze which areas of SWI images contributed more to the classification patients with abnormal neurology outcome.
Conclusion:
Such regions that are important in the classification accuracy can interpret the relationship between the brain regions affected by hypoxic-ischemic and neurodevelopmental outcomes of infants with HIE at the age of 2 years.
