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Deep Learning Network with Spatial Attention Module for Detecting Acute Bilirubin Encephalopathy in Newborns Based on

Huan Zhang1, Yi Zhuang2, Shunren Xia1

  • 1Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China.

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Summary

This study shows that combining multiple MRI images improves the diagnosis of acute bilirubin encephalopathy (ABE) in newborns. Adding a spatial attention module further enhances the deep learning model's accuracy for ABE detection.

Keywords:
acute bilirubin encephalopathynewbornresidual networkspatial attention module

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Area of Science:

  • Neonatal neurology
  • Medical imaging analysis
  • Artificial intelligence in medicine

Background:

  • Acute bilirubin encephalopathy (ABE) is a major cause of neonatal mortality and disability.
  • Early detection and treatment are crucial to prevent long-term complications.
  • Single-modal MRI has limited classification ability for ABE.

Purpose of the Study:

  • To validate a deep learning model using multimodal MRI for ABE classification.
  • To evaluate the impact of a spatial attention module (SAM) on diagnostic performance.
  • To improve the accuracy of distinguishing ABE from non-ABE conditions.

Main Methods:

  • A multimodal MRI classification model (ResNet18 with SAM) was developed.
  • 97 neonates with ABE and 80 with hyperbilirubinemia (non-ABE) were included.
  • T1WI, T2WI, and ADC maps were used as input, testing all combinations.

Main Results:

  • Multimodal MRI combinations outperformed single-modal images.
  • The combination of T1WI and T2WI yielded the best performance (accuracy = 0.808).
  • Spatial attention modules significantly improved classification accuracy.

Conclusions:

  • Multimodal MRI classification networks with SAMs enhance ABE diagnostic accuracy.
  • This approach offers a promising tool for early ABE detection and management.