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

Abstract

Background: Acute bilirubin encephalopathy (ABE) is a significant cause of neonatal mortality and disability. Early detection and treatment of ABE can prevent the further development of ABE and its long-term complications. Due to the limited classification ability of single-modal magnetic resonance imaging (MRI), this study aimed to validate the classification performance of a new deep learning model based on multimodal MRI images. Additionally, the study evaluated the effect of a spatial attention module (SAM) on improving the model’s diagnostic performance in distinguishing ABE. Methods: This study enrolled a total of 97 neonates diagnosed with ABE and 80 neonates diagnosed with hyperbilirubinemia (HB, non-ABE). Each patient underwent three types of multimodal imaging, which included T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and an apparent diffusion coefficient (ADC) map. A multimodal MRI classification model based on the ResNet18 network with spatial attention modules was built to distinguish ABE from non-ABE. All combinations of the three types of images were used as inputs to test the model’s classification performance, and we also analyzed the prediction performance of models with SAMs through comparative experiments. Results: The results indicated that the diagnostic performance of the multimodal image combination was better than any single-modal image, and the combination of T1WI and T2WI achieved the best classification performance (accuracy = 0.808 ± 0.069, area under the curve = 0.808 ± 0.057). The ADC images performed the worst among the three modalities’ images. Adding spatial attention modules significantly improved the model’s classification performance. Conclusion: Our experiment showed that a multimodal image classification network with spatial attention modules significantly improved the accuracy of ABE classification.