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Background  Neonatal hyperbilirubinemia is a common clinical condition that requires medical attention in newborns, which may develop into acute bilirubin encephalopathy with a significant risk of long-term neurological deficits. The current clinical challenge lies in the separation of acute bilirubin encephalopathy and non-acute bilirubin encephalopathy neonates both with hyperbilirubinemia condition since both of them demonstrated similar T1 hyperintensity and lead to difficulties in clinical diagnosis based on the conventional radiological reading. This study aims to investigate the utility of T1-weighted MRI images for differentiating acute bilirubin encephalopathy and non-acute bilirubin encephalopathy neonates with hyperbilirubinemia.    Methods  3 diagnostic approaches, including a visual inspection, a semi-quantitative method based on normalized the T1-weighted intensities of the globus pallidus and subthalamic nuclei, and a deep learning method with ResNet18 framework were applied to classify 47 acute bilirubin encephalopathy neonates and 32 non-acute bilirubin encephalopathy neonates with hyperbilirubinemia based on T1-weighted images. Chi-squared test and t-test were used to test the significant difference of clinical features between the 2 groups.    Results  The visual inspection got a poor diagnostic accuracy of 53.58 ± 5.71% indicating the difficulty of the challenge in real clinical practice. However, the semi-quantitative approach and ResNet18 achieved a classification accuracy of 62.11 ± 8.03% and 72.15%, respectively, which outperformed visual inspection significantly.    Conclusion  Our study indicates that it is not sufficient to only use T1-weighted MRI images to detect neonates with acute bilirubin encephalopathy. Other more MRI multimodal images combined with T1-weighted MRI images are expected to use to improve the accuracy in future work. However, this study demonstrates that the semi-quantitative measurement based on T1-weighted MRI images is a simple and compromised way to discriminate acute bilirubin encephalopathy and non-acute bilirubin encephalopathy neonates with hyperbilirubinemia, which may be helpful in improving the current manual diagnosis.

Detecting neonatal acute bilirubin encephalopathy based on T1-weighted MRI images and learning-based approaches