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

BMC Medical Imaging

Xiaoxia Shen¹  Can Lai²  Yingqun Li²  Zhongli Shangguan²  Weihao Zheng³  Dan Wu³  Tingting Liu³  Miao Wu⁴  Chuanbo Yan⁵ 

[1] Department of Neonatal Intensive Care Unit, Children’s Hospital, Zhejiang University School of Medicine, 310051, Hangzhou, China;Department of Radiology, Children’s Hospital, Zhejiang University School of Medicine, 310052, Hangzhou, China;Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, 310027, Hangzhou, China;Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, 310027, Hangzhou, China;State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, College of Medical Engineering and Technology, Xinjiang Medical University, 830011, Urumqi, China;State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, College of Medical Engineering and Technology, Xinjiang Medical University, 830011, Urumqi, China;

关键词: Acute bilirubin encephalopathy;    Hyperbilirubinemia;    Normalized T1-weighted intensities;    Deep convolutional neural networks;    ResNet18;    Classification;    Diagnosis;   

DOI  :  10.1186/s12880-021-00634-z

来源: Springer

【 摘 要 】

BackgroundNeonatal 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.Methods3 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.ResultsThe 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.ConclusionOur 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.

【 授权许可】

CC BY