ArticleBMC medical imaging2021
Detecting neonatal acute bilirubin encephalopathy based on T1-weighted MRI images and learning-based approaches.
Article in BMC medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 15 citations in OpenAlex.
- Computer-aided diagnosis of neonatal acute bilirubin encephalopathy with multi-modal MRI images and convolutional neural networks.Scientific reports · 2026Article
- Association of T1-weighted hyperintensity with timing of peak bilirubin levels in neonates with hyperbilirubinemia.Neuroradiology · 2025Article
- Detecting acute bilirubin encephalopathy in neonates based on multimodal MRI images and non-image clinical data.BMC pediatrics · 2025Article
- Detection of Sub-acute Brain Injury and Hypoxic-ischemic Encephalopathy using I2C2-WGO and CO-GW-RNN.Current medical imaging · 2025Article
- Visualization of Unconjugated Bilirubin In Vivo with a Novel Approach Using Chemical Exchange Saturation Transfer Magnetic Resonance Imaging in a Rat Model.ACS chemical neuroscience · 2024Article
- Acute and Chronic Kernicterus: MR Imaging Evolution of Globus Pallidus Signal Change during Childhood.AJNR. American journal of neuroradiology · 2023Article
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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