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

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 bilirub...

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Autores principales: Wu, Miao, Shen, Xiaoxia, Lai, Can, Zheng, Weihao, Li, Yingqun, Shangguan, Zhongli, Yan, Chuanbo, Liu, Tingting, Wu, Dan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8218479/
https://www.ncbi.nlm.nih.gov/pubmed/34158001
http://dx.doi.org/10.1186/s12880-021-00634-z
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author Wu, Miao
Shen, Xiaoxia
Lai, Can
Zheng, Weihao
Li, Yingqun
Shangguan, Zhongli
Yan, Chuanbo
Liu, Tingting
Wu, Dan
author_facet Wu, Miao
Shen, Xiaoxia
Lai, Can
Zheng, Weihao
Li, Yingqun
Shangguan, Zhongli
Yan, Chuanbo
Liu, Tingting
Wu, Dan
author_sort Wu, Miao
collection PubMed
description 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.
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spelling pubmed-82184792021-06-23 Detecting neonatal acute bilirubin encephalopathy based on T1-weighted MRI images and learning-based approaches Wu, Miao Shen, Xiaoxia Lai, Can Zheng, Weihao Li, Yingqun Shangguan, Zhongli Yan, Chuanbo Liu, Tingting Wu, Dan BMC Med Imaging Research Article 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. BioMed Central 2021-06-22 /pmc/articles/PMC8218479/ /pubmed/34158001 http://dx.doi.org/10.1186/s12880-021-00634-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Wu, Miao
Shen, Xiaoxia
Lai, Can
Zheng, Weihao
Li, Yingqun
Shangguan, Zhongli
Yan, Chuanbo
Liu, Tingting
Wu, Dan
Detecting neonatal acute bilirubin encephalopathy based on T1-weighted MRI images and learning-based approaches
title Detecting neonatal acute bilirubin encephalopathy based on T1-weighted MRI images and learning-based approaches
title_full Detecting neonatal acute bilirubin encephalopathy based on T1-weighted MRI images and learning-based approaches
title_fullStr Detecting neonatal acute bilirubin encephalopathy based on T1-weighted MRI images and learning-based approaches
title_full_unstemmed Detecting neonatal acute bilirubin encephalopathy based on T1-weighted MRI images and learning-based approaches
title_short Detecting neonatal acute bilirubin encephalopathy based on T1-weighted MRI images and learning-based approaches
title_sort detecting neonatal acute bilirubin encephalopathy based on t1-weighted mri images and learning-based approaches
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8218479/
https://www.ncbi.nlm.nih.gov/pubmed/34158001
http://dx.doi.org/10.1186/s12880-021-00634-z
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