MRI of bilirubin encephalopathy (kernicterus): A case series of 4 patients from Sub-Saharan Africa, May 2017

Neknek, Getachew Assefa; Woldemichael, Kindu; Moges, Ayalew; Solomon, Daniel Zewdneh

doi:10.1016/j.radcr.2018.03.018

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…Acute bilirubin encephalopathy is typically characterized by a high symmetric signal intensity in T1-weighted images in the GP, subthalamic nucleus (SN), and hippocampus regions [7]; however, in chronic bilirubin encephalopathy, the high signal intensity in T2-weighted images is more pronounced in the GP and SN compared to T1-weighted images [24,25]. Our experiment's results showed that the classification performance of the T2-weighted images was better than that of the T1-weighted images.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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“…Kernikterusun akut fazında en sık rastlanan tipik MRG bulgusu globus pallidusların posteromedial sınırlarında ve subtalamik nükleuslarda izlenen T1 hiperintensitedir (Resim 1). Bu görünüm hastalık süresi ilerledikçe kronik dönemde yerini T2 hiperintensiteye terkedecektir (Resim 2) [10]. Bu non-spesifik görünümler bağlanmamış bilirubinin birikimine bağlıdır ve ayırıcı tanısında hipoksik-iskemik ensealopati, diğer toksik-metabolik, enfeksiyöz ve kalıtsal hastalıkların göz önünde bulundurulması gerekir [3,11].…”

Section: öZunclassified

Bilirubin Ensefalopati

Erbay¹,

Şahin²

2023

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“…The most characteristic MRI finding described in kernicterus is a bilateral, highintensity signal in the globus pallidus (GP). Most studies to date have relied on images obtained from term infants with full-blown kernicterus or on infants with chronic bilirubin encephalopathy, studied months to years after the initial insult [1][2][3][4][5][6][7][8]15,16]. Our study is unique in that we studied preterm neonates, still in the neonatal period, and without clinical signs of bilirubin encephalopathy.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Mildly Elevated Bilirubin Levels are Associated with Increased Magnetic Resonance Imaging Signal Intensity in the Basal Ganglia of Preterm Neonates

et al. 2021

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Objective This study aimed to test whether mildly elevated bilirubin levels in preterm infants are associated with increased signal intensity (SI) on magnetic resonance imaging (MRI) of the basal ganglia (BG). Study Design MRI was performed at term equivalent age in 55 postpreterm infants using a neonatal MRI 1-T scanner. SI of the BG was correlated with mild hyperbilirubinemia. Results BG MRI SI was significantly increased in infants with mild hyperbilirubinemia on T1-weighted image (T1; p = 0.0393) and T2-weighted image (T2; p = 0.0309). We found no effect of gestational age or sepsis on BG MRI intensity; however, there was a significant effect of acidosis on T1 (p = 0.0223) but not on T2 (p = 0.2316). Infants with combined hyperbilirubinemia and acidosis had the most significant increase in SI on both T1 and T2 respectively (p = 0.0072 and 0.0195, respectively). Conclusion We found a positive association between increased BG MRI SI and mildly elevated bilirubin levels. The effect was greatly strengthened when hyperbilirubinemia was associated with acidosis. Key Points

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MRI of bilirubin encephalopathy (kernicterus): A case series of 4 patients from Sub-Saharan Africa, May 2017

Cited by 6 publications

References 4 publications

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

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

Bilirubin Ensefalopati

Mildly Elevated Bilirubin Levels are Associated with Increased Magnetic Resonance Imaging Signal Intensity in the Basal Ganglia of Preterm Neonates

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