Detecting acute bilirubin encephalopathy in neonates based on multimodal MRI images and non-image clinical data
Abstract Purpose Accurately detecting Acute bilirubin encephalopathy (ABE) from non-ABE neonates with hyperbilirubinemia (HB) condition remains a challenge in clinical practice. In this study, an automatic ABE diagnosing system based on multi-modal MRI images and non-image clinical metadata is propo...
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BMC
2025-05-01
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| Series: | BMC Pediatrics |
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| Online Access: | https://doi.org/10.1186/s12887-025-05411-3 |
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| author | Miao Wu Qian Liu Can Lai |
| author_facet | Miao Wu Qian Liu Can Lai |
| author_sort | Miao Wu |
| collection | DOAJ |
| description | Abstract Purpose Accurately detecting Acute bilirubin encephalopathy (ABE) from non-ABE neonates with hyperbilirubinemia (HB) condition remains a challenge in clinical practice. In this study, an automatic ABE diagnosing system based on multi-modal MRI images and non-image clinical metadata is proposed to address the issue. Methods A total of 75 ABE neonates and 75 non-ABE neonates with HB are included in the study. Each patient has 3 multi-modal magnetic resonance images and 8 non-image clinical features. To investigate the diagnosing model’s performance, 3 different feature sets, namely deep features from multi-modal MRI images, non-image clinical features, and fusion features, are extracted, respectively, and then further classified by a support vector machine (SVM), respectively. Results The results indicated the SVM classifier built on the fusion features achieved the best classification performance with an accuracy of 93.24 ± 2.35, specificity of 91.38 ± 4.45%, sensitivity of 95.11 ± 2.97%, precision of 91.87 ± 3.88%, area-under-the-curve (AUC) of 98.08 ± 1.16%, F1_score of 93.38 ± 2.23%. The performance of the SVM classifier built on the deep features was better than that built on the non-image clinical features. Conclusion Our study demonstrated that ABE diagnostic performance based on deep features from multi-modal MRI images could be significantly improved by incorporating clinical features. The proposed strategy may potentially be applicable to clinical practice to facilitate clinical management. |
| format | Article |
| id | doaj-art-e97b90d4e41e4f6c859946dc3e825a5d |
| institution | DOAJ |
| issn | 1471-2431 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pediatrics |
| spelling | doaj-art-e97b90d4e41e4f6c859946dc3e825a5d2025-08-20T03:16:51ZengBMCBMC Pediatrics1471-24312025-05-012511810.1186/s12887-025-05411-3Detecting acute bilirubin encephalopathy in neonates based on multimodal MRI images and non-image clinical dataMiao Wu0Qian Liu1Can Lai2College of Medical Engineering and Technology, Xinjiang Medical UniversityBasic Medical College, Xinjiang Medical UniversityDepartment of Radiology, Children’s Hospital, Zhejiang University School of MedicineAbstract Purpose Accurately detecting Acute bilirubin encephalopathy (ABE) from non-ABE neonates with hyperbilirubinemia (HB) condition remains a challenge in clinical practice. In this study, an automatic ABE diagnosing system based on multi-modal MRI images and non-image clinical metadata is proposed to address the issue. Methods A total of 75 ABE neonates and 75 non-ABE neonates with HB are included in the study. Each patient has 3 multi-modal magnetic resonance images and 8 non-image clinical features. To investigate the diagnosing model’s performance, 3 different feature sets, namely deep features from multi-modal MRI images, non-image clinical features, and fusion features, are extracted, respectively, and then further classified by a support vector machine (SVM), respectively. Results The results indicated the SVM classifier built on the fusion features achieved the best classification performance with an accuracy of 93.24 ± 2.35, specificity of 91.38 ± 4.45%, sensitivity of 95.11 ± 2.97%, precision of 91.87 ± 3.88%, area-under-the-curve (AUC) of 98.08 ± 1.16%, F1_score of 93.38 ± 2.23%. The performance of the SVM classifier built on the deep features was better than that built on the non-image clinical features. Conclusion Our study demonstrated that ABE diagnostic performance based on deep features from multi-modal MRI images could be significantly improved by incorporating clinical features. The proposed strategy may potentially be applicable to clinical practice to facilitate clinical management.https://doi.org/10.1186/s12887-025-05411-3DetectingAcute bilirubin encephalopathyHyperbilirubinemiaNeonatesMultimodal MRI imagesNon-image clinical data |
| spellingShingle | Miao Wu Qian Liu Can Lai Detecting acute bilirubin encephalopathy in neonates based on multimodal MRI images and non-image clinical data BMC Pediatrics Detecting Acute bilirubin encephalopathy Hyperbilirubinemia Neonates Multimodal MRI images Non-image clinical data |
| title | Detecting acute bilirubin encephalopathy in neonates based on multimodal MRI images and non-image clinical data |
| title_full | Detecting acute bilirubin encephalopathy in neonates based on multimodal MRI images and non-image clinical data |
| title_fullStr | Detecting acute bilirubin encephalopathy in neonates based on multimodal MRI images and non-image clinical data |
| title_full_unstemmed | Detecting acute bilirubin encephalopathy in neonates based on multimodal MRI images and non-image clinical data |
| title_short | Detecting acute bilirubin encephalopathy in neonates based on multimodal MRI images and non-image clinical data |
| title_sort | detecting acute bilirubin encephalopathy in neonates based on multimodal mri images and non image clinical data |
| topic | Detecting Acute bilirubin encephalopathy Hyperbilirubinemia Neonates Multimodal MRI images Non-image clinical data |
| url | https://doi.org/10.1186/s12887-025-05411-3 |
| work_keys_str_mv | AT miaowu detectingacutebilirubinencephalopathyinneonatesbasedonmultimodalmriimagesandnonimageclinicaldata AT qianliu detectingacutebilirubinencephalopathyinneonatesbasedonmultimodalmriimagesandnonimageclinicaldata AT canlai detectingacutebilirubinencephalopathyinneonatesbasedonmultimodalmriimagesandnonimageclinicaldata |