T1WI deep learning models for evaluating brain injury of neonatal hyperbilirubinemia
10.13929/j.issn.1003-3289.2025.03.010
- VernacularTitle:T1WI深度学习模型评估新生儿高胆红素血症脑损伤
- Author:
Jingwei CUI
1
;
Yongchao NIU
;
Beichen XIE
;
Chang LIU
;
Jinhui DUAN
;
Qin XUE
;
Ruifang YAN
Author Information
1. 新乡医学院第一附属医院磁共振科,河南新乡 453100
- Publication Type:Journal Article
- Keywords:
hyperbilirubinemia,neonatal;
brain injuries;
magnetic resonance imaging;
deep learning
- From:
Chinese Journal of Medical Imaging Technology
2025;41(3):394-398
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of T1WI deep learning models for evaluating brain injury of neonatal hyperbilirubinemia(NHB).Methods Totally 106 NHB(defined as newborns with neonatal behavioral neurological assessment≤37,NHB group)and 119 non-NHB newborns(control group)in center A,as well as 34 NHB and 18 non-NHB newborns in center B were collected.ROI was delineated based on bilateral globus pallidus on T1WI.A total of 690 slices were obtained by preprocessing data of center A and then were divided into training set(n=552)and test set(n=138)at a ratio of 8∶2.ResNet18,DenseNet121 and EfficientNetB0 models was established,respectively.External validation was performed based on data of center B.Receiver operating characteristic curves were drawn,area under the curves(AUC)were calculated to evaluate the performance of models for assessing NHB brain injuries compared with traditional visual analysis.Results The AUC of ResNet18 model for evaluating NHB brain injury was 0.910-0.990,significantly higher than that of DenseNet121 model(0.710-0.820)and EfficientNetB0 model(0.640-0.740)(all P＜0.001).The accuracy,sensitivity and precision of ResNet18 model for evaluating NHB brain injury were all higher than those of visual analysis(all P＜0.05),while no significant difference of specificity was found between the above two(P＞0.05).Conclusion T1WI ResNet18 model showed excellent performance and generalization ability for evaluating NHB brain injury.
