Prediction of intraventricular hemorrhage based on deep learning of brain CT images

Abstract

Objective To explore the application of deep-learning technology in automatic classification of intraventricular hemorrhage. Methods The brain CT images of 1 027 patients with spontaneous ICH from the First Affiliated Hospital of Army Medical University from January 2010 to December 2020 were collected and retrospectively analyzed, which were subsequently divided into 3 types: normal, intracerebral hemorrhage with/without intraventricular extension.Six typical deep networks, including DenseNet121, ResNet101, ResNet50, Swin-base, Vit-base and VGG16, were used to construct classification models for identifying whether there was intraventricular extension of intracerebral hemorrhage.The performance of each model was assessed on both the internal and external data sets (CQ500) respectively.In order to enhance the interpretability of the deep learning networks, the EigenGradCAM method was adopted to generate heatmaps for visualization of the interested regions of the model. Results The performance of the models was evaluated based on precision, recall rate, specificity, negative predictive value (NPV) and F1 value.On the internal test set of VGG16 model, the normal group achieved 0.983, 0.977, 0.984, 0.978 and 0.980, respectively; the intracerebral hemorrhage without intraventricular extension group achieved 0.917, 0.902, 0.965, 0.958 and 0.909;and the intraventricular extension of intracerebral hemorrhage group obtained 0.877, 0.911, 0.966, 0.976 and 0.894, respectively.Meanwhile, on the external test set of VGG16 model, the normal group reached 0.967, 0.870, 0.985, 0.938 and 0.916;the intracerebral hemorrhage without intraventricular extension group reached 0.827, 0.939, 0.902, 0.967 and 0.879;and the intraventricular extension of intracerebral hemorrhage group achieved 0.938, 0.906, 0.970, 0.954 and 0.922.The accuracy (ACC) of internal and external test sets was 0.940 and 0.905, respectively.Finally, the heatmaps generated by EigenGradCAM method showed the VGG16 could reasonably focus on the relevant areas. Conclusion The VGG16 achieved the best predictive performance for identifying whether there is an intraventricular extension in intracerebral hemorrhage or not, indicating that deep learning can be effectively applied to the judgement of intraventricular hemorrhage.

Keywords

• deep learning
• ct
• intraventricular hemorrhage
• classification network