Fig. 1
From: Deep learning to identify stroke within 4.5 h using DWI and FLAIR in a prospective multicenter study
Classification performance of the proposed multimodal Res-U-Net model. (A) In the training set (n = 487), mRUNet achieved an AUC of 0.951 (95% CI 0.905–0.989), outperforming ResNet-34 (AUC, 0.888; 95% CI 0.806–0.951) and DenseNet-121 (AUC, 0.835; 95% CI 0.756–0.908). (B) In the internal test set (n = 123), mRUNet achieved an AUC of 0.903 (95% CI 0.835–0.959), significantly higher than ResNet-34 (AUC, 0.768; 95% CI 0.670–0.853; p = 0.007) and DenseNet-121 (AUC, 0.790; 95% CI 0.760–0.867; p = 0.011). (C) In the external test set 1 (n = 468, single-center cohort), mRUNet achieved an AUC of 0.910 (95% CI 0.883–0.935), significantly higher than ResNet-34 (AUC, 0.790; 95% CI 0.744–0.833; p < 0.001) and DenseNet-121 (AUC, 0.814; 95% CI 0.775–0.853; p < 0.001). (D) In the external test set 2 (n = 1151, multi-center cohort), mRUNet achieved an AUC of 0.868 (95% CI 0.848–0.888), significantly outperforming ResNet-34 (AUC, 0.805; 95% CI 0.777–0.830; p < 0.001) and DenseNet-121 (AUC, 0.808; 95% CI 0.783–0.832; p < 0.001). AUC-ROC, area under the receiver operating characteristic curve; CI, confidence interval; mRUNet, multimodal Res-U-Net.
