Abstract
To enhance thrombolysis eligibility in acute ischemic stroke, we developed a deep learning model to estimate stroke onset within 4.5 h using diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) images. Given the variability in human interpretation, our multimodal Res-U-Net (mRUNet) model integrates a modified U-Net and ResNet-34 to classify stroke onset as < 4.5 or ≥ 4.5 h. Using DWI and FLAIR images from patients scanned within 24 h of symptom onset, the modified U-Net generated a DWI-FLAIR mismatch image, while ResNet-34 performed the final classification. mRUNet was evaluated against ResNet-34 and DenseNet-121 on an internal test set (n = 123) and two external test sets: a single-center (n = 468) and a multi-center (n = 1151). mRUNet achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.903 on the internal set and 0.910 and 0.868 on external sets, significantly outperforming ResNet-34 and DenseNet-121. Our mRUNet model demonstrated robust and consistent classification of the 4.5-h onset-time window across datasets. By leveraging DWI and FLAIR images as a tissue clock, this model may support timely and individualized thrombolysis in patients with unclear stroke onset, such as those with wake-up stroke, in clinical settings.