Developing a Predictive Model for Ischemic Stroke Onset Time Using Transfer Learning.

Introduction Identification of acute ischemic stroke (AIS) patients within the 4.5-hour therapeutic window is critical for therapy. Diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) sequences are an approach to determine whether the time since stroke (TSS) is within 4.5 hours. However, inter-observer variability and limited accuracy are observed in visual assessments. We aim to develop a transfer-learning model for predicting AIS onset within 4.5 hours. Materials and Methods 266 AIS patients with known TSS who underwent imaging scans before treatment were retrospectively analysed, divided into a training set (n = 211) and a validation set (n = 55). The model was built using DWI and FLAIR sequences. After image preprocessing and data augmentation, a 3D ResNet-18 pretrained on the Kinetics dataset was selected and adapted via transfer-learning with DWI-FLAIR input. The model performance was compared with human visual assessment, which was based on the DWI-FLAIR mismatch principle. Partial mismatch was defined as hyperintense infarct on DWI with a smaller corresponding hyperintense area on FLAIR. Results Baseline characteristics did not differ between the training and validation sets. On the validation set, the model achieved sensitivity of 0.833 (0.703-0.941), specificity of 0.880 (0.737-1.000), and AUC of 0.929 (0.758-0.935), outperforming human visual assessment (sensitivity 0.767(0.613-0.903); specificity 0.360(0.185-0.560); AUC 0.563(0.451-0.693)). For partial DWI-FLAIR mismatch cases, the model correctly classified all 15 cases, whereas humans classified 4. Conclusion The 3D ResNet-18 model shows promise in identifying AIS within 4.5 hours, including partial DWI-FLAIR mismatch, but requires multi-center validation before use.