Automated delineation of acute ischemic stroke lesions on non-contrast CT using 3D deep learning: A promising step towards efficient diagnosis and treatment

Purpose: We adopt the existing Deep learning architecture to support diagnosing acute ischemic stroke by automatically detecting lesion location on 3D non -contrast CT brain scans. We also investigate the feasibility of the model's applications in the clinical scenario by data analysis. Methods: We retrospectively collected 3D non -contrast CT scans of 317 patients with acute ischemic stroke from the China Medical University Hospital. All patients underwent standard baseline non -contrast CT scanning followed by diffusion -weighted imaging. We utilized these data for training the existing model - SwinUNETR, which includes a self -attention module as an encoder and a convolutional -based decoder. Moreover, the software innovatively incorporates uncertainty quantification to enhance model performance. Results: In the test set, the AI model predicted lesion volume with a mean Dice score of 46.7 % compared to diffusion -weighted imaging verified by experts. The model completed the analysis on a 3D non -contrast CT scan in approximately 30 s. The average difference between the model -segmented acute ischemic stroke lesion volume (67.11 ml) and diffusion -weighted imaging lesion volume (35.2 ml) was 27.09 ml. Pearson correlation of lesion volume between prediction and ground truth is 83.46 %. We also found our model has superior performance in the CT scan with lesion volume > 40 ml and 3 h < onset -to -CT time <24 h. Moreover, our approach was applied to the AISD public dataset, yielding a Dice score of 0.619 upon testing. Conclusions: This model could help facilitate timely and accurate diagnosis of acute ischemic stroke in a clinical emergency setting and low-resourced hospital.