Deep learning-based segmentation of aneurysmal subarachnoid hemorrhage: toward accurate and scalable prognostic imaging

This work proposes a fully automated approach for segmenting intracranial hemorrhages, focused on aneurysmal SAH. The model outperformed existing methods in accuracy and drastically reduced annotation time. Automated volume estimations matched manual annotations in predicting long-term outcomes and surpassed the modified Fisher scale. These results support integrating AI-based segmentation tools into clinical workflows for outcome modeling in aneurysmal hemorrhage.