A comparative analysis of deep learning-based quantitative hemorrhagic CT parameters versus traditional semi-quantitative CT scores for predicting delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage: a multicenter cohort study

Background: Delayed cerebral ischemia (DCI) is the leading cause of poor outcomes after aneurysmal subarachnoid hemorrhage (aSAH). Hemorrhage extent assessment on pre-treatment CT is critical for DCI prediction, but traditional semi-quantitative CT scores remain subjective and imprecise despite their widespread use. This study aimed to assess whether deep learning (DL)-based quantitative hemorrhagic CT parameters outperform traditional scores in predicting DCI. Materials and Methods: Patients with aSAH from a prospectively maintained observational registry trial database (the *BLIND* study) were stratified into retrospective (2021.01 to 2023.12), prospective (2024.01 to 2024.12), and external validation (2018.07 to 2024.11) cohort. Hemorrhage was quantified using 3D-UNet. The primary outcome was DCI. Multivariate analyses explored the association between DL-based parameters and DCI. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used to compare the predictive accuracy and clinical benefit of DL-based parameters (based on SAH volume with the combination of intraventricular hemorrhage, intraparenchymal hematoma, and subdural hematoma) with traditional scores. Results: The three cohorts were comparable in baseline characteristics. Multivariate binary logistic analysis showed that DL-based parameters significantly correlated with DCI (all p < 0.05). ROC analysis revealed superior predictive accuracy for DL-based parameters compared to traditional scores across all cohorts: AUCs ranged 0.735-0.816 vs. 0.635-0.698 for traditional scores (all adjusted p < 0.05 by multiple DeLong tests). DCA demonstrated more favorable clinical benefit for DL-based parameters. Conclusion: DL-based quantitative hemorrhagic CT parameters, particularly SAH volume-related parameters, provide significantly better predictive accuracy and clinical benefit for DCI in aSAH patients compared to traditional semi-quantitative CT scores, and may offer a more objective and precise method for future risk stratification.