CEREBLEED: Automated quantification and severity scoring of intracranial hemorrhage on non-contrast CT
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Background
Intracranial hemorrhage (ICH), whether spontaneous or traumatic, is a neurological emergency with high morbidity and mortality. Accurate assessment of severity is essential for neurosurgical decision-making. This study aimed to develop and evaluate a fully automated, deep learning-based tool for the standardized assessment of ICH severity, based on the segmentation of the hemorrhage and intracranial structures, and the computation of an objective severity index.
Methods
Non-contrast cranial CT scans from patients with spontaneous or traumatic ICH were retrospectively collected from public datasets and a tertiary care center. Deep learning models were trained to segment hemorrhages and intracranial structures. These segmentations were used to compute a severity index reflecting bleeding burden and mass effect through volumetric relationships. Segmentation performance was evaluated on a hold-out test cohort. In a prospective cohort, the severity index was assessed in relation to expert-rated CT severity, clinical outcomes, and the need for urgent neurosurgical intervention.
Results
A total of 1,110 non-contrast cranial CT scans were analyzed, 900 from the retrospective cohort and 200 from the prospective evaluation cohort. The binary segmentation model achieved a median Dice score of 0.90 for total hemorrhage. The multilabel model yielded Dice scores ranging from 0.55 to 0.94 across hemorrhage subtypes. The severity index significantly correlated with expert-rated CT severity (p < 0.001), the modified Rankin Scale (p = 0.007), and the Glasgow Outcome Scale–Extended (p = 0.039), and independently predicted the need for urgent surgery (p < 0.001). A threshold ∼300 was identified as a decision point for surgical management (AUC = 0.83).
Conclusion
We developed a fully automated and openly accessible pipeline for the analysis of non-contrast cranial CT in intracranial hemorrhage. It computes a novel index that objectively quantifies hemorrhage severity and is significantly associated with clinically relevant outcomes, including the need for urgent neurosurgical intervention.
