aceCT-based prediction of hematoma expansion and adverse outcomes after intracerebral hemorrhage: evidence appraisal, artificial intelligence translation, and GeroScience perspectives
Spontaneous intracerebral hemorrhage (ICH) is a highly lethal and disabling form of stroke, in which hematoma expansion (HE) is a major and potentially modifiable determinant of early neurological deterioration and poor functional outcome. Computed tomography (CT) remains the first-line imaging modality for acute ICH and provides essential information for early HE risk stratification. However, current evidence is dispersed across conventional CT signs, composite scores, radiomics, machine learning, and deep lear…
CT-based prediction, including AI approaches, provides essential information for early hematoma expansion risk stratification to support individualized management after spontaneous intracerebral hemorrhage.
Artificial intelligence approaches for CT-based prediction of hematoma expansion and adverse outcomes after spontaneous ICH are limited by small cohorts, overfitting, dataset heterogeneity, insufficient external validation, poor interpretability and lack of workflow integration.
Evidence base is fragmented and many AI models lack robust validation, with small cohorts, overfitting, dataset heterogeneity and insufficient external validation limiting translational readiness and workflow integration.
Evidence
- Peer-reviewedGeroScience2026-08-15
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Truvace Impact Record TRV-2026-0810, v1: “CT-based prediction of hematoma expansion and adverse outcomes after intracerebral hemorrhage: evidence appraisal, artificial intelligence translation, and GeroScience perspectives.” Truvace, 2026-08-17. /record/TRV-2026-0810 (accessed at citation time). sha256 a77db113efe013d0…
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