Machine learning models for predicting vasospasm following ruptured intracranial aneurysms: a systematic review and meta-analysis.

ABSTRACT:

Background

Cerebral vasospasm remains a leading cause of delayed cerebral ischemia following aneurysmal subarachnoid hemorrhage (aSAH). Despite advances in critical care, current monitoring strategies are reactive and non-personalized. Machine learning (ML) has emerged as a promising tool to anticipate vasospasm risk.

Methods

A systematic review and meta-analysis were performed following PRISMA 2020 guidelines. PubMed and Embase databases were searched for studies applying ML algorithms to predict clinical or radiological vasospasm. Data were pooled using bivariate and proportional meta-analyses and their quality was assessed with the PROBAST tool.

Results

Twelve studies (2011-2025) encompassing 25 ML models were included. Deep learning achieved the highest sensitivit

SUBMITTER: Palermo M 

PROVIDER: S-EPMC12678459 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature