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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Twelve studies (2011\u20132025) encompassing 25 ML models were included. Deep learning achieved the highest sensitivity (mean: 97.6%) and AUC-ROC (0.97), outperforming regression, ensemble, and SVM methods in sensitivity (p\u2009=\u20090.003) but not in specificity or AUC. SVM models showed the highest NPV (85%), while ensemble and regression methods had superior PPV. Across cohort types, deep learning consistently delivered high accuracy and generalizability, although with greater PPV variability. Bivariate analysis confirmed that artificial neural networks and random forest models achieved favorable sensitivity\u2013specificity trade-offs. Risk of bias was low to moderate, with most concerns related to patient selection and lack of external validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>ML models, particularly deep learning and ensemble methods, demonstrate promising accuracy in predicting vasospasm after aSAH. These tools may enable earlier, personalized interventions; however, methodological heterogeneity, limited external validation, and lack of prospective trials currently hinder clinical adoption.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00701-025-06725-y","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T21:04:39Z","timestamp":1764795879000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Machine learning models for predicting vasospasm following ruptured intracranial aneurysms: a systematic review and meta-analysis"],"prefix":"10.1007","volume":"167","author":[{"given":"Matteo","family":"Palermo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sonia","family":"D\u2019Arrigo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Olivi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carmelo Lucio","family":"Sturiale","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,3]]},"reference":[{"key":"6725_CR1","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.jcrc.2016.09.011","volume":"37","author":"IRF Da Silva","year":"2017","unstructured":"Da Silva IRF, Gomes JA, Wachsman A, De Freitas GR, Provencio JJ (2017) Hematologic counts as predictors of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage. 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