Artificial Intelligence for Rupture Risk Stratification of Small Unruptured Intracranial Aneurysms (≤ 7 mm): A Systematic Review and Meta-analysis.

Authors

Neda Kamandi, Saeed Mohammadzadeh, Moein Mirzai, Moein Ghasemi, Danial Elyassirad, Seyed Amir Asef Agah, Kavous Firouznia

Published in

Journal of imaging informatics in medicine. Oct 27, 2026. Epub Oct 27, 2026.

Abstract

Small unruptured intracranial aneurysms (UIAs; ≤ 7 mm) carry collectively significant rupture risk, yet conventional scoring tools (PHASES, ELAPSS) perform near chance-level in this subgroup. This systematic review and meta-analysis evaluated the pooled diagnostic accuracy of AI-based models for rupture risk prediction in small UIAs and compared performance across algorithmic architectures. Prospectively registered with PROSPERO (CRD420251142915) and conducted per PRISMA-DTA/PRISMA-S standards. MEDLINE, Scopus, and Web of Science were searched in October 2025 and updated on June 2026. Eligible studies applied machine learning (ML), deep learning (DL), radiomics, or computational fluid dynamics-integrated models to small UIAs. Area under the receiver operating characteristic curve (AUC) was pooled via a three-level random-effects model with restricted maximum likelihood estimation and Knapp-Hartung correction; heterogeneity was explored through multivariable meta-regression. Bias and reporting quality were assessed using QUADAS-3 and METRICS, respectively. Ten studies (2019-2025) encompassing 5409 patients, 7057 aneurysms, and 62 models were included. Pooled AUC was 0.793 (95% CI 0.754-0.827), with sensitivity of 0.713 and specificity of 0.780. Conventional ML achieved the highest AUC (0.827), followed by statistical models (0.779) and DL (0.741). Meta-regression explained 76.2% of variance; statistical model family was the sole significant predictor of inferior performance (p = 0.002). Nine of ten studies carried high risk of bias. No publication bias was detected (p = 0.186). AI-based models, particularly conventional ML ensemble architectures, demonstrate moderate-to-good discriminative accuracy for small UIA rupture risk stratification, meaningfully surpassing conventional scoring tools. Multicenter prospective registries, pre-rupture imaging protocols, and explainable AI frameworks are essential to support transparent neurosurgical decision-making.

PMID:
42944966
Bibliographic data and abstract were imported from PubMed on 28 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.