Machine learning algorithms to predict the risk of rupture of intracranial aneurysms - a systematic review

Aug 2, 2026

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Subarachnoid haemorrhage is a potentially fatal outcome of intracranial aneurysm rupture, yet predicting which aneurysms will rupture remains challenging. Prophylactic treatment also carries risk, making identification of rupture-prone aneurysms clinically important. This systematic review evaluates the performance of machine learning algorithms for predicting rupture risk, using searches of MEDLINE, Embase, Cochrane Library and Web of Science up to December 2023.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages36

Reading Time1 h 31 min

AccessFree to download

Abstract

Purpose

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King’s Research Portal Document Version Peer reviewed version Link to publication record in King's Research Portal Citation for published version (APA): Daga, K. , Agarwal, S. , Moti, Z. , Lee, M. , Din, M. , Wood, D. , Modat, M. , & Booth, T. (in press) . Machine learning algorithms to predict the risk of rupture of intracranial aneurysms: a systematic review. Clinical neuroradiology. Citing this paper Please note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections. General rights Copyright and moral rights for the publications made accessible in the Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights. •Users may download and print one copy of any publication from the Research Portal for the purpose of private study or research. •You may not further distribute the material or use it for any profit-making activity or commercial gain •You may freely distribute the URL identifying the publication in the Research Portal Take down policy If you believe that this document breaches copyright please contact [librarypure@kcl.ac.uk](librarypure@kcl.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim. Download date: 23. Oct. 2024 Title Page Title: Machine learning algorithms to predict the risk of rupture of intracranial aneurysms: a systematic review. Authors: Karan Daga1,2, Siddharth Agarwal 1, Zaeem Moti2, Matthew BK Lee3, Munaib Din2, David Wood1, Marc Modat 1, Thomas C Booth1,4 . Affiliations: 1 School of Biomedical Engineering & Imaging Sciences, King’s College London, London UK. 2 Guy’s and St. Thomas’NHS Foundation Trust, London UK. 3 University College London Hospital NHS Foundation Trust, London UK. 4 Department of Neuroradiology, King’s College Hospital, London UK. Corresponding Author: Name: Dr. Thomas C Booth Email: [thomas.booth@kcl.ac.uk](thomas.booth@kcl.ac.uk) [Telephone](Telephone:)[:](Telephone:) +[44](44) ([0](0)) 20 7848 9568 Department: School of Biomedical Engineering & Imaging Sciences Institute: King’s College London Address: BMEIS, King’s College London. 1 Lambeth Palace Road. London, UK SE1 7EU. Funding: T.C.B. and D.W. are supported by the UK Medical Research Council (MR/W021684/1) and by the Wellcome EPSRC Centre for Medical Engineering at King's College London (203148/Z/16/Z) Competing Interests: All authors declare no financial or non-financial competing interests. Data Availability: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Author Contributions 1. KD: Conception and design of the review, acquisition, analysis and interpretation of the data and writing the manuscript. 2. SA: Conception and design of the review, acquisition, analysis and interpretation of the data. 3. ZM: Acquisition and analysis of the data 4. ML: Conception and design of the review, interpretation of the data. 5. MD: Conception and design of the review, acquisition of the data 6. DW: Conception and design of the review 7. MM: Conception and design of the review 8. TCB: Conception and design of the review, acquisition, analysis and interpretation of the data and writing the manuscript. All authors have read and approved the final manuscript. TCB is the corresponding author and guarantor of the study. Ethics approval: No ethics approval was requ

FAQs

What clinical problem does this systematic review address?

It addresses the difficulty of predicting intracranial aneurysm rupture risk and the clinical importance of identifying rupture-prone aneurysms to guide prophylactic decisions.

How were studies selected for the review?

MEDLINE, Embase, Cochrane Library and Web of Science were searched until December 2023, including studies that used any machine learning algorithm to predict rupture risk of intracranial aneurysms.

What do the results indicate about machine learning performance and limitations?

Across 20 included studies, model performance accuracy ranged from 0.66 to 0.90, but comparisons with clinical standards were mixed, many studies had high or unclear bias, and insufficient homogeneous data prevented meta-analysis.

Aug 2, 2026

Upload by Oliver