{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T09:07:13Z","timestamp":1788944833581,"version":"build-2803163510"},"reference-count":29,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Program for Research and Development (PKKZH) of the National Agency for Information Society"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Intracranial aneurysm rupture is a major cause of hemorrhagic stroke and a significant contributor to human mortality. With the emergence of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), a series of new approaches have been introduced to improve aneurysm detection, rupture assessment, risk prediction, and prognosis evaluation. This narrative review synthesizes the findings from 21 peer-reviewed studies identified through searches in PubMed, Scopus, Web of Science, and IEEE Xplore, covering the period from June to September 2025. The reviewed studies examined the use of AI for aneurysm detection, rupture assessment and risk prediction, and prognosis evaluation based on clinical, morphological, radiomic, and hemodynamic data. The reviewed studies applied different AI approaches, including conventional ML algorithms, convolutional neural networks, transformer-based models, radiomics, and multimodal models. Several studies reported encouraging performance in aneurysm detection, segmentation, rupture assessment, and prognosis evaluation, while some approaches showed better performance compared with traditional clinical scoring systems. However, important limitations remain, including the frequent use of small or single-center datasets, limited external validation, and heterogeneity in imaging protocols and feature-extraction methods. AI is showing strong potential in supporting the management of intracranial aneurysms. However, further research is needed, particularly through multicenter and external validation, improved model explainability and robustness, and better integration of these systems into clinical workflows.<\/jats:p>","DOI":"10.3390\/diagnostics16182901","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T08:05:43Z","timestamp":1788941143000},"page":"2901","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence for Intracranial Aneurysm Detection, Rupture-Risk Prediction, and Prognosis Evaluation: Recent Advances and Clinical Integration"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1080-5403","authenticated-orcid":false,"given":"Olti","family":"Qirici","sequence":"first","affiliation":[{"name":"Faculty of Natural Sciences, University of Tirana, 1000 Tirana, Albania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5097-9574","authenticated-orcid":false,"given":"Eugen","family":"Enesi","sequence":"additional","affiliation":[{"name":"Service of Radiology, University Hospital Center Mother Theresa, 1001 Tirana, Albania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9019-2481","authenticated-orcid":false,"given":"Kleona","family":"Binjaku","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Polytechnic University of Tirana, 1001 Tirana, Albania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7141-0475","authenticated-orcid":false,"given":"Anduel","family":"Kuqi","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Polytechnic University of Tirana, 1001 Tirana, Albania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2695-2726","authenticated-orcid":false,"given":"Indrit","family":"Enesi","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Polytechnic University of Tirana, 1001 Tirana, Albania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6749-117X","authenticated-orcid":false,"given":"Ana","family":"Ktona","sequence":"additional","affiliation":[{"name":"Faculty of Natural Sciences, University of Tirana, 1000 Tirana, Albania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7353-476X","authenticated-orcid":false,"given":"Elinda Kajo","family":"Me\u00e7e","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Polytechnic University of Tirana, 1001 Tirana, Albania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5827-0877","authenticated-orcid":false,"given":"Arjan","family":"Durresi","sequence":"additional","affiliation":[{"name":"Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, IN 46202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/S1474-4422(13)70263-1","article-title":"Development of the PHASES score for prediction of risk of rupture of intracranial aneurysms: A pooled analysis of six prospective cohort studies","volume":"13","author":"Greving","year":"2014","journal-title":"Lancet Neurol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2344","DOI":"10.1161\/STROKEAHA.119.025599","article-title":"Treatment Scoring of Unruptured Intracranial Aneurysms","volume":"50","author":"Juvela","year":"2019","journal-title":"Stroke"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.jvs.2007.10.018","article-title":"The Glasgow Aneurysm Score as a tool to predict 30-day and 2-year mortality in the patients from the Dutch Randomized Endovascular Aneurysm Management trial","volume":"47","author":"Baas","year":"2008","journal-title":"J. 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