{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T11:04:36Z","timestamp":1771671876313,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T00:00:00Z","timestamp":1651708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Federal Ministry of Education and Research","award":["01\/S18026A-F"],"award-info":[{"award-number":["01\/S18026A-F"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Intracranial aneurysms represent a potentially life-threatening condition and occur in 3\u20135% of the population. They are increasingly diagnosed due to the broad application of cranial magnetic resonance imaging and computed tomography in the context of headaches, vertigo, and other unspecific symptoms. For each affected individual, it is utterly important to estimate the rupture risk of the respective aneurysm. However, clinically applied decision tools, such as the PHASES score, remain insufficient. Therefore, a machine learning approach assessing the rupture risk of intracranial aneurysms is proposed in our study. For training and evaluation of the algorithm, data from a single neurovascular center was used, comprising 446 aneurysms (221 ruptured, 225 unruptured). The machine learning model was then compared with the PHASES score and proved superior in accuracy (0.7825), F1-score (0.7975), sensitivity (0.8643), specificity (0.7022), positive predictive value (0.7403), negative predictive value (0.8404), and area under the curve (0.8639). The frequency distributions of the predicted rupture probabilities and the PHASES score were analyzed. A symmetry can be observed between the rupture probabilities, with a symmetry axis at 0.5. A feature importance analysis reveals that the body mass index, consumption of anticoagulants, and harboring vessel are regarded as the most important features when assessing the rupture risk. On the other hand, the size of the aneurysm, which is weighted most in the PHASES score, is regarded as less important. Based on our findings we discuss the potential role of the model for clinical practice in geographically confined aneurysm patients.<\/jats:p>","DOI":"10.3390\/sym14050943","type":"journal-article","created":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T02:46:39Z","timestamp":1651805199000},"page":"943","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Machine Learning for Rupture Risk Prediction of Intracranial Aneurysms: Challenging the PHASES Score in Geographically Constrained Areas"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5531-2613","authenticated-orcid":false,"given":"Georg","family":"Walther","sequence":"first","affiliation":[{"name":"ScaDS.AI, Faculty of Mathematics and Computer Science, Leipzig University, Humboldtstra\u00dfe 25, 04105 Leipzig, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3128-630X","authenticated-orcid":false,"given":"Christian","family":"Martin","sequence":"additional","affiliation":[{"name":"ScaDS.AI, Faculty of Mathematics and Computer Science, Leipzig University, Humboldtstra\u00dfe 25, 04105 Leipzig, Germany"}]},{"given":"Amelie","family":"Haase","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Clinic and Polyclinic for Neurosurgery, University Hospital Leipzig, Liebigstra\u00dfe 20, 04103 Leipzig, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2963-4344","authenticated-orcid":false,"given":"Ulf","family":"Nestler","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Clinic and Polyclinic for Neurosurgery, University Hospital Leipzig, Liebigstra\u00dfe 20, 04103 Leipzig, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2846-5443","authenticated-orcid":false,"given":"Stefan","family":"Schob","sequence":"additional","affiliation":[{"name":"Department of Neuroradiology, Clinic and Policlinic of Radiology, University Hospital Halle, Ernst-Grube-Stra\u00dfe 40, 06120 Halle (Saale), Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1016\/S1474-4422(11)70109-0","article-title":"Prevalence of unruptured intracranial aneurysms, with emphasis on sex, age, comorbidity, country, and time period: A systematic review and meta-analysis","volume":"10","author":"Vlak","year":"2011","journal-title":"Lancet Neurol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1056\/NEJMoa070972","article-title":"Incidental Findings on Brain MRI in the General Population","volume":"357","author":"Vernooij","year":"2007","journal-title":"N. 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