{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T04:50:30Z","timestamp":1788497430694,"version":"build-2803163510"},"reference-count":13,"publisher":"Georg Thieme Verlag KG","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Neurol Surg A Cent Eur Neurosurg"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>\n          Background\u2003Symptomatic cerebral vasospasms are deleterious complication of the rupture of a cerebral aneurysm and potentially lethal. The existing scales used to classify the initial presentation of a subarachnoid hemorrhage (SAH) offer a blink of the outcome and the possibility of occurrence of symptomatic cerebral vasospasms. Altogether, neither are they sufficient to predict outcome or occurrence of events reliably nor do they offer a united front. This study tests the common grading scales and factors that otherwise affect the outcome, in an artificial intelligence (AI) based algorithm to create a reliable prediction model for the occurrence of cerebral vasospasms.<\/jats:p><jats:p>\n          Methods\u2003Applying the R environment, an easy-to-operate command line was programmed to prognosticate the occurrence of vasospasms. Eighty-seven patients with aneurysmal SAH during a 24-month period of time were included for study purposes. The holdout and cross-validation methods were used to evaluate the algorithm (65 patients constituted the validation set and 22 patients constituted the test set). The Support Vector Machines (ksvm) classification method provided a high accuracy. The medical dataset included demographic data, the Hunt and Hess scale (H&amp;H), Fisher grade, Barrow Neurological Institute (BNI) scale, length of intervention for aneurysmal repair, etc.<\/jats:p><jats:p>\n          Results\u2003Our prediction model based on the AI algorithm demonstrated an accuracy of 61 to 86% for the event of symptomatic vasospasms. For subgroup analysis, 28.8% (n\u2009=\u200913) patients in the surgical cohort developed symptomatic vasospasm. Of these, 50% (n\u2009=\u20097) were admitted with Fisher scale grade 4, 37.5% (n\u2009=\u20095) with H&amp;H 5, and 28.5% (n\u2009=\u20094) with BNI 5. In the endovascular cohort, vasospasms occurred in 31.8% (n\u2009=\u200914) patients. Of these, 69% (n\u2009=\u20099) patients were admitted with Fisher grade 4, 23% (n\u2009=\u20093) patients with H&amp;H 5, and 7% (n\u2009=\u20091) patients with BNI 5.<\/jats:p><jats:p>\n          Conclusion\u2003From our data, we may believe that the algorithm presented can help in identifying patients with SAH who are at \u201chigh\u201d or \u201clow\u201d risk of developing symptomatic vasospasms. This risk balancing might further allow the treating physician to go for an earlier intervention trying to prevent permanent sequelae. Certainly, accuracy will improve with a higher caseload and more statistical coefficients.<\/jats:p>","DOI":"10.1055\/a-2402-6136","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T22:49:46Z","timestamp":1724453386000},"page":"196-204","source":"Crossref","is-referenced-by-count":5,"title":["Artificial Intelligence Prediction Model of Occurrence of Cerebral Vasospasms Based on Machine Learning"],"prefix":"10.1055","volume":"86","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0229-0139","authenticated-orcid":false,"given":"Konstantinos","family":"Lintas","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Klinikum Dortmund gGmbH, Dortmund, Nordrhein-Westfalen, Germany"},{"name":"Department of Neurosurgery, Witten\/Herdecke University, Witten, Nordrhein-Westfalen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Rohde","sequence":"additional","affiliation":[{"name":"Radiology and Neuroradiology, Klinikum Dortmund gGmbH, Dortmund, Nordrhein-Westfalen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Mpoukouvala","sequence":"additional","affiliation":[{"name":"Graduate in Statistics and Mathematical Modeling, Aristotle University of Thessaloniki, Thessalonike, Kentrik\u1e17 Makedon\u00eda, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boris","family":"El Hamalawi","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Klinikum Dortmund gGmbH, Dortmund, Nordrhein-Westfalen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Sarge","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Klinikum Dortmund gGmbH, Dortmund, Nordrhein-Westfalen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver Marcus","family":"Mueller","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Klinikum Dortmund gGmbH, Dortmund, Nordrhein-Westfalen, Germany"},{"name":"Department of Neurosurgery, Witten\/Herdecke University, Witten, Nordrhein-Westfalen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"194","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"issue":"04","key":"ref1","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1227\/NEU.0b013e318267360f","article-title":"A simple and quantitative method to predict symptomatic vasospasm after subarachnoid hemorrhage based on computed tomography: beyond the Fisher scale","volume":"71","author":"D A Wilson","year":"2012","journal-title":"Neurosurgery"},{"issue":"01","key":"ref2","first-page":"21","article-title":"Prediction of symptomatic vasospasm after subarachnoid hemorrhage: the modified fisher scale","volume":"59","author":"J A 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hemorrhage","author":"L V Vossen","year":"2023","journal-title":"J NeuroIntervent Surg"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"e97","DOI":"10.1016\/j.wneu.2022.09.042","article-title":"Impact of treatment timing on the risk of cerebral infarction in patients with aneurysmal subarachnoid hemorrhage","volume":"168","author":"A N Santos","year":"2022","journal-title":"World Neurosurg"},{"issue":"19","key":"ref10","doi-asserted-by":"crossref","first-page":"5850","DOI":"10.3390\/jcm11195850","article-title":"Delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage: the results of induced hypertension only after the IMCVS trial: a prospective cohort study","volume":"11","author":"E G\u00fcresir","year":"2022","journal-title":"J Clin Med"},{"issue":"20","key":"ref11","doi-asserted-by":"crossref","first-page":"6197","DOI":"10.3390\/jcm11206197","article-title":"Invasive diagnostic and therapeutic management of cerebral vasospasm after aneurysmal subarachnoid hemorrhage (IMCVS): a phase 2 randomized controlled trial","volume":"11","author":"H Vatter","year":"2022","journal-title":"J Clin Med"},{"issue":"02","key":"ref12","doi-asserted-by":"crossref","first-page":"E150","DOI":"10.1093\/neuros\/nyaa401","article-title":"Development of a complication- and treatment-aware prediction model for favorable functional outcome in aneurysmal subarachnoid hemorrhage based on machine learning","volume":"88","author":"N Maldaner","year":"2021","journal-title":"Neurosurgery"},{"issue":"05","key":"ref13","doi-asserted-by":"crossref","first-page":"2837","DOI":"10.1007\/s10143-020-01453-6","article-title":"Outcome prediction in aneurysmal subarachnoid hemorrhage: a comparison of machine learning methods and established clinico-radiological scores","volume":"44","author":"N F Dengler","year":"2021","journal-title":"Neurosurg Rev"}],"container-title":["Journal of Neurological Surgery Part A: Central European 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