Artificial Intelligence Prediction Model of Occurrence of Cerebral Vasospasms Based on Machine Learning - Lintas, Konstantinos; Rohde, Stefan; Mpoukouvala, Anna; El Hamalawi, Boris; Sarge, Robert; Mueller, Oliver Marcus (Journal of Neurological Surgery Part A: Central European Neurosurgery, 2024)
Background Symptomatic 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.Methods Applying 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&H), Fisher grade, Barrow Neurological Institute (BNI) scale, length of intervention for aneurysmal repair, etc.Results Our 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 = 13) patients in the surgical cohort developed symptomatic vasospasm. Of these, 50% (n = 7) were admitted with Fisher scale grade 4, 37.5% (n = 5) with H&H 5, and 28.5% (n = 4) with BNI 5. In the endovascular cohort, vasospasms occurred in 31.8% (n = 14) patients. Of these, 69% (n = 9) patients were admitted with Fisher grade 4, 23% (n = 3) patients with H&H 5, and 7% (n = 1) patients with BNI 5.Conclusion From our data, we may believe that the algorithm presented can help in identifying patients with SAH who are at “high” or “low” 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.
Keywords: artificial intelligence; machine learning; prediction model; cerebral vasospasms; subarachnoid hemorrhage