Reduction of rupture risk in ICA aneurysms by endovascular techniques of coiling and stent: numerical study

Cerebral aneurysms are a silent yet prevalent condition that affects a significant global population. Their development can be attributed to various factors, presentations, and treatment approaches. The importance of selecting the appropriate treatment becomes evident upon diagnosis, as the severity of the disease guides the course of action. Cerebral aneurysms are particularly vulnerable in the circle of Willis and pose a significant concern due to the potential for rupture, which can lead to irreversible consequences, including fatality. The primary objective of this study is to predict the rupture status of cerebral aneurysms. To achieve this, we leverage a comprehensive dataset that incorporates clinical and morphological data extracted from 3D real geometries of previous patients. The aim of this research is to provide valuable insights that can help make informed decisions during the treatment process and potentially save the lives of future patients. Diagnosing and predicting aneurysm rupture based solely on brain scans is a significant challenge with limited reliability, even for experienced physicians. However, by employing statistical methods and machine learning techniques, we can assist physicians in making more confident predictions regarding rupture likelihood and selecting appropriate treatment strategies. To achieve this, we used 5 classification machine learning algorithms and trained them on a substantial database comprising 708 cerebral aneurysms. The dataset comprised 3 clinical features and 35 morphological parameters, including 8 novel morphological features introduced for the first time in this study. Our models demonstrated exceptional performance in predicting cerebral aneurysm rupture, with accuracy ranging from 0.76 to 0.82 and precision score from 0.79 to 0.83 for the test dataset. As the data are sensitive and the condition is critical, recall is prioritized as the more crucial parameter over accuracy and precision, and our models achieved outstanding recall score ranging from 0.85 to 0.92. Overall, the best model was Support Vector Machin with an accuracy and precision of 0.82, recall of 0.92 for the testing dataset and the area under curve of 0.84. The ellipticity index, size ratio, and shape irregularity are pivotal features in predicting aneurysm rupture, respectively, contributing significantly to our understanding of this complex condition. Among the multitude of parameters under investigation, these are particularly important. In this study, the ideal roundness parameter was introduced as a novel consideration and ranked fifth among all 38 parameters. Neck circumference and outlet numbers from the new parameters were also deemed significant contributors.

Section: Introductionmentioning

confidence: 99%

A comprehensive investigation of morphological features responsible for cerebral aneurysm rupture using machine learning

Zakeri,

Atef,

Aziznia

et al. 2024

“…The hemodynamic characteristics of an aneurysm, particularly the WSS and OSI, are influenced by various geometrical features 9 , 10 . The size of the aneurysm, including its diameter and volume, has a direct impact on the flow patterns and the magnitude of WSS.…”

Section: Introductionmentioning

confidence: 99%

Impacts of morphology parameters on the risk of rupture in intracranial aneurysms: statistical and computational analyses

Wang,

Jin,

Chen

et al. 2023

The hemodynamic analysis of the blood stream inside the cerebral aneurysms reveals the risk of the aneurysm rupture. In addition, the high risk region prone to rupture would be determined by the hemodynamic analysis of the blood. In present article, computational fluid dynamic is used for the investigation of the hemodynamic effects on the aneurysm wall and risk of rupture. This study tries to find the connection between the risk of rupture with three geometrical features of aneurysm i.e., Ellipsoid Max semi-axis, Size ratio and Tortuosity. Statistical analysis is done over 30 different ruptured /unruptured ICA aneurysms to find meaningful relation between selected geometrical factors and rupture risk. The hemodynamic analysis is done over four distinct aneurysm models to attain more details on effects of chosen geometrical factors. The results of simulations indicate that the Ellipsoid Max semi-axis have meaningful impacts on the risk of rupture.

“…The side effect of stent usage is the deformation of the parent vessel which also has a great impact on the performance of the occlusion of blood stream into the main sac area 15 – 18 . The recognition of the blood hemodynamic under impacts of the stent-induced deformation offers valuable information for surgeons for estimation of long-term treatment of the aneurysm 19 – 22 .…”

Section: Introductionmentioning

confidence: 99%

Analysis of the effects of stent-induced deformation on the hemodynamics of MCA aneurysms

Huang,

Zhou,

Duan

et al. 2023

The use of a stent to coil an aneurysm can alter the position of the main blood vessel and affect blood flow within the sac. This study thoroughly examines the impact of stent-induced changes on the risk of MCA aneurysm rupture. The research aims to assess the effects of coiling and vessel deformation on blood flow dynamics by comparing the OSI, WSS, and blood structure of two distinct MCA aneurysms to identify high-risk areas for hemorrhage. Computational fluid dynamics is used to model blood flow. The results indicate that aneurysm deformation does not always decrease the risk of rupture, and coiling is more effective in occluding blood flow than aneurysm deformation.