ANALYSIS OF ANEURYSMS IN MIDDLE CEREBRAL ARTERIES: A REVIEW OF CURRENT COMPUTATIONAL MODELLING - Mat Zin, Ahmad Faiz; Taib, Ishkrizat; Abdul Rahman, Muhammad Riddha; Yahya, Mohd Shahir; Roslan, Muhammad Sufi (INTERNATIONAL JOURNAL OF INNOVATION AND INDUSTRIAL REVOLUTION (IJIREV), 2026)
Cerebral aneurysms in the middle cerebral artery (MCA), particularly saccular aneurysms, present a significant clinical risk due to their potential for rupture and associated high morbidity and mortality. This review aims to analyse current computational modelling approaches used to investigate haemodynamics, rupture risk, and treatment strategies for MCA aneurysms. A systematic literature review was conducted using three major databases: PubMed, Scopus, and IEEE. Articles published between 2019 and 2024 were identified using predefined keywords related to saccular aneurysms, computational modelling, and MCA. Inclusion criteria focused on original studies involving human or patient-specific models, while non-computational studies, reviews, and non-MCA-related research were excluded. A total of 14 studies were selected following PRISMA guidelines. Data were extracted and synthesised based on modelling approaches, haemodynamic parameters, and clinical applications. Most studies employed computational fluid dynamics (CFD) to analyse haemodynamic factors, including wall shear stress (WSS), oscillatory shear index (OSI), and inflow jet dynamics. These parameters were consistently associated with aneurysm growth and rupture risk. Patient-specific modelling and integration with imaging techniques, such as computed tomography angiography (CTA) and magnetic resonance imaging (MRI), improved simulation accuracy. Alternative computational methods, including finite element analysis (FEA), statistical modelling, and image-based reconstruction, complement CFD in device evaluation and morphological assessment. Additionally, computational modelling has been widely applied in optimising treatment strategies, including flow-diverting devices and bypass surgeries. Computational modelling plays a crucial role in advancing the understanding and management of MCA aneurysms. While CFD remains the dominant approach for haemodynamic analysis, integrating multi-modal imaging, patient-specific data, and emerging techniques such as machine learning can further improve rupture risk prediction and personalised treatment planning. Future research should focus on enhancing model accuracy and clinical translation.