The Development of a Reduced Order Model for Prediction of Haemodynamic and Biochemical Changes in a Computational Cerebral Aneurysm Thrombosis Model

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A cerebral aneurysm is a pathological, localized, and irreversible cerebral arterial vessel wall dilation. Cerebral aneurysms pose a risk of subarachnoid haemorrhage if they rupture. A ruptured cerebral aneurysm could lead to mortality or morbidity. It is therefore important for clinicians to be able to predict whether an aneurysm will rupture or not. This enables them to decide whether to perform surgical or endovascular procedures or not intervene at all. However, medical imaging is not always possible, thus there is a need for the development of computational tools to assist with predictions. A computational fluid dynamics (CFD) model that couples haemodynamics and biochemistry was developed to simulate cerebral aneurysm thrombosis. The blood was modelled as a Newtonian fluid and thrombin generation curves (TGCs) were used to model the release of thrombin from the injured vessel walls. TGCs representative of haemophiliac, healthy and thrombotic patients were used. Thrombin reacts with fibrinogen to produce fibrin and form a clot within the aneurysmal sac. The CFD results were used to build reduced-order models using machine learning algorithms. Multiple polynomial regression and logistic regression machine learning algorithms were used to predict clot size in patients. The K-nearest neighbours algorithm was used to develop a model that classifies patients' clotting profiles. The biochemistry was found to be more sensitive to mesh size compared to the haemodynamics. Large timesteps overpredicted clot size in pulsatile flow. The rate of clot growth in pulsatile and plug flow were different; the predicted clot size in pulsatile flow was 14.6% greater than in plug flow. When variable diffusivity was used, the predicted clot size was 25.4% less than that with constant diffusivity. The numerical model was validated against the experimental results of Ngoepe et al. and there was good agreement in the predictions. Vortical structures that formed in the aneurysm sac evolved differently in the haemophiliac, healthy and thrombotic cases. The clot size, biochemistry and haemodynamics were found to be interdependent. Only the thrombotic case had full occlusion in the 5000 seconds of simulation time considered, however, the haemophiliac and healthy case had more than 90% occlusion. The clot in the healthy case was larger than in the haemophiliac patient. iv Logistic regression fitted the CFD data better than multiple polynomial regression. The KNN algorithm produced satisfactory decision boundaries and classified the patients effectively. The reduced order models developed from the classification and regression algorithms could assist clinicians in interventional planning and reduce the cost of health care.