Hemodynamics is thought to play an important role in the pathogenesis of cerebral aneurysms and recent development of computer technology makes it possible to simulate blood flow using high-resolution 3D images within several hours. A lot of studies of computational fluid dynamics (CFD) for cerebral aneurysms were reported; therefore, application of CFD for cerebral aneurysms in clinical settings is reviewed in this article.CFD for cerebral aneurysms using a patient-specific geometry model was first reported in 2003 and it has been revealing that hemodynamics brings a certain contribution to understanding aneurysm pathology, including initiation, growth and rupture. Based on the knowledge of the state-of-the-art techniques, this review treats the decision-making process for using CFD in several clinical settings. We introduce our CFD procedure using digital imaging and communication in medicine (DICOM) datasets of 3D CT angiography or 3D rotational angiography. In addition, we review rupture status, hyperplastic remodeling of aneurysm wall, and recurrence of coiled aneurysms using the hemodynamic parameters such as wall shear stress (WSS), oscillatory shear index (OSI), aneurysmal inflow rate coefficient (AIRC), and residual flow volume (RFV).
The authors described a case of AVF of the CE fed by the PRA and demonstrated the difficulty of the differentiation from that of the FT. The utmost precautions are necessary when endovascular treatment is selected.
To examine whether machine learning (ML) approach can be used to predict hematoma expansion in acute intracerebral hemorrhage (ICH) with accuracy and widespread applicability, we applied ML algorithms to multicenter clinical data and CT findings on admission. Patients with acute ICH from three hospitals (n = 351) and those from another hospital (n = 71) were retrospectively assigned to the development and validation cohorts, respectively. To develop ML predictive models, the k-nearest neighbors (k-NN) algorithm, logistic regression, support vector machines (SVMs), random forests, and XGBoost were applied to the patient data in the development cohort. The models were evaluated for their performance on the patient data in the validation cohort, which was compared with previous scoring methods, the BAT, BRAIN, and 9-point scores. The k-NN algorithm achieved the highest area under the receiver operating characteristic curve (AUC) of 0.790 among all ML models, and the sensitivity, specificity, and accuracy were 0.846, 0.733, and 0.775, respectively. The BRAIN score achieved the highest AUC of 0.676 among all previous scoring methods, which was lower than the k-NN algorithm (p = 0.016). We developed and validated ML predictive models of hematoma expansion in acute ICH. The models demonstrated good predictive ability, showing better performance than the previous scoring methods.
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