Abstract
Cerebral aneurysms affect a significant portion of the adult population worldwide. Despite significant progress, the development of robust techniques to evaluate the risk of aneurysm rupture remains a critical challenge. We hypothesize that vertebral artery fusiform aneurysm (VAFA) morphology may be predictive of rupture risk and can serve as a deciding factor in clinical management. To investigate the VAFA morphology, we use a combination of image analysis and machine learning techniques to study a geometric feature set computed from a depository of 37 (12 ruptured and 25 un-ruptured) aneurysm images. Of the 571 unique features we compute, we distinguish five features for use by our machine learning classification algorithm by an analysis of statistical significance. These machine learning methods achieve state-of-the-art classification performance (81.43 ± 13.08%) for the VAFA morphology, and identify five features (cross-sectional area change of aneurysm, maximum diameter of nearby distal vessel, solidity of aneurysm, maximum curvature of nearby distal vessel, and ratio of curvature between aneurysm and its nearby proximal vessel) as effective predictors of VAFA rupture risk. These results suggest that the geometric features of VAFA morphology may serve as useful non-invasive indicators for the prediction of aneurysm rupture risk in surgical settings.
Publication
Royal Society Open Science (5)
Head of Data Science
Nathan is the Head of Data Science at Shara Inc. a lending company in Sub-Saharan Africa using peer selection and network financing to offer credit facilities to merchants in local markets. He is responsible for the majority of data-related projects and model development. This includes infrastructure set up for a Modern Data Stack (ELT pipelines and data warehousing), analytics needs for product development and monitoring, and machine learning modelling.
Assistant Professor
Shixin Xu is an Assistant Professor of Mathematics at Duke Kunshan University. His research interests are machine learning and data-driven models for diseases, multiscale modeling of complex fluids, homogenization theory, and numerical analysis. Xu has a B.Sc. in mathematics (honors) from Ocean University of China and a Ph.D. in mathematics from the University of Science and Technology China. From 2013 to 2017, he held postdoctoral positions at the National University of Singapore, the University of Notre Dame, the University of California, Riverside, and the Fields Institute for Research in Mathematical Sciences, Canada.
Professor
Arvind Gupta is Professor of Computer Science at the University of Toronto. He has served as President and Vice-Chancellor of UBC, and as the CEO and Scientific Director of Mitacs Inc. He is also a founder of Palette Skills Inc. He has published extensively on theoretical computer science, computational genomics and national innovation strategies.
Professor
Huaxiong Huang is Professor of Mathematics at the York University. He is VP (Academic) and Executive Director of Research Center of Mathematics (Zhuhai, China). He has served as Deputy Director of the Fields Institute and Director of the Fields Centre for Quantitative Analysis and Modelling. His wide array of publications in applied mathematics focus on fluid mechanics and scientific computing, finance, biology, physiology, energy and medicine.