Predicting the risk of rupture for vertebral aneurysm based on geometric features of blood vessels.
Repository Usage Stats
A significant proportion of the adult population worldwide suffers from cerebral aneurysms. If left untreated, aneurysms may rupture and lead to fatal massive internal bleeding. On the other hand, treatment of aneurysms also involve significant risks. It is desirable, therefore, to have an objective tool that can be used to predict the risk of rupture and assist in surgical decision for operating on the aneurysms. Currently, such decisions are made mostly based on medical expertise of the healthcare team. In this paper, we investigate the possibility of using machine learning algorithms to predict rupture risk of vertebral artery fusiform aneurysms based on geometric features of the blood vessels surrounding but excluding the aneurysm. For each of the aneurysm images (12 ruptured and 25 unruptured), the vessel is segmented into distal and proximal parts by cross-sectional area and 382 non-aneurysm-related geometric features extracted. The decision tree model using two of the features (standard deviation of eccentricity of proximal vessel, and diameter at the distal endpoint) achieved 83.8% classification accuracy. Additionally, with support vector machine and logistic regression, we also achieved 83.8% accuracy with another set of two features (ratio of mean curvature between distal and proximal parts, and diameter at the distal endpoint). Combining the aforementioned three features with integration of curvature of proximal vessel and also ratio of mean cross-sectional area between distal and proximal parts, these models achieve an impressive 94.6% accuracy. These results strongly suggest the usefulness of geometric features in predicting the risk of rupture.
Published Version (Please cite this version)10.1098/rsos.210392
Publication InfoLi, Shixuan; Pan, Ruiqi; Gupta, Arvind; Xu, Shixin; Fang, Yibin; & Huang, Huaxiong (2021). Predicting the risk of rupture for vertebral aneurysm based on geometric features of blood vessels. Royal Society open science, 8(8). pp. 210392. 10.1098/rsos.210392. Retrieved from https://hdl.handle.net/10161/23925.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
More InfoShow full item record
Assistant Professor of Mathematics at Duke Kunshan University
Shixin Xu is an Assistant Professor of Mathematics. His research interests are machine learning and data-driven model for diseases, multiscale modeling of complex fluids, Neurovascular coupling, homogenization theory, and numerical analysis. The current projects he is working on are image data-based for the prediction of hemorrhagic transformation in acute ischemic stroke, electrodynamics modeling electrochemical modeling <
Articles written by Duke faculty are made available through the campus open access policy. For more information see: Duke Open Access Policy
Rights for Collection: Scholarly Articles
Works are deposited here by their authors, and represent their research and opinions, not that of Duke University. Some materials and descriptions may include offensive content. More info