Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/80971
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dc.contributor.authorCanchi, Tejasen
dc.contributor.authorKumar, Srinivasan Dineshen
dc.contributor.authorNg, Eddie Yin Kweeen
dc.contributor.authorNarayanan, Sriramen
dc.date.accessioned2015-12-07T08:54:35Zen
dc.date.accessioned2019-12-06T14:18:37Z-
dc.date.available2015-12-07T08:54:35Zen
dc.date.available2019-12-06T14:18:37Z-
dc.date.issued2015en
dc.identifier.citationCanchi, T., Kumar, S. D., Ng, E. Y. K., & Narayanan, S. (2015). A Review of Computational Methods to Predict the Risk of Rupture of Abdominal Aortic Aneurysms. BioMed Research International, 2015, 861627-.en
dc.identifier.issn2314-6133en
dc.identifier.urihttps://hdl.handle.net/10356/80971-
dc.identifier.urihttp://hdl.handle.net/10220/38984en
dc.description.abstractComputational methods have played an important role in health care in recent years, as determining parameters that affect a certain medical condition is not possible in experimental conditions in many cases. Computational fluid dynamics (CFD) methods have been used to accurately determine the nature of blood flow in the cardiovascular and nervous systems and air flow in the respiratory system, thereby giving the surgeon a diagnostic tool to plan treatment accordingly. Machine learning or data mining (MLD) methods are currently used to develop models that learn from retrospective data to make a prediction regarding factors affecting the progression of a disease. These models have also been successful in incorporating factors such as patient history and occupation. MLD models can be used as a predictive tool to determine rupture potential in patients with abdominal aortic aneurysms (AAA) along with CFD-based prediction of parameters like wall shear stress and pressure distributions. A combination of these computer methods can be pivotal in bridging the gap between translational and outcomes research in medicine. This paper reviews the use of computational methods in the diagnosis and treatment of AAA.en
dc.format.extent12 p.en
dc.language.isoenen
dc.relation.ispartofseriesBioMed Research Internationalen
dc.rights© 2015 Tejas Canchi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.titleA Review of Computational Methods to Predict the Risk of Rupture of Abdominal Aortic Aneurysmsen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen
dc.contributor.schoolLee Kong Chian School of Medicine (LKCMedicine)en
dc.identifier.doi10.1155/2015/861627en
dc.description.versionPublished versionen
item.grantfulltextopen-
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Appears in Collections:LKCMedicine Journal Articles
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