Investigation of Rupture Risk of Thoracic Aortic Aneurysms via Fluid-Structure Interaction and Artificial Intelligence Method

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dc.authorid0000-0003-0565-5914
dc.contributor.authorKoru, Murat
dc.contributor.authorCanbolat, Gokhan
dc.contributor.authorDaricik, Fatih
dc.contributor.authorKarahan, Oguz
dc.contributor.authorEtli, Mustafa
dc.contributor.authorKorkmaz, Ergun
dc.date.accessioned2026-01-24T12:31:02Z
dc.date.available2026-01-24T12:31:02Z
dc.date.issued2024
dc.departmentAlanya Alaaddin Keykubat Üniversitesi
dc.description.abstractPatient-specific studies on vascular flows have significantly increased for hemodynamics due to the need for different observation techniques in clinical practice. In this study, we investigate aortic aneurysms in terms of deformation, stress, and rupture risk. The effect of Ascending Aortic Diameter (AAD) was investigated in different aortic arches (19.81 mm, 42.94 mm, and 48.01 mm) via Computational Fluid Dynamics (CFD), Two-way coupling Fluid-Structure Interactions (FSI) and deep learning. The non-newtonian Carreau viscosity model was utilized with patient-specific velocity waveform. Deformations, Wall Shear Stresses (WSSs), von Mises stress, and rupture risk were presented by safety factors. Results show that the WSS distribution is distinctly higher in rigid cases than the elastic cases. Although WSS values rise with the increase in AAD, aneurysm regions indicate low WSS values in both rigid and elastic artery solutions. For the given AADs, the deformations are 2.75 mm, 6. 82 mm, and 8.48 mm and Equivalent von Mises stresses are 0.16 MPa, 0.46 MPa, and 0.53 MPa. When the rupture risk was evaluated for the arteries, the results showed that the aneurysm with AAD of 48.01 mm poses a risk up to three times more than AAD of 19.81 mm. In addition, an Artificial neural network (ANN) method was developed to predict the rupture risk with a 98.6% accurate prediction by numerical data. As a result, FSI could indicate more accurately the level of rupture risk than the rigid artery assumptions to guide the clinical assessments and deep learning methods could decrease the computational costs according to CFD and FSI.
dc.description.sponsorshipAlanya Alaaddin Keykubat University
dc.description.sponsorshipThis paper was not financially supported by any foundation.
dc.identifier.doi10.1007/s13369-024-08810-3
dc.identifier.endpage14802
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85186585273
dc.identifier.scopusqualityQ1
dc.identifier.startpage14787
dc.identifier.urihttps://doi.org/10.1007/s13369-024-08810-3
dc.identifier.urihttps://hdl.handle.net/20.500.12868/5605
dc.identifier.volume49
dc.identifier.wosWOS:001176055600001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofArabian Journal For Science and Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260121
dc.subjectComputational fluid dynamics (CFD)
dc.subjectFluid-structure interactions (FSI)
dc.subjectDeep learning
dc.subjectArtificial neural network (ANN)
dc.subjectSafety factor
dc.subjectVascular flow
dc.titleInvestigation of Rupture Risk of Thoracic Aortic Aneurysms via Fluid-Structure Interaction and Artificial Intelligence Method
dc.typeArticle

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