IDEAS home Printed from https://ideas.repec.org/a/taf/gcmbxx/v28y2025i7p937-948.html
   My bibliography  Save this article

Sensitivity analysis of the mechanical properties on atherosclerotic arteries rupture risk with an artificial neural network method

Author

Listed:
  • Di Zuo
  • Daye Chen
  • Mingji Zhu
  • Qiwen Xue

Abstract

Considering the differences between individuals, in this paper, an uncertainty analysis model for predicting rupture risk of atherosclerotic arteries is established based on a back-propagation artificial neural network. The influence of isotropy and anisotropy on the rupture risk of atherosclerotic arteries is analyzed, and the results demonstrate the effectiveness of the artificial neural network in predicting the rupture risk. Moreover, the rupture risk of atherosclerotic arteries at different inflation sizes are simulated. This study contributes to a better understanding of the underlying mechanisms of atherosclerotic arteries rupture and promotes the advancement of artificial neural networks in atherosclerosis research.

Suggested Citation

  • Di Zuo & Daye Chen & Mingji Zhu & Qiwen Xue, 2025. "Sensitivity analysis of the mechanical properties on atherosclerotic arteries rupture risk with an artificial neural network method," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 28(7), pages 937-948, May.
    • Handle: RePEc:taf:gcmbxx:v:28:y:2025:i:7:p:937-948
    DOI: 10.1080/10255842.2024.2305862
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10255842.2024.2305862
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10255842.2024.2305862?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:gcmbxx:v:28:y:2025:i:7:p:937-948. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/gcmb .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.