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    Rapid wall shear stress prediction for aortic aneurysms using deep learning: a fast alternative to CFD

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    s11517-025-03311-3.pdf (2.827Mb)
    Date
    2025-02-17
    Author
    Faisal, Md Ahasan Atick
    Mutlu, Onur
    Mahmud, Sakib
    Tahir, Anas
    Chowdhury, Muhammad E.H.
    Bensaali, Faycal
    Alnabti, Abdulrahman
    Yavuz, Mehmet Metin
    El-Menyar, Ayman
    Al-Thani, Hassan
    Yalcin, Huseyin Cagatay
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    Abstract
    Abstract: Aortic aneurysms pose a significant risk of rupture. Previous research has shown that areas exposed to low wall shear stress (WSS) are more prone to rupture. Therefore, precise WSS determination on the aneurysm is crucial for rupture risk assessment. Computational fluid dynamics (CFD) is a powerful approach for WSS calculations, but they are computationally intensive, hindering time-sensitive clinical decision-making. In this study, we propose a deep learning (DL) surrogate, MultiViewUNet, to rapidly predict time-averaged WSS (TAWSS) distributions on abdominal aortic aneurysms (AAA). Our novel approach employs a domain transformation technique to translate complex aortic geometries into representations compatible with state-of-the-art neural networks. MultiViewUNet was trained on 23 real and 230 synthetic AAA geometries, demonstrating an average normalized mean absolute error (NMAE) of just 0.362% in WSS prediction. This framework has the potential to streamline hemodynamic analysis in AAA and other clinical scenarios where fast and accurate stress quantification is essential.
    URI
    https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85218196841&origin=inward
    DOI/handle
    http://dx.doi.org/10.1007/s11517-025-03311-3
    http://hdl.handle.net/10576/64033
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    • Biomedical Research Center Research [‎785‎ items ]
    • Biomedical Sciences [‎796‎ items ]
    • Electrical Engineering [‎2821‎ items ]
    • Mechanical & Industrial Engineering [‎1461‎ items ]

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