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Updated: Aug 12, 2025
Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
Published on: January 15, 2022
An artificial intelligence-based platform for automatically estimating time-averaged wall shear stress in the
Lei Lv1, Haotian Li1, Zonglv Wu1,2
1Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yan Jiang West Road, 510120 Guangzhou, China.
Aims:
Aortopathies are a series of disorders requiring multiple indicators to assess risk. Time-averaged wall shear stress (TAWSS) is currently considered as the primary indicator of aortopathies progression, which can only be calculated by Computational Fluid Dynamics (CFD). However, CFD's complexity and high computational cost, greatly limit its application. The study aimed to construct a deep learning platform which could accurately estimate TAWSS in ascending aorta.
Methods And Results:
A total of 154 patients who had thoracic computed tomography angiography were included and randomly divided into two parts: training set (90%, n = 139) and testing set (10%, n = 15). TAWSS were calculated via CFD. The artificial intelligence (AI)-based model was trained and assessed using the dice coefficient (DC), normalized mean absolute error (NMAE), and root mean square error (RMSE). Our AI platform brought into correspondence with the manual segmentation (DC = 0.86) and the CFD findings (NMAE, 7.8773% ± 4.7144%; RMSE, 0.0098 ± 0.0097), while saving 12000-fold computational cost.
Conclusion:
The high-efficiency and robust AI platform can automatically estimate value and distribution of TAWSS in ascending aorta, which may be suitable for clinical applications and provide potential ideas for CFD-based problem solving.
