Reliable and rapid estimation of blood flow dynamics factors in eccentric stenotic arteries could significantly improve clinical treatments. Numerical simulation methods such as FSI and CFD are widely used to investigate blood flow conditions. However, both FSI and CFD are computationally expensive and not suitable for large-scale research. This work proposes an effective approach for estimating the blood flow dynamics factors of eccentric stenotic arteries based on ML. The estimation approach includes three steps: (a) blood flow conditions of idealized eccentric stenotic arteries modeled with different geometric parameters were simulated by CFD and FSI, and the error between CFD and FSI results were evaluated, (b) datasets of nonlinear relationships between discretized geometric parameters and blood flow dynamics factors were created using CFD, and (c) blood flow dynamics factors of eccentric stenotic arteries were estimated by carefully designed ML model. The accuracy validation was conducted by different representative cases, which have different combinations of geometric parameters including stenosis severity, eccentricity, and stenosis plaque length. The ML model can output the blood flow dynamics factors including peak wall shear stress and peak systole velocity accurately within 1 s, which shows proposed approach not only achieves accurate estimation of blood flow dynamics factors but also bypasses the expensive computational cost.

中文翻译：

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基于机器学习估计偏心狭窄动脉血流动力学因素的新方法

可靠而快速地估计偏心狭窄动脉中的血流动力学因素可以显着改善临床治疗。FSI和CFD等数值模拟方法被广泛用于研究血流状况。然而，FSI 和 CFD 的计算成本都很高，不适合大规模研究。这项工作提出了一种基于机器学习估计偏心狭窄动脉血流动力学因素的有效方法。该估计方法包括三个步骤：（a）通过CFD和FSI模拟不同几何参数建模的理想化偏心狭窄动脉的血流状况，并评估CFD和FSI结果之间的误差，（b）离散化之间的非线性关系数据集使用CFD创建几何参数和血流动力学因子，并且(c)通过精心设计的ML模型估计偏心狭窄动脉的血流动力学因子。准确性验证是通过不同的代表性病例进行的，这些病例具有不同的几何参数组合，包括狭窄严重程度、偏心率和狭窄斑块长度。ML模型可以在1秒内准确输出包括峰值壁剪切应力和峰值收缩速度在内的血流动力学因素，这表明所提出的方法不仅实现了血流动力学因素的准确估计，而且绕过了昂贵的计算成本。