內漏是指腹主動脈瘤囊內與支架之間發生血液滲漏的情況。這種情況可能會導致動脈瘤的破裂，增加致命風險並引發其他併發症。為了評估病患的內漏風險，基於血液動力學的方法成為一種有前途的心血管疾病臨床評估技術。計算流體動力學是被廣泛應用於內漏評估的模擬方法。CFD利用數值解析的方式，對血液在支架和動脈瘤囊內的流動進行建模與模擬，從而預測內漏的影響與可能性。
物理資訊神經網絡則被認為是具有潛力超越 CFD的方法。 相比之下，PINNs 是基於深度學習的方法，通過將物理定律和數據驅動的學習相結合，來模擬血液動力學行為。PINNs 模型具有較強的學習能力和快速的計算速度。本研究通過比較 CFD 方法和 PINNs 方法在血液動力學的模擬結果，以深入研究支架在腹主動脈瘤中的影響。
結果顯示，兩種方法在支架內的流體域壓力、流場分布和壁面剪應力的預測上呈現出一致性和可靠性。具體而言，兩種模型在支架內的壓力分佈趨勢和流場特徵上相似，能夠捕捉到主要的流動行為。然而，在支架末端和特定區域，CFD 和 PINNs 之間存在一些差異，可能是由於模型的物理特性建模和幾何形狀對流場的影響不同所致。總體而言，PINNs 展現了良好的準確性和可靠性，對於血液動力學分析提供了有用的訊息。這些模擬方法可以提供對流場的詳細分析，幫助醫生了解內漏的潛在風險因素，並制定更有效的治療策略。

Intraluminal endoleaks refers to the blood seepage between the endovascular stent-graft and the aneurysm sac. This condition can lead to aneurysm rupture, increasing the risk of fatality and triggering other complications. To assess the risk of intraluminal leakage in patients, hemodynamics-based methods have emerged as promising clinical evaluation techniques for cardiovascular diseases. Computational Fluid Dynamics (CFD) is widely employed as a simulation method for hemodynamic assessment. It models and simulates the blood flow within the stent-graft and aneurysm sac using numerical analysis to predict the impact and likelihood of endoleaks.
On the other hand, Physics-Informed Neural Networks (PINNs) are considered a potential alternative to CFD. In contrast, PINNs are based on deep learning and combine the learning of physical laws with data-driven approaches to simulate hemodynamic behavior. PINNs models exhibit strong learning capabilities and fast computational speed. In this study, a comparison between CFD and PINNs methods was conducted to investigate the influence of the stent in abdominal aortic aneurysms through the simulation of hemodynamics.
The results showed consistent and reliable predictions of fluid pressure, flow distribution, and wall shear stress between the CFD and PINNs methods. Both models exhibited similar trends in pressure distribution and flow characteristics within the stent, capturing the primary flow behavior. However, some differences were observed, particularly at the stent's end and specific regions, which could be attributed to variations in modeling the physical characteristics and geometric effects on the flow field. Overall, PINNs demonstrated accurate and reliable performance, providing valuable insights for hemodynamic analysis.

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