## 6 Discussion  
This review highlights the current challenge of insufficient accuracy in stroke risk stratification within clinical practice. It argues that the limitations of existing stratification methods stem from their over-reliance on demographic and clinical data, neglecting the valuable insights offered by modern imaging techniques and hemodynamic analysis. The review then explores potential avenues for improving stroke risk stratification, focusing on the shape, dimensions, \begin{table}
\begin{tabular}{c c c c c} \hline
**AI Task** & **Reference** & **Neural Network** & **Input** & **Output** & **Performance** \\ \hline A & [135] & U-net & CCT & Felling defects area & AUC = 0.979 \\  & & & & ICC \(\geq\) 0.895 \\ \hline A & [133] & 3D-Unet & CCTA & LV, Aorta, LA, LAA and Myoc & DSC = 0.882–0.961 \\ \hline B & [21] & PCA-FCN/U-net/GDL & \begin{tabular}{c} 2D mapping of the 3D \\ geometrical model for \\ PCA-FCN U-net and point \\ clouds of 3D geometrical \\ \end{tabular} & ECAP & GDL has the best performance \\ \hline B & [136] & GDL/U-net & \begin{tabular}{c} 3D point clouds for GDL and \\ mesh information for U-net \\ \end{tabular} & RRT & NRMSE = 0.08 \\ \hline B & [20] & GDL & 3D point clouds & TAWSS/OSI/ECAP/RRT & MAE = 0.784 \\ \hline B & [19] & GDL & 3D point clouds & Velocity and Pressure & NMAE = 5.7\% \\  & & & & Pressure & for pressure and 10.35\% \\ \hline C & [134] & DNN & Medical record & \begin{tabular}{c} risk score for \\ post-stroke AF \\ \end{tabular} & AUC = 0.922 \\ \hline C & [137] & \
\begin{tabular}{c} U-net \\ EfficientNetV2 \\ \end{tabular} & MRI with AF information & Stroke subtype &
\begin{tabular}{c} percentage agreement with \\ expert consensus = 72.9\% \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 4: Research on left atrial appendage based on artificial intelligence methods. CCT: cardiac computed tomography. CCTA: cardiac computed tomography angiography. ICC: intraclass correlation coefficient. DSC: dice similarity coefficient. NRMSE: normalized root mean square error. MAE: mean absolute error. NMAE: normalized mean absolute error. DNN: deep neural network. and hemodynamic characteristics of the LAA. A comprehensive overview of clinical techniques and methods for acquiring these parameters, including advanced imaging technologies and computer-aided analysis, is provided. Specifically, the review highlights the superior clinical applicability of quantitative dimensional assessment of the LAA compared to shape classification for stroke risk evaluation. Current clinical practice faces challenges in distinguishing subtle differences between the four classical LAA shapes, whereas dimensional measurements offer superior reproducibility and objectivity. Consistent findings indicate that increased LAA volume, higher lobe count, longer centerline length, and larger ostial diameter are all associated with a higher risk of thrombogenesis. However, further large-scale clinical-statistical analyses are needed to clarify the roles of factors such as LAA orifice characteristics and pulmonary vein positioning. Furthermore, this review elucidates the relationship between [PERSON]'s triad and LAA hemodynamic parameters, summarizing current evidence on stroke-associated hemodynamic indicators (Table 3). Existing indicators primarily focus on flow velocity and wall shear stress derivatives, with limited attention paid to pressure. Future investigations should explore potential correlations between intracardiac pressure variations and stroke risk. Importantly, current research predominantly focuses on the mechanisms of thrombosis. However, the risk of stroke is significantly reduced if a thrombus remains attached. Therefore, future studies should also prioritize investigating the mechanisms underlying thrombus detachment and subsequent embolic events. Translating these findings into clinical practice faces several obstacles. Accurate assessment of LAA geometry and hemodynamics requires advanced imaging modalities that may not be universally accessible.