## 5 Diagnostic Methods and Techniques
### Computer Aided Diagnosis
#### 5.3.2 Artificial Intelligence  
AI is increasingly applied in clinical settings due to its strong performance in nonlinear high-dimensional analyses, such as image recognition and data regression [21; 133; 134]. Its applications in the context of the LA and LAA include extracting morphological information from medical images [133; 135], calculating hemodynamic indices [19; 20; 21; 136], and developing risk stratification or subtype classification based on specific indicators [134; 137]. Figure 4 and Table 4 illustrate the current research pipeline regarding AI applications in LAA/LA studies. In Figure 4, the pipeline represented by blue arrows outlines the process of integrating hemodynamic and geometric parameters into stroke risk stratification without the use of AI technology. The blue arrow labeled a denotes the model reconstruction and segmentation from medical imaging using non-AI methods, such as threshold segmentation, region growing, and edge detection. The output of this step is the geometrical model (for b) or geometrical parameters (for d). The blue arrow b represents the use of CFD methods. The blue arrows c and d indicate the expert judgment of experienced researchers or medical professionals, highlighting that each of these processes incurs significant costs in computation, manpower, and time. In contrast, the orange arrows represent three AI tasks (A/B/C) that can greatly simplify these processes. Task A involves utilizing AI techniques for reconstructing and segmenting models, as well as for identifying lesions in medical imaging. Task B refers to AI methods that predict hemodynamic parameters on the internal surface or wall of LA/LAA geometric models as an alternative to CFD. Task C pertains to AI technologies that directly diagnose stroke subtypes or assess stroke risk scores based on medical imaging. Currently, the clinical application of Task C often necessitates expert supervision of its results. However, with the continued advancement of AI explainability, algorithmic transparency, and generalization capabilities, AI holds the potential to become a significantly more efficient tool. This progress will foster a complementary partnership between AI and expert judgment. Figure 4: Pipeline of the CFD method (blue arrows) and AI method (orange arrows) for LA/LAA related tasks. a. non-AI methods for model reconstruction and segmentation from medical imaging; b. CFD methods; c. and d. expert judgement. A. AI methods for model reconstruction and segmentation from medical imaging; B. AI technology alternative to CFD; C. AI technologies that directly diagnose stroke subtypes or assess stroke risk scores based on medical imaging. Table 4 summarizes the references corresponding to Tasks A, B, and C. In this table, the most commonly used neural network (NN) related to imaging is U-Net (Tasks A/C), while the most frequently employed NN for flow field prediction is Graph-based Deep Learning (GDL) (Task B). U-Net is structured with a contracting path and an expanding path, which allows for the creation of highly detailed segmentation maps using a limited number of training samples. This is particularly important in the medical field, where labeled images are often scarce. Furthermore, due to its context-based learning approach, U-Net typically trains much faster than many other segmentation models [138,139]. On the other hand, GDL aggregates features from a given point along with those from its neighboring points to generate feature maps. This method significantly enhances the ability of traditional NNs to capture geometric features. Improvements in the interpretability of GDL can also assist clinicians in diagnosing diseases, such as identifying brain regions most relevant to specific tasks [140,141]. However, similar applications of GDL have not yet been explored in the context of LA/LAA tasks. In addition to the aforementioned applications, AI technologies excel at the induction and extraction of multidimensional indicators or datasets. [PERSON] et al.