## 4 Discussion
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Specific Application** & **AI Approach** & & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{Segmentation} & \multirow{4}{*}{3D UNet, RF + MGAC, MCV} & \multirow{4}{*}{–} & Can accurately segment different structures in the brain. & \multirow{4}{*}{–} & Requires a large amount of training data. \\  & & & Can capture spatial information and context by leveraging features, allowing them to understand the relationship between pixels and their neighbors [48]. & \multirow{4}{*}{–} & May struggle with segmenting fine-grained or intricate details in images [48]. \\ \cline{3-4}  & & & & & \\ \hline \multirow{4}{*}{Classification DL methods} & \multirow{4}{*}{–} & \multirow{4}{*}{–} & Can achieve high accuracy in classification tasks [48]. & \multirow{4}{*}{–} & Requires a large amount of training data. \\  & & DCNN, Grad-CAM, DCNN-PCA, ResNet34, DCNN + FPO, NHDDeepDNN, Bayesian UNet, DenseNets, UNet & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{Classification DL methods} & \multirow{4}{*}{LogReg, RF, SVM, MLP, GaussNh, GBDT, ExraTrees, XGB, AdaBoost} & \multirow{4}{*}{–} & Provides probability estimates for each class, allowing for probabilistic interpretations of the classification results [33]. & \multirow{4}{*}{–} & Sensitive to outliers, affecting its performance [18]. \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{Feature analysis} & \multirow{4}{*}{PCA, LogReg, RF} & \multirow{4}{*}{–} & Can identify specific features that are associated with NPH [50]. & \multirow{4}{*}{–} & The features may not be specific to NPH and may be present in other conditions [50]. \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{Feature analysis} & \multirow{4}{*}{PCA, LogReg, RF} & \multirow{4}{*}{–} & Can identify specific features that are associated with NPH [50]. & \multirow{4}{*}{–} & The features may not be specific to NPH and may be present in other conditions [50]. \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{4}{*}{Prediction} & \multirow{4}{*}{XGB, SVM-RF-Other-CNN} & \multirow{4}{*}{–} & Can predict the risk of developing NPH or the response to CSF shunting [33]. & \multirow{4}{*}{–} & The predictions may not be accurate for all patients [32]. \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of advantages and disadvantages of AI approaches for the detection of NPH. Despite their superior accuracy, deep learning methods require substantially larger training datasets and higher computational resources [48; 49]. More critically, they often function as \"black boxes\" with internal representations that are challenging to interpret [49], limiting their explainability, a crucial factor for clinical adoption. Traditional ML methods, while typically less accurate, offer clearer interpretability and can perform reasonably well with smaller datasets. In this review, we found that MRI scans were more typically used than CT scans. MRIs provide a better visualization of CSF and ventricular volume. However, some researchers achieved good performance using CT scans, demonstrating the potential value of both modalities as data sources. The choice of imaging modality may depend on factors like data availability, computational requirements, and clinical needs. Dice similarity coefficients, intraclass correlations, and ROC-AUC are commonly used metrics to evaluate segmentation and classification models. Reported Dice coefficients for ventricular segmentation range from 0.70 to 0.96, indicating generally good agreement between automated and manual segmentation.