## 4 Discussion
Studies also report high classification accuracy, between 85% and 97%, for differentiating NPH from other conditions. We found an increasing amount of studies applying DL methods for NPH diagnosis. For instance, [PERSON] et al. (2020) [29] conducted a study, in which, using DCNN and Grad-CAM, they were able to generate advances in the classification between AD and NPH, showing the potential of these methods based on generative algorithms. They also presented an approach to differentiate NPH from AD and its stages without the use of Amyloid-PET. They reported limitations in the number of subjects they had at their disposal. Leave-one-subject-out validation is commonly used in studies to test model generalizability. This method ensures that data from the same patient is not used for both training and testing the model, reducing bias. The use of techniques like Grad-CAM can help visualize what deep learning models \"see\" to identify discriminative image features. These model interpretation methods can provide insight to researchers and clinicians. There were studies where both traditional ML and DL methods were used. In those studies, we noted that the use of structural features in conjunction with the features generated using DL methods achieved promising results, demonstrating the relevance of both methods. Studies such as the one developed by [39] or [30], provided a clarity of the diversity of methods used to analyze these disorders and the latent need to continue improving the implemented techniques. DL methods show great potential to automatically extract relevant features from medical images for accurate NPH diagnosis. However, traditional ML still offers benefits through rigorous feature selection and model interpretability. A hybrid deep-traditional ML framework may capture the best of both worlds to develop precise yet generalizable diagnostic tools for NPH. Some key challenges remain for implementing ML in clinical practice. Datasets must be sufficiently large and diverse to validate models and account for heterogeneity. Regulatory hurdles, issues around data privacy, and expertise gaps also need to be addressed. With advancements in these areas, ML has the potential to transform NPH diagnosis through more efficient, accurate, and personalized analysis of medical data. For example, several ML and DL mixed approaches showed promising results for segmenting brain structures, classifying NPH, identifying informative features, and predicting outcomes for NPH patients. Segmentation methods like 3D UNet and random forest models aim to precisely delineate regions of interest in the brain. They can leverage spatial context and feature relationships to improve performance. However, these algorithms require large, annotated datasets for training. DCNN achieves high classification accuracy for NPH but depends on sizable training datasets. They can automatically extract relevant features, but their representations may be difficult to interpret. Traditional ML classifiersoffer probabilistic interpretations and generally perform well in high-dimensional spaces. However, they require hyperparameters to be tuned and are sensitive to outliers. The most promising results emerged from hybrid approaches that combined traditional ML and deep learning techniques. Reference [31] achieved exceptional results (97% accuracy, 100% specificity, and 96% sensitivity) by integrating Watershed segmentation with CNN. Similarly, [39] demonstrated that combining 3D UNet with probabilistic maps yielded better performance for subarachnoid space segmentation (72 \(\pm\) 5%) compared to either approach alone. These hybrid frameworks leverage the complementary strengths of both methodologies: the interpretability and efficient feature selection of traditional ML, with the automatic feature extraction and pattern recognition capabilities of deep learning.