## 3 Results
### DL Methods
This three-dimensional context is particularly important for NPH diagnosis, where the spatial relationship between ventricles, subarachnoid spaces, and surrounding brain tissue is diagnostically relevant. The 3D convolution operations extract features that represent spatial patterns across adjacent voxels in all directions, enabling more comprehensive analysis of subtle morphological changes that might be missed in slice-by-slice 2D analysis. This advantage was demonstrated in [44] study, where 3D DCNN captured complex relationships between CSF volume in the subarachnoid space relative to ventricles (77.04% higher), a volumetric biomarker that would be difficult to quantify accurately with 2D approaches. In another study they analyzed the relationship between CSF variation and hydrocephalus in patients with different cerebral injuries [44]. They collected MRI scans from brain damaged patients and adopted a DCNN model to preprocess image features. They then conducted offline training and online reconstruction after constructing the model. Four databases were selected for DL analysis using principal component analysis and 3D scale-invariant feature transformation. The results showed that the weighted histogram of gradient orientation descriptor and ROC-AUC score were the highest. Scale-invariant feature transformation, principal component analysis and weighted histogram of gradient orientation achieved sensitivities of 87.5%, 88.2%, and 90.1%, respectively, while specificities were 91.8%, 90.1%, and 94.2%, respectively. The weighted histogram of gradient orientation method also had the highest correct rate at 92.4%. The study found that the CSF volume in the subarachnoid space was 77.04% higher than that in the ventricles. This study presented a DCNN approach to analyzing CSF variation in patients with brain injuries and hydrocephalus. The researchers utilized various image processing and classification techniques, finding that the weighted histogram of gradient orientation descriptor achieved the highest performance in differentiating subarachnoid fluid from ventricular fluid. The results suggest the effectiveness of DL algorithms like DCNNs for diagnosing hydrocephalus using MRI. Another work using DL methods did not use DCNN; instead, they used transfer learning, an approach that allows to repurpose pre-trained DL models. An study used brain CT and MRI images from 143 NPH patients, which were manually labeled with ventricular volume and intracranial volume [44]. A multilabel segmentation model handled both thick-slice CT and MRI images, addressing domain shift caused by distinctive image distributions. The encoder used a pre-trained ResNet34 architecture pretrained on ImageNet to extract features. During training, the objective function incorporated cross-entropy loss for thick-slice images and entropy loss for thin-slice images. Integrating the pre-trained ResNet34 encoder allowed the network to effectively learn textual and shape priors during initial training. The results showed the suitability of the AI-based method for accurate automatic measurement of ventricular volume in NPH patients. Statistical evaluations achieved Dice similarity coefficient of 0.95 for ventricular volume, intraclass correlation of 0.99, and Pearson correlation of 0.99. For intracranial volume, Dice similarity coefficient was 0.96, intraclass correlation was 0.99, and Pearson correlation was 0.99. Bland-Altman analysis indicated minimal bias between automatic and manual segmentations. These findings highlight the potential of ResNet-based DL approaches as an alternative analysis method for NPH, providing reliable measurements for clinical diagnosis and treatment. In a work researchers proposed using phase-contrast MRI and CSF flow quantification across the cerebellar aqueduct [43]. Two radiologists manually performed region of interest studies.