## 3 Results
### DL Methods
Their proposed MultiResUNet and UNet DCNN algorithms trained based on the region of interest, representing the first major difference from previous work as this aimed to calculate region of interest for segmentation rather than classify disorders (NPH, AD, healthy control). Another difference was dataset division into 80% training and 20% validation sets. Segmentation was performed by calculating Dice similarity coefficients for manual and DCNN-derived region of interest. MultiResUNet, UNet and the second radiologist (Rater 2) had Dice similarity coefficients of 0.933, 0.928, and 0.867, respectively, with \(p<0.001\) between DCNN and Rater 2. Comparing CSF flow parameters showed excellent intraclass correlation coefficients for MultiResUNet, with the lowest being 0.67. For UNet, lower intraclass correlation coefficients of -0.01 to 0.56 were observed. Only 3/353 (0.8%) studies failed to have appropriate region of interest placed by MultiResUNet, versus 12/353 (3.4%) failed cases for UNet. Several DL algorithms were compared in terms of their performance in classifying between PD and NPH. The ResNet34 transfer learning model achieved a sensitivity of 93.6%, specificity of 94.4%, and ROC-AUC of 93%. Transfer learning using DCNN segmentation achieved a Dice coefficient of 87%, a metric that measures the similarity between sets. The RUDOLPH model obtained a higher Dice coefficient of 0.93. Additionally, the FreeSurfer and MALP-EM model achieved Dice coefficients of 0.72 and 0.90, respectively. The proposed methodology in the study combined Watershed segmentation with a convolutional neural network. This achieved promising results with 97% accuracy, 100% specificity, 96% sensitivity and 95% precision [30]. A work compared multiple segmentation approaches for CT-based analysis, including both traditional ML and DL methods [39]. Their comprehensive performance comparison demonstrated a clear hierarchy: 3D UNet + Probabilistic Maps (ventricles: 85 \(\pm\) 0%, gray-white matter: 94 \(\pm\) 1%, subarachnoid space: 72 \(\pm\) 5%) and standard 3D UNet (ventricles: 85 \(\pm\) 7%, gray-white matter: 93 \(\pm\) 1%, subarachnoid space: 69 \(\pm\) 13%) substantially outperformed RF-based approaches (ventricles: 65 \(\pm\) 12%, gray-white matter: 87 \(\pm\) 2%), particularly for ventricle segmentation. The dramatic performance gap between the best and worst methods (ranging from 85% to 13% for ventricles) emphasizes the critical importance of methodological selection for NPH-related image analysis. The consistently poor performance for subarachnoid space segmentation across all methods (13-72%) highlights a persistent technical challenge requiring further methodological development. While most works used DL to process MRI scans, some studies have utilized CT scans and DL to analyze NPH. References [40] and [27] implemented similar DCNN algorithms using CT. Reference [40] aimed to perform automatic segmentation, while [27] aimed to detect NPH. Reference [40] trained a 3D UNet algorithm to accurately segment lateral ventrics, subarachnoid space, gray-matter, and white-matter. They used FSL FLIRT to extract a probabilistic map of each region and train the DL algorithm using the original images. The results were compared between different ML and DL algorithms. Segmentation results for (1) ventricle, (2) gray-white matter, and (3) subarachnoid space using dice similarity and five-fold cross-validation were: 3D UNet + Probabilistic Maps: 85 \(\pm\) 0%, 94 \(\pm\) 1%, and 72 \(\pm\) 5%, respectively; 3D UNet: 85 \(\pm\) 7%, 93 \(\pm\) 1%, and 69 \(\pm\) 13%, respectively; RF + 3D MCV: 84 \(\pm\) 4%, 87 \(\pm\) 2%, and 35 \(\pm\) 10%, respectively; RF: 65 \(\pm\) 12%, 87 \(\pm\) 2%, and N/A, respectively; RF + 3D morphological geodesic active contours: 25 \(\pm\) 17%, 81 \(\pm\) 2%, and N/A, respectively; 3D MCV: 13 \(\pm\) 14%, 80 \(\pm\) 2%, and N/A, respectively. A team conducted a retrospective study using CT data collected from 1997 to 2020. They implemented a DCNN model to classify NPH patients versus non-NPH patients [27].