## 2 Methods
### Filtering and Selection  
Data extraction was performed independently by two reviewers ([PERSON] and [PERSON]) using a standardized data extraction form developed a priori. The form was pilot-tested on three randomly selected included studies and refined accordingly. Extracted data were cross-checked for accuracy, with any discrepancies resolved through discussion. When necessary, information was unclear or missing from published reports; we attempted to contact study authors via email with a maximum of three attempts over a four-week period. After obtaining the initial results, we meticulously reviewed each article, discarding those that did not meet the predefined criteria or were unrelated to the specific analysis methods or application areas of interest. The selection criteria used for article filtering included the following:  
* The article must have been published in a scientific database. * The article must have been an empirical study, research article, or case study. * The article must have focused on AI techniques for NPH detection and diagnosis using MRI or CT scans. * The article must have included sufficient methodological details. Articles that were solely bibliographic reviews or that did not meet all the above criteria were excluded from the study. The purpose of these criteria was to ensure that the articles included in the study were high-quality, empirical studies that could provide valuable insights into the use of AI techniques for NPH detection and diagnosis. Excluding articles that were not empirical studies, bibliographic reviews, or case studies, the authors were able to ensure that the articles included in the study were based on real-world data and that they provided concrete examples of how AI techniques could be used to improve NPH detection and diagnosis. We also excluded articles that did not include sufficient methodological details. Methodological details allow other researchers to replicate the results of a given study. Two independent reviewers ([PERSON] and [PERSON]) screened all titles and abstracts identified from the database searches against the predefined eligibility criteria. Full-text articles of potentially relevant studies were then retrieved and independently assessed by the same reviewers. Any discrepancies at either stage were resolved through discussion, with a third reviewer ([PERSON]) consulted when consensus could not be reached. The inter-rater agreement was calculated using [PERSON]'s kappa coefficient, yielding substantial agreement (\(\kappa=0.82\)). Due to the anticipated heterogeneity in AI methodologies, imaging modalities, outcome measures, and study populations, we planned a priori to explore potential sources of heterogeneity through narrative analysis rather than statistical methods. We specifically examined variability across the following domains: (1) AI approach (ML vs. DL vs. hybrid); (2) imaging modality (MRI vs. CT); (3) control group composition (healthy controls vs. disease-specific controls); (4) key radiological features utilized; and (5) sample size and population characteristics. These factors were systematically compared across studies to identify patterns that might explain differences in diagnostic performance. Given the heterogeneity of the included studies and our narrative synthesis approach, formal sensitivity analyses were not performed. However, we conducted a qualitative assessment of the robustness of findings by examining how results varied across studies with different methodological quality characteristics, particularly focusing on validation methods (e.g., studies using external validation vs. cross-validation only) and sample size (larger vs. smaller studies). To assess the certainty of evidence for key outcomes, we adapted the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) approach for diagnostic accuracy studies to the AI context.