## 2 Materials and Methods
### Methods  
In this study, we searched three major bibliographic databases: Web of Science (WoS), Scopus, and dblp. They were selected for their wide coverage of studies and rich citation data that supports in-depth bibliometric analysis of DTs (Table 1). We applied filters to the searches to focus on the relevant literature, narrowing the scope to original articles in English. After filtering, a manual review of each article was conducted to ensure that it met the inclusion criteria, which helped determine the final sample size. Three reviewers were involved in the process, and inclusion/exclusion decisions were made independently. Any discrepancies were resolved by consensus among at least two of the three reviewers. Key features of the dataset were then analyzed, including known authors, research groups/institutions, countries, topic clusters, and emerging trends. This allowed for mapping the evolution of key terminology and major research developments in the field. Where possible, temporal trends were tracked to monitor changes in research coverage over time, and publications were grouped into topic clusters, revealing relationships between different research areas. This process highlighted key topics and subfields within the research area.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Stage Name** & **Tasks** \\ \hline Defining research objectives & Defining goals of the bibliometric analysis \\ \hline Selecting databases and data collections & Choosing appropriate dataset(s) and developing research \\  & queries according to the study goals \\ \hline Data preprocessing & Cleaning the collected data to remove duplicates and \\  & irrelevant records \\ \hline Bibliometric software selection & Choosing a suitable bibliometric software tool for analysis \\ \hline Data analysis & Description, Author, Journal, Area/Topics, \\  & Institution/Country, etc. \\ \hline Visualization (where possible) & Visualizing the analysis results to present insights \\ \hline Interpretation and discussion & Interpreting findings in the context of the research goals \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bibliometric analysis procedure used in this study (own approach).  
The study was based on selected elements of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines for bibliographic reviews, focusing on aspects such as rationale (item 3), objectives (item 4), eligibility criteria (item 5), information sources (item 6), search strategy (item 7), selection process (item 8), data collection process (item 9), synthesis methods (item 13a), synthesis results (item 20b), and discussion (item 23a). For the bibliometric analysis, tools embedded in the Web of Science (WoS), Scopus, and dblp databases were used. This selected review methodology supports bibliometric and scientometric studies, often allowing for refined categorization by concepts, research areas, authors, documents, and sources.  
The rationale for using a bibliometric approach in this study is its ability to complement the PRISMA framework by providing a quantitative perspective on the structure and evolution of a research field. While PRISMA was primarily designed for systematic reviews and qualitative content analysis, bibliometric analysis enables the identification of publishing trends, influential authors, collaboration networks, and topic clusters. This dual-method approach increases the depth and breadth of the review by combining structured content assessment with insights based on bibliographic metadata. It also helps uncover hidden patterns and gaps in the literature that may not be apparent using qualitative methods alone. Therefore, integrating bibliometrics with PRISMA supports a more comprehensive and transparent synthesis of current knowledge on AI-based DT personalization in engineering.