## 3 Methodology
### Search Strategy
#### 3.1.2 Broader Screening: Addressing the RQ2 and RQ3  
**Search string.** To identify relevant research, comprehensive search queries were constructed using the following search keywords: SPECT, single-photon emission, scintigraphy, machine learning, artificial intelligence and deep learning. For the techniques used, we have opted for broad keywords, such as \"machine learning\", to avoid limiting the search to known, popular, or expected ML methods. To enhance the specificity of the search and reduce irrelevant results, the keywords were combined using the boolean operators: AND, OR, and NOT. Using NOT operator results from the phase one search, from which connections to renal scintigraphy and CKD has to be omitted. For example, the WoS search query used was: \"TS = ((SPECT OR \"single-photon emission\" OR \"scintigraphy\") AND (\"machine learning\" OR \"artificial intelligence\" OR \"deep learning\") NOT (renal OR kidney OR CKD OR GFR OR \"glomerular filtration rate\" OR \"chronic kidney disease\"))\".  
**Databases.** The search was performed in two databases, WoS and Scopus. The search in PubMed was not performed due to the high number of duplicates for WoS and Scopus results. The last search was performed in January 2025.  
**Inclusion/exclusion criteria.** Since the second phase broadened the scope to examine the overall use of ML methods regardless of the anatomical focus, the type of study/quality of source was considered as an inclusion criterion to limit the search to peer-reviewed journals and conference proceedings, excluding journal reviews, books, book chapters, reports,etc. Moreover, due to rapid changes in emerging and developing fields like ML and AI, we limited the search for work published between 2020 and 2024, to capture the most recent advancements and current state-of-the-art methods. Considering the research domain, for WoS we considered the following categories: multidisciplinary sciences, computer science artificial intelligence, computer science interdisciplinary applications, computer science information systems, computer science theory methods, computer science software engineering, medical informatics, and engineering multidisciplinary; while for Scopus, computer science and multidisciplinary sciences. All returned papers were screened by title and abstract and filtered by the following exclusion criteria:  
1. Not related to any research question;
2. Considers other image modalities;
3. Considers dual or multi-modal approach;
4. Not written in the English language.  
Exclusion criterion one helps filter out papers that are not related to this research domain or to our research questions, such as studies in seismography or agriculture. Exclusion criterion two helps filter out research that is related to other imaging modalities, such as MRI or CT. Exclusion criterion three filters out studies that explore multiple image modalities, separately or via image fusion.  
**Data charting process.** A data charting form was developed as a Google Sheets document, with access granted to all three authors. The charting process was guided by research questions and was iteratively refined during the initial phases of data extraction. Finally, form captured details such as publication year, target organ and disease, study objective, image modality, dataset characteristics, ML approach (e.g., classical ML and deep learning), performance metrics, results, technology details (e.g., programming languages, libraries, and models), preprocessing details, data augmentation details, validation methods, reported problems and limitations. Two reviewers independently charted data from the eligible studies. Discrepancies were resolved through discussion. No automation tools were used for the data extraction.