## 5 Discussion
### Data, Data Sources, and Data Utilization  
Studies employing planar or SPECT imaging techniques offer valuable insights into various strategies for handling such data. One key consideration is whether to use a single slice or multiple slices, either separately or combined into a single image. For example, [PERSON] et al. [44] selected eight slices (slices 37-41 out of 91) to generate three types of 3D inputs. Their methodology involved training different models using three distinct approaches: one based on the individual slices, another on a single image created by merging the slices, and a third where models were trained eight times with the final decision determined by an adaptive boosting algorithm. Although all combinations of models and datasets achieved high accuracy, variations in image organization were observed to have an impact on the performance of different models. [PERSON] et al. [20] also utilized a multi-slice approach for segmentation, noting that a single-image method fails to capture the relationships between adjacent slices. By employing dice coefficients at both the slice and pixel levels, they identified the optimal number of slices for effective segmentation of myocardial SPECT images. While image data forms the basis for most research, as shown in the reviewed studies, other **types of data** should also be considered in future research. That said, tabular (i.e., blood work markers) or textual (i.e., patient's history) data, or data from demographic sources, in addition to the images, can enhance diagnostic accuracy and clinical decision making. For instance, results from deep learning image-based models can be compared with radiomics-based classic machine learning models, such as SVM, which could be a beneficial strategy if an interpretable model is developed [48]. Furthermore, efforts to enhance models by incorporating radiomics or numerical features into pre-trained deep learning image-based models have shown potential to improve overall accuracy [62]. Another approach involves combining deep radiomics features with clinical data to estimate cognitive decline in Parkinson's disease patients [51]. The authors demonstrated that deep radiomics features alone yield superior results compared to using clinical data alone or a combination of both. Another approach explores the integration of deep features with clinical data for estimating cognitive decline in patients with Parkinson's disease [51]. The authors show that deep radiomics features alone outperformed both clinical data alone and the combination of clinical and deep radiomics data. Finally, a noteworthy approach by [PERSON] et al. [70], involved using porcine studies for model training. The use of an animal model represents a divergence from the standard reliance on human clinical data, which can offer potential advantages when acquiring human data are challenging. If this approach is feasible within an institution, researchers might consider adopting it as an alternative or supplementary data source. A critical limitation identified in the reviewed studies is the lack of attention to data heterogeneity and its impact on model generalization. Variability in imaging protocols, such as differences in scanners, radiotracer types, or image resolution, can cause ML models to learn features specific to a particular site (e.g., brightness, noise level, or image textures, as cues for classification or prediction), rather than clinically meaningful patterns related to kidney pathology. This reduces the ability of the model to generalize across institutions and limits its clinical applicability. Moreover, the use of different data sources, such as publicly available datasets and institution-specific (internal) datasets, can affect model performance due to differences in data quality, patient demographics, and labeling practices. Models trained solely on internal datasets may overfit local characteristics and fail to generalize to external cohorts, while open datasets may lack clinical richness or standardization.