## 4 Results
### Categorization of the Results
#### 4.3.2 Type of Research
For example, patient data may be recorded under both stress and rest conditions or only at a single time point. Additionally, DICOM files can contain varying numbers of slices, and researchers may choose to use all or only a subset of them. The final content and structure of the data are influenced by multiple factors, including clinical protocols, imaging equipment, and research objectives. Although the final datasets are primarily composed of reconstructed nuclear images and/or calculated radiomic features, given the differences mentioned above, datasets cannot be directly compared at a broad review level. Studies using reconstructed images often apply **preprocessing techniques**, though some omit them entirely or do not specify their preprocessing steps. The most common preprocessing techniques include normalization (e.g., zero-centering or min-max scaling), cropping \begin{table}
\begin{tabular}{l l l l} \hline
**Data** & **Name** & **County** & **N** \\ \hline \multirow{4}{*}{Open dataset (N = 18)} & PPMI & Multicenter & 14 \\  & UCI SPECT Heart & USA & 2 \\  & SPECTMPISeg & China & 1 \\  & SPECT MPI & Turkey & 1 \\ \hline \multirow{6}{*}{In house (N = 38)} & China & 9 \\  & Japan & 5 \\  & Taiwan & 5 \\  & Greece & 3 \\  & Iran & 3 \\  & South Korea & 2 \\  & Vietnam & 2 \\  & Algeria & 1 \\  & Egypt & 1 \\  & Germany & 1 \\  & USA & 1 \\  & Multicenter & 1 \\  & Not reported & 4 \\ \hline \multicolumn{2}{l}{Software phantoms} & \multicolumn{2}{l}{1} \\ \hline None & \multicolumn{2}{l}{1} \\ \hline \end{tabular}
\end{table} 
Table 2: Data sources. or extracting the region of interest, and additional steps such as resizing, standardization, or noise reduction. Some researchers also introduced novel preprocessing strategies. For example, [PERSON] et al. [47] converted original 3D stereo images into 2D polar images, while [PERSON] et al. [33] merged two cubic images into a dual channel representation, differentiating between stress and rest conditions. Despite the widespread use of preprocessing, **data augmentation** is reported in only 19 studies, with common techniques including rotation, flipping, and translation. Some studies apply discrete transformations, such as 90-degree rotations or axis-aligned flips, while others introduce variability through random Gaussian noise, blurring, or affine transformations with dynamically adjusted parameters. Two studies specifically use augmentation for numerical data rather than reconstructed images, applying the synthetic minority oversampling technique (SMOTE) method to balance datasets [29; 47]. In renal imaging, since each patient has two kidneys, some authors treat them as separate images, effectively increasing the number of records used for model training and testing [60; 61]. As a result, the number of dataset records in these studies often exceeds the actual number of patients. Lastly, it should be noted that some authors use multimodal SPECT/CT datasets but employ only SPECT data for model implementation. When CT data are included, it serves primarily as ground truth for attenuation correction [66; 68; 69; 75] or automated localization [64].