## 4 Results
### Categorization of the Results
#### 4.3.3 Research Methods
One paper approaches the problem of the necessity of normalization in SPECT image utilization from a formal approach, rather than as an application approach, providing proof that self-normalization can be effective, eliminating the need for the normalization of regions [72]. Also, for feature selection, [PERSON] et al. explored the binary approach to quantum-based avian navigation optimizer method [65]. Many publications lack clear notation regarding the **technological tools** used for model development. Out of 58 publications, only 26 specify the programming language employed for implementation: Python (n = 22), R (n = 3) and Matlab Script (n = 1). Some mention frameworks such as TensorFlow, Keras, or Matlab but without providing specific details on the programming language or specific libraries used (n = 16). Additionally, 16 publications do not disclose the technology utilized for model development. Among the \begin{table}
\begin{tabular}{l l l} \hline \hline
**Synthetic data** & & \\ Planar & - & \\ SPECT & U-net & [77] \\  & GAN & [67; 71] \\
**Reconstruction** & & \\ Planar & DnCNN, Win5 RB, ResUnet & [74] \\ SPECT & Custom Transformer-based Dual-domain Network & [70] \\  & Custom Residual Network & [76] \\
**Attenuation generation or enhancement** & & \\ Planar & - & \\ SPECT & cGAN (U-net + PatchGAN) & [68] \\  & ResNet, U-net & [69] \\  & U-net & [75] \\
**Normalization** & & \\ Planar & - & \\ SPECT & Self-normalization via a projection of voxels & [72] \\  & U-net & [73] \\
**Feature selection** & & \\ Planar & - & \\ SPECT & Quantum-Based Avian Navigation Optimizer & [65] \\
**Mixed** & & \\ Planar & - & \\ SPECT & Custom Cross-domain Iterative Network (U-net) & [66] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Research tasks and methods: **general imaging methods** (\(N=13\)). studies that report how image annotation is performed, all state that LabelMe software was used for this task [85]. Considering **interpretability**, the most popular approach used is the grad-CAM method [22; 28; 33; 35]. Other methods employed include local interpretable model-agnostic explanations (LIME) [39], layer-wise relevance propagation [40], and class activation mapping (CAM) [32]. One study explored several methods for interpretable AI, including saliency maps, guided backpropagation, grad-CAM, guided grad-CAM, deep learning important features (DeepLIFT), and shapley additive explanations (SHAP) [48]. Finally, regarding **validation**, 27 publications (46.5%) did not report any validation processes during model development, making it unclear whether the authors employed any validation strategies for model training and/or testing. Among the publications that describe their experimental design, k-fold cross-validation (with k = 1, 5, 10, 25, and 100) is commonly used during hyperparameter tuning and model performance assessment. Most of these studies use a fixed test set, though some authors apply nested cross-validation for more robust evaluation [30; 47; 48; 50; 51; 57]. Only a few studies report whether stratification was considered during the data split, while external validation is mentioned in only three studies [35; 64; 69].