## 5 Discussion
### Research Methods  
The primary application of machine learning in SPECT and planar nuclear imaging is **automated classification**. Typically, deep learning models are used when working with image data, while classic machine learning methods are employed with numerical data. Deep learning, particularly CNN-based models, has demonstrated high accuracy in classification tasks. However, many studies also explore innovative approaches that could be considered for future research in CKD diagnosis using SPECT or planar scintigraphy images. For example, [PERSON] et al. [25] used SVM, a traditional classifier, with deep and shallow features extracted from various pre-trained deep neural networks (DNNs) to identify myocardial ischemia and infarction. This deep learning-based model is then compared to a knowledge-based classification model, demonstrating performance comparable to expert analysis. [PERSON] et al. [28] enhanced network performance by integrating feature-fusion and attention modules enhancing the localization abilities of the base VGG19 model. [PERSON] et al. [55] proposed custom VGG7, VGG21, and VGG24 models, derived from the architectures of the official VGG16 and VGG19 models, to examine the impact of network depth on accuracy in thoracic metastasis classification task. Their results indicate that the new models perform comparably to established models, with the custom VGG21 model achieving the best results when using augmented images with normalization. [PERSON] et al. [46] proposed unsupervised clustering methods (i.e., density-based spatial, k-means, and hierarchical clustering) for classifying healthy individuals and PD patients within the SWEDD group (i.e., patients exhibiting Parkinsonian symptoms despite normal dopamine imaging scans). Although clustering is not commonly applied to classification tasks, the authors demonstrated its effectiveness in distinguishing patients when labeled data are limited. Finally, to reduce computational cost, [PERSON] et al. [41] detailed a model selection framework that incorporates diffusion maps and linear discriminant analysis, outperforming deep learning models such as AlexNet, VGG16, and VGG19. Another important application of machine learning in SPECT and planar nuclear imaging is **segmentation**, where ML models are used to delineate organs and extract quantitative metrics or identify specific regions of interest in images, such as lesions. Segmentation studies are less prevalent, which is somewhat expected, as nuclear imaging primarily focuses on functionality rather than morphology. Additionally, nuclear images often have low contrast and high noise, posing significant challenges for precise segmentation. To address these issues, innovative approaches were developed. [PERSON] et al. [21] combined deep learning with a module that incorporates shape priors generated via dynamic programming, enabling automatic left ventricular segmentation, that closely matches manually annotated ground truth images. Similarly, [PERSON] et al. [57] developed a custom CNN model that employs view aggregation (i.e., using a pixel-wise addition operation), to enhance regions with high uptake in planar whole-body scans. Their model performed well against expert annotations, with the authors suggesting further improvements to the process. [PERSON] et al. [53] improved baseline U-Net model performance in lung SPECT scan segmentation adding residual structure and attention mechanism. These segmentation methods, originally developed for other organs, offer promising potential for adaptation in systems that automatically generate renogram curves or GFR calculations, thereby improving and accelerating clinical processes in CKD diagnosis. **General imaging methods** are yet another category where machine learning models play a crucial role in nuclear renal imaging. In this area, deep learning models, both generative and convolutional, are instrumental in image reconstruction and enhancement. One prominent application of machine learning is synthetic data generation.