## 5 Discussion
### Diagnosis of Renal Pathologies  
The broader screening identified several anatomical organs that have been studied using ML methods applied to planar and SPECT scans. Although CKD diagnosis was not represented in the selected papers, special attention should be given to six studies addressing renal pathologies, in general, as the underlying imaging is the same.  
[PERSON] et al. [61], as mentioned in Section 4.3.3, investigated deep learning methods for differentiation between normal and scarred kidneys in pediatric patients using three different interpretations of data from same SPECT scans. A model that used multiple 2D slices (2.5D model) showed the best results with an accuracy of 92% in the classification of healthy and diseased kidneys.  
[PERSON] et al. [59] automatically delineated renal ROIs and calculated renal function from dynamic renal scintigraphy scans by employing a custom deep learning method based on the Swin-Unet model. The fundamental unit of this model is the swin transformer block which is used for local self-attention. The model achieved very good segmentation performance with an intersection over union (IOU) of 0.83 and a dice similarity coefficient (DSC) of 0.91, compared to references from human experts.  
[PERSON] et al. proposed the Mask R-CNN model that simultaneously performed segmentation of renal images and classification of acute pyelonephritis [60]. The first component of the model is ResNet, the second is the regional proposal network, and the last component is actually the two networks, one fully convolutional for segmentation and fully connected layers for classification. The model achieved an IOU of 90.3 for segmentation and an accuracy of 89% for the classification task. One notable point in this paper is that authors discuss mis-classified cases in order to explain and potentially suggest future improvements which is not commonly reported in similar studies.  
[PERSON] et al. [75] presented a novel approach to generate synthetic attenuation maps directly from kidney SPECT data eliminating the need for accompanying CT scans. The authors employed a custom 3D Unet model, replacing the transpose convolution with nearest neighbor interpolation effectively eliminating checkerboard artifacts commonly seen in up-sampling processes. The proposed approach has the potential to reduce patient radiation exposure by 45.3% to 787.8%.  
The methods previously discussed in Section 4.3.3 for reducing radiation dosage by reconstructing full-acquisition-time images are both performed on the renal SPECT images. [PERSON] et al. [74] evaluated the performance of deep learning models in predicting full-acquisition-time images from pediatric planar images acquired at only one-fifth of the standard acquisition time. Using three deep learning methods, the authors demonstrated that acquisition time reduction in pediatric planar scans is feasible, as the predicted images were comparable in quality to those acquired with full acquisition times. Among the tested models, ResUnet achieved the best performance, with a normalized mean squared error of 0.4%, a peak signal-to-noise ratio of 55.4 dB, and a structural similarity index of 0.997. [PERSON] et al. [76] assessed the reconstruction from half-time acquisition images using deep learning also reporting comparable quality of reconstructed images, achieving an accuracy of 91.7%. Both findings suggest that deep learning techniques can effectively reduce scan times while minimizing patient discomfort, particularly for pediatric patients, without compromising diagnostic reliability.