## 2 Background  
Despite the growing adoption of machine learning in medical imaging, its application to renal scintigraphy, particularly in the context of PLANAR and SPECT imaging for CKD detection, remains largely unexplored, while prior research has extensively examined ML applications in nephrology and medical imaging, few studies have focused on the unique advantages of nuclear imaging techniques such as scintigraphy. Given the functional nature of scintigraphy, leveraging ML for CKD diagnosis holds significant promise, yet existing reviews do not comprehensively assess its potential in this area. One notable review by [PERSON] et al. [10] highlights that while AI has been successfully applied to MRI and CT, deep learning models for SPECT and PET imaging are still in early development. The complexity of 3D nuclear imaging data and noise reduction, segmentation, and annotation challenges have hindered progress. Moreover, most AI studies focus on structural imaging rather than functional modalities like scintigraphy, which requires tailored ML approaches. This gap underscores the necessity of this review, which systematically examines the current state of ML applications in PLANAR and SPECT renal scintigraphy for CKD assessment. In contrast, several systematic reviews have explored ML applications in CKD prediction, yet they primarily focus on structured data such as demographic or laboratory data. For example, [PERSON] et al. [3] reviewed 15 studies where ML models were employed for kidney disease prediction, CKD, and Immunoglobulin A Nephropathy, with an emphasis on accuracy metrics and model validation. However, these studies relied exclusively on tabular data, without incorporating imaging data, thereby limiting insights into ML-based image analysis. Despite its relevance, this review does not address ML applications to scintigraphy. Expanding the scope further, [PERSON] et al. [11] conducted a broader review of ML applications in nephrology, analyzing various pathologies, including kidney masses, acute kidney injury, CKD, kidney stones, glomerular disease, and kidney transplants. The study categorized ML tasks into three groups: segmentation of medical images, classification of disease severity, and prediction of treatment outcomes, while it acknowledged the role of ML in CKD diagnosis, most studies reviewed focused on the widely used UCI chronic kidney disease tabular dataset [12], with only one paper addressing CKD prediction using ultrasound images. These findings reinforce the absence of ML research specifically targeting scintigraphy-based CKD diagnosis. A more recent systematic review by [PERSON] et al. [13] examined ML models for CKD prediction, diagnosis, and treatment, identifying key datasets and features used in 68 studies. Despite mentioning five studies that applied ML to medical imaging, it did not analyze them separately. This review highlighted common limitations in existing works, including dataset biases, lack of external validation, and challenges, in generalizing ML models. These insights are relevant for future research applications of ML methods in CKD diagnosis but do not address the specific potential of scintigraphy imaging. Deep learning applications in kidney disease imaging have also been reviewed by [PERSON] et al. [14], with a focus on segmentation and volumetric evaluation techniques for renal tumors, renal calculi, and other pathologies, while CKD was considered in this review, all related works focused on ultrasound and CT imaging, omitting nuclear imaging modalities. Moreover, [PERSON] et al. [8] reviewed medical imaging techniques for CKD monitoring, emphasizing MRI-based renal function evaluation. The study categorized ML applications into three main areas: texture analysis and traditional ML, deep learning for renal function evaluation, and automatic renal segmentation. Notably, only two studies [15,16] investigated scintigraphy imaging, both employing traditional ML techniques rather than deeplearning.