###### Abstract  
Chronic kidney disease (CKD) is a progressive condition affecting over 800 million people worldwide (more than 10% of the general population) and is a major contributor to morbidity and mortality. Early detection is critical, yet current diagnostic methods (e.g., computed tomography or magnetic resonance imaging) do not focus on functional impairments, which begin long before structural damage becomes evident, limiting timely and accurate assessment. Nuclear medicine imaging, particularly planar scintigraphy and single-photon emission computed tomography (SPECT), offers a non-invasive evaluation of renal function, but its clinical use is hindered by interpretive complexity and variability. Machine learning (ML) holds promise for enhancing image analysis and supporting early CKD diagnosis. This study presents a scoping review of ML applications in CKD detection and monitoring using renal scintigraphy. Following the PRISMA framework, the literature was systematically identified and screened in two phases: one targeting ML methods applied specifically to renal scintigraphy, and another encompassing broader ML use in scintigraphic imaging. The results reveal a notable lack of studies integrating advanced ML techniques, especially deep learning, with renal scintigraphy, despite their potential. Key challenges include limited annotated datasets, inconsistent imaging protocols, and insufficient validation. This review synthesizes current trends, identifies methodological gaps, and highlights opportunities for developing reliable, interpretable ML tools to improve nuclear imaging-based diagnostics and support personalized management of CKD. artificial intelligence; convolutional neural networks; deep learning; nuclear imaging; nuclear medicine; renal imaging +
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making, facilitate learning and decision-making skills in medical students, and contribute significantly to personalized care in healthcare [4]. CKD is marked by a gradual and irreversible decline in renal function, along with reduced glomerular filtration rate (GFR) and gradual structural damage to the kidneys, which accelerates over time. These changes impair kidneys to filter blood, ultimately leading to end-stage renal disease (ESRD) when dialysis or a kidney transplant are becoming the only viable options to sustain life [4]. Therefore, the accurate assessment of renal function and kidney structure is crucial for detecting and monitoring CKD. Many advanced imaging techniques, such as magnetic resonance imaging (MRI), ultrasound elastography (UE), and computed tomography (CT) are used today in CKD diagnosis and prognosis; however, nuclear techniques like PLANAR scintigraphy, PET (positron emission tomography), and SPECT (single-photon emission computed tomography), offer unique advantages that set it apart from other imaging modalities [5]. As CKD begins with functional impairments before structural damage becomes evident, structural imaging techniques like CT or MRI may not reveal abnormalities until substantial tissue damage has occurred. Thus, scintigraphy provides real-time functional and molecular data which allows for detecting early functional changes and disease progression, enabling timely diagnosis and prevention treatments. Both planar and SPECT images are visual representations of quantitative data that represent radiotracer uptake where each pixel (in planar) or voxel (in SPECT) encodes a value proportional to tracer accumulation. These values are visualized as grayscale or color images using different reconstruction algorithms to enable clinical interpretation. For example, Figure 1 shows multiple visualizations for one dynamic planar renal scan [6]. Figure 2 shows a comparison of planar scans and coronal-simulated planar from SPECT scans where SPECT is visualized as a grayscale image to enable comparison with planar scans [7].