Machine Learning for Chronic Kidney Disease Detection from Planar and SPECT Scintigraphy: A Scoping Review

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.