Accurate assessment of stone burden is fundamental in urolithiasis, as it directly influences treatment selection and prognostic evaluation. Although maximum stone diameter on non-contrast computed tomography remains the most widely used parameter, it does not adequately reflect the three-dimensional complexity of urinary calculi. This review aimed to summarize the evolution of stone burden assessment from conventional imaging-based methods to software-assisted volumetry and artificial intelligence (AI)-driven tools, with emphasis on their accuracy, reproducibility, and clinical utility.
A narrative review of the literature was performed using PubMed/MEDLINE, Scopus, and Google Scholar for English-language studies published up to March 2026. Original studies, validation studies, technical reports, reviews, and guideline-related papers addressing conventional CT-based measurement, software-assisted volumetry, AI-based stone segmentation, and the clinical significance of stone volume were included. Due to heterogeneity in study design and reported outcomes, the evidence was synthesized narratively.
Maximum stone diameter remains simple and widely available, but it incompletely represents true stone burden, particularly in larger or irregular stones. Formula-based ellipsoid calculations are practical yet show limited accuracy in complex geometries. Semi-automated CT-based segmentation software provides more reliable volumetric assessment, with excellent agreement with reference standards and reduced interobserver variability. AI-based approaches have further improved efficiency by enabling rapid and highly accurate automated stone detection and volume calculation. Across the reviewed literature, stone volume was consistently shown to be more clinically informative than linear dimensions for predicting spontaneous passage, stone-free rates after shockwave lithotripsy and ureteroscopy, and future symptomatic events during surveillance.
Stone volume offers a more accurate and clinically meaningful estimate of stone burden than maximum stone diameter alone. The transition from formula-based methods to software-assisted and AI-driven volumetry represents an important advance in urolithiasis imaging. Wider adoption will depend on standardized imaging protocols, improved software accessibility, and validation of volume-based thresholds for routine clinical practice.
World journal of urology. 2026 May 24*** epublish ***
Stamatios Katsimperis, Lazaros Tzelves, Patrick Juliebø-Jones, Arman Tsaturyan, Eugenio Ventimiglia, Davide Perri, Matthias Boeykens, Victoria Jahrreiss, Stefano Moretto, Mehmet Fatih Sahin, Alberto Olivero, Łukasz Nowak, Amelia Pietropaolo, Begoña Ballesta Martinez, Bhaskar Somani, Andreas Skolarikos
Second Department of Urology, National and Kapodistrian University of Athens, Sismanogleio Hospital, Athens, 15126, Greece. ., Second Department of Urology, National and Kapodistrian University of Athens, Sismanogleio Hospital, Athens, 15126, Greece., EAU Young Academic Urologists (YAU) Urolithiasis and Endourology Working Group, Arnhem, The Netherlands., Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, UK.