From conventional imaging software to artificial intelligence: tools for stone volume assessment in urolithiasis. Α review by the EAU and YAU sections of endourology.

Clinical Snapshot

Key Findings

• P Value: Not applicable - narrative review

• Effect Size: Not quantified - narrative synthesis only

• Primary Outcome: Stone volume provides more accurate clinical assessment than maximum diameter

• Nnt Or Sensitivity: Excellent agreement reported for semi-automated segmentation with reference standards

• Confidence Interval: Not provided

Clinical Application

Implementation dependent on software accessibility, standardized protocols, and validation of volume thresholds Relevant to Australian urologists using CT imaging for stone assessment. May require TGA consideration for AI software implementation and potential PBS implications for advanced imaging protocols All patients with urolithiasis requiring stone burden assessment for treatment planning

Abstract

INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

References

1. 1.Katsimperis, S., Tzelves, L., Juliebø-Jones, P., Tsaturyan, A., Ventimiglia, E., Perri, D., Boeykens, M., Jahrreiss, V., Moretto, S., Sahin, M. F., Olivero, A., Nowak, Ł., Pietropaolo, A., Martinez, B. B., Somani, B., & Skolarikos, A. (2026). From conventional imaging software to artificial intelligence: tools for stone volume assessment in urolithiasis. Α review by the EAU and YAU sections of endourology. World Journal of Urology. https://doi.org/10.1111/bju.16693