Letter: Enhancing Stone Volume Estimation Accuracy: Future Directions

Dear Editor,

I recently read with great interest the article titled “Comparison of Measurement Methods for Stone Volume Estimation: An In Vitro Study” published in the Journal of Endourology. ¹ The study provides valuable insights into the accuracy of various stone volume (SV) estimation methods, which is crucial for optimizing the treatment of urolithiasis. However, I would like to offer some perspectives and suggestions for further improvement based on recent advancements in the field.

Firstly, while the study highlights the superior accuracy of 3D segmentation methods (Horos and Kidney Stone Calculator [KSC]) over traditional formula-based methods, it is important to consider the clinical reproducibility of these tools. The KSC, for instance, has demonstrated excellent inter- and intra-observer correlation in clinical settings. ² This suggests that while 3D segmentation methods are accurate, their practical application in diverse clinical environments should be further validated to ensure consistent results across different operators and institutions.

Secondly, the integration of artificial intelligence (AI) and machine learning into SV estimation holds great promise. Recent studies have shown that AI algorithms can achieve automated stone detection and volume quantification with high accuracy. ³ This could potentially overcome the limitations of manual segmentation, such as time consumption and variability. Future research should focus on developing and validating AI-based tools that can seamlessly integrate with existing clinical workflows, thereby enhancing the precision and efficiency of SV estimation.

Moreover, the study’s findings on the limitations of formula-based methods underscore the need for more accurate and reliable alternatives. The comprehensive review by Panthier et al. (2024) emphasizes that automated volume acquisition is more precise and reproducible than calculated volume. ⁴ This supports the notion that moving away from traditional formulas toward advanced imaging techniques and software solutions is essential for better preoperative planning and postoperative outcomes.

Lastly, the study’s in vitro nature limits its direct applicability to clinical practice. Future research should include in vivo studies to validate the findings and assess the clinical significance of different SV estimation methods. Additionally, incorporating patient-specific factors, such as stone composition and surgical approach, could provide a more comprehensive understanding of how SV estimation impacts treatment outcomes. ¹

In conclusion, the study by Pauchard et al. (2025) makes a significant contribution to the field of urolithiasis management. ¹ However, considering the advancements in technology and the potential of AI, there is a need to further refine and validate SV estimation methods to ensure their clinical utility and reproducibility. I believe that continued research in this direction will ultimately lead to improved patient care and better surgical outcomes.