A validated custom pipeline for three-dimensional kidney stone renderings to create an open access repository

Clinical Snapshot

Key Findings

• P Value: p=0.026 for difference in geometric fidelity across stone compositions; geometry vs texture score difference not significant (p not specified)

• Effect Size: Mean fidelity scores ranged from 3.0–3.3 (struvite/MAPH/CA) to 3.8–3.9 (UA and COM) on a 5-point Likert scale; statistically significant difference in geometric fidelity across compositions (χ²=9.30)

• Primary Outcome: Successful 3D rendering rates by stone composition: COM 8/11 (73%), UA 5/5 (100%), MAPH/CA 2/2 (100%), CHPD 3/3 (100%), cystine 0/4 (0%)

• Nnt Or Sensitivity: Inter-rater reliability: individual ICC=0.10 (poor); aggregated mean ICC=0.67 (moderate). No sensitivity/specificity or NNT applicable to this validation study design.

• Confidence Interval: Not reported for any outcome

Clinical Application

The pipeline uses commercially accessible 4K cameras and a custom rotating stage, suggesting moderate reproducibility in well-resourced academic urology departments. The open-access GitHub repository lowers the barrier to adoption. However, technical expertise in photogrammetry software is required, and the cystine stone limitation restricts completeness of the model library. In Australia, kidney stone disease affects approximately 1 in 10 adults, with COM and UA stones comprising the majority of cases — compositional types for which this pipeline performs best. Australian urology training programs (USANZ) increasingly incorporate simulation-based education, and an open-access 3D stone repository could supplement existing simulation curricula. The pipeline is not a therapeutic or diagnostic device and therefore does not require TGA registration. PBS implications are not applicable. RACGP guidelines do not directly address 3D simulation tools, but the broader movement toward procedural simulation in specialist training is well-supported. Australian academic urology centres with photogrammetry capability could feasibly replicate and expand this repository with locally sourced stone specimens. Endourologists, urology trainees, and researchers requiring 3D kidney stone models for simulation-based training, surgical planning education, or computer vision/machine learning research in urolithiasis

Abstract

Three-dimensional (3D) rendering of urologic pathology plays an important role in simulation-based education, surgical training, and computer vision research; however, a standardized, open-access repository of high-fidelity kidney stone models stratified by chemical composition is lacking. We developed and validated a reproducible photogrammetry-based pipeline to generate realistic 3D kidney stone renderings. Chemically characterized human stones composed of calcium oxalate monohydrate (COM) (n = 11), uric acid (UA) (n = 5), cystine (n = 4), magnesium ammonium phosphate hexahydrate/carbonate apatite (MAPH/CA) (n = 2), and calcium hydrogen phosphate dihydrate (CHPD) (n = 3) were photographed using a custom-built rotating stage and dual fixed 4 K cameras. Rendered models were sent to 25 endourologists using a 5-point Likert-scale survey assessing geometric and surface texture fidelity. Successful 3D renderings were obtained for 8/11 COM stones, 5/5 UA stones, 2/2 MAPH/CA fragments, and 3/3 CHPD fragments, while all cystine stones failed to render. Across stone types, mean fidelity scores were highest for UA and COM stones (mean 3.8-3.9), intermediate for calcium phosphate stones (mean 3.6-3.8), and lowest for struvite stones (mean 3.0-3.3). Geometry scores were higher than texture scores overall, though this difference was not significant. Significant differences in geometric fidelity were observed across stone compositions (χ² = 9.30, p = 0.026). Inter-rater reliability was poor for individual evaluators (ICC = 0.10) but moderate for aggregated mean ratings (ICC = 0.67). This validated workflow enables the creation of generally realistic, open-access 3D kidney stone models (github.com/uro-glidar/3d-rendering-diverse-stones) for simulation, education, and future machine learning applications in endourology.

References

1. 1.Pérez, K. C., Porto, J. G., Civetta, L., Khandekar, A., Marcovich, R., Shah, H. N., Saini, S., Ojalvo, J., Visser, U., & Katz, J. E. (2026). A validated custom pipeline for three-dimensional kidney stone renderings to create an open access repository. Urolithiasis. https://doi.org/10.1016/j.euros.2021.09.011