A validated custom pipeline for three-dimensional kidney stone renderings tocreate an open access repository.

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.

Urolithiasis. 2026 Jun 18*** epublish ***

Kimberly Cortés Pérez, Joao G Porto, Luca Civetta, Archan Khandekar, Robert Marcovich, Hemendra N Shah, Sarvesh Saini, Julio Ojalvo, Ubbo Visser, Jonathan E Katz

Desai Sethi Urology Institute, University of Miami, Miller School of Medicine, Miami, FL, USA., Department of Biomechanical Engineering, University of Michigan, Ann Arbor, MI, USA., Department of Computer Science, University of Miami, Miller School of Medicine, Miami, FL, USA., Desai Sethi Urology Institute, University of Miami, Miller School of Medicine, Miami, FL, USA. .