What does this research mean for the field?

While AI-driven predictive models have been developed for various clinical outcomes in urinary stone surgery, their routine clinical adoption is currently limited by inconsistent reporting of calibration and clinical utility, as well as a lack of robust external or temporal validation. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This review aims to assess AI-driven prediction models that forecast outcomes in endourological procedures for urolithiasis.

May 2, 20260 citations

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AI-driven Predictive Models in Endourological Treatment of Urolithiasis: A Systematic Review of Nomogram Applications from EAU Endourology.

NPNatali Rodriguez PeñarandaUniversity of Modena and Reggio Emilia FPFrédéric PanthierCentre National de la Recherche Scientifique SBStefano Di BariUniversity of Modena and Reggio Emilia

Key Points

This review aims to assess AI-driven prediction models that forecast outcomes in endourological procedures for urolithiasis.
Conducted a systematic search across multiple databases including PubMed and EMBASE up to May 2025 following PRISMA guidelines.
Included studies focused on AI prediction models related to outcomes after ureteroscopy, retrograde intrarenal surgery, or percutaneous nephrolithotomy.
Data extraction included modeling approaches, outcomes, and evaluations of discrimination and calibration.
Reviewed 25 studies, primarily retrospective, with stone-free rate as the main outcome measure.
Discrimination was often reported, but calibration and clinical utility were inconsistently documented; internal validation was more common than external validation.
One study introduced a web-based model for implementation.

Abstract

BACKGROUND: Artificial intelligence (AI) is increasingly applied in endourology to enhance surgical planning, risk stratification, and outcome prediction. This systematic review evaluates AI-labelled prediction models predicting treatment-related outcomes in patients with urinary stone disease undergoing endourological procedures. METHODS: A systematic search of PubMed, EMBASE, Scopus, and Web of Science was conducted up to May 2025, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Original studies assessing AI-labelled prediction models for clinical outcomes after ureteroscopy, retrograde intrarenal surgery, or percutaneous nephrolithotomy were included. Data were extracted on modelling approach, outcomes, prevalence, and reporting of discrimination, calibration, and clinical utility, as well as the validation strategy. The protocol was registered in PROSPERO (CRD420251090820). RESULTS: Twenty-five studies were included, mostly retrospective and from Asian or European centres. Stone-free rate was the most frequent outcome. Infectious outcomes included febrile urinary tract infection, systemic inflammatory response syndrome, sepsis, fungal infection, and pyonephrosis, while other outcomes comprised operative time, radiation exposure, impacted stones, stone composition, transfusion, stenting, and broader perioperative outcomes. Across outcomes, discrimination was commonly reported, whereas calibration and formal assessment of clinical utility were less consistently reported; validation was predominantly internal, with limited external or temporal validation. One study reported a web-based implementation. CONCLUSIONS: AI-labelled prediction models have been developed for multiple clinically relevant outcomes in urinary stone surgery. However, calibration, clinical utility, and robust validation remain inconsistently reported. Future work should prioritise transparent reporting of these elements, alongside multicentre external or temporal validation and more standardised outcome definitions, before routine clinical adoption.

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AI-driven Predictive Models in Endourological Treatment of Urolithiasis: A Systematic Review of Nomogram Applications from EAU Endourology.

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