Can AI and predictive models accurately predict stone-free status? a systematic review and meta-analysis.

Abstract

Objectives: The emergence of artificial intelligence (AI) and predictive modeling offers prospects for clinical, anatomical, and imaging factor combination, like radiomics, to help with stone-free status (SFS) estimation and peroperative decision-making. The goal of this study was, therefore, to define the present performance range, determine sources of heterogeneity, and determine methodological practices permitting reliable implementation by varied circumstances.

Methods: We searched six bibliographic databases through 19 September 2025. Studies deriving or validating AI/predictive models for SFS after ureteroscopy were eligible. Independent dual screening, duplicate data extraction, and risk-of-bias consideration using QUADAS-AI were conducted.

Results: Five retrospective cohorts were included. Modeling approaches encompassed multivariable logistic regression, regularized/radiomics pipelines, gradient boosting, and ensembles. SFS definitions ranged from <2 mm residual (day-1 to 3 months) to ≤5 mm residual (1 month), determined by plain radiography, ultrasound, and/or CT. The pooled ratio-scale effect for stone size per 1 mm increase was 1.26 (95% CI 0.91-1.76; τ² ≈ 0.055; Q = 18.52; I² = 94.6%; prediction interval 0.03-49.45). Hydronephrosis (moderate-severe vs. mild/none) showed a pooled RR 2.72 (95% CI 0.96-7.72; τ² ≈ 0.821; Q = 65.40; I² = 96.9%; prediction interval 0.03-249.87). As continuous contrasts, stone size was larger in the non-stone-free group (SMD 1.36, 95% CI 0.85-1.86; τ² ≈ 0.096; I² = 72.9%; prediction interval -3.77 to 6.48), and HU was higher (SMD 0.64, 95% CI 0.39-0.90; τ² ≈ 0; Q = 0.73; I² = 0%; prediction interval -0.99 to 2.27).

Conclusions: Across studies evaluating AI and predictive models for ureteroscopy, discrimination was generally acceptable to excellent, and performance appeared highest in models integrating radiomics with anatomic/clinical descriptors. However, the degree of between-study heterogeneity (population mix, outcome definitions, imaging protocols, thresholds, and follow-up windows) was sufficiently large that pooled quantitative estimates should be considered clinically uninterpretable.