The diagnostic performance of machine learning based detection of urinary tract stones: a systematic review and...

Urolithiasis is a prevalent urological condition, and Non-Contrast Computed Tomography (NCCT) is the gold standard for diagnosis. In recent years, there has been growing interest in investigating machine learning (ML)- based detection of urolithiasis and the wider potential of AI in urology.

To synthesise the diagnostic accuracy of ML-based UTS detection on NCCT and in externally validated cohorts.

We performed a systematic review and bivariate meta-analysis of studies evaluating ML for detecting urinary stones. We used QUADAS-2 to assess the risk of bias. Subgroup analyses examined performance by model type, classification task, stone site, dataset source, and CT orientation. Bivariate meta-regression was performed to further explore heterogeneity. Publication bias was assessed using Deeks' test. The study was prospectively registered in Prospero (CRD42024542409).

Forty-five studies were included qualitatively. 24 studies (49,277 test images) provided extractable 2 × 2 data for meta-analysis. For NCCT (10 studies), pooled sensitivity was 96% (95% CI 92-98%) and pooled specificity was 98% (95% CI 97-99%). In externally validated NCCT cohorts (4 studies; 1,056 images), pooled sensitivity was 95% (95% CI 92-97%) and pooled specificity was 96% (95% CI 70-100%). Subgroup performance remained high, but heterogeneity persisted; meta-regression found stone site contributed to variability (p = 0.014), while other moderators were not significant. Deeks' test showed no small-study effects (p = 0.571).

ML models show high image-level diagnostic performance for stone detection on NCCT and may support radiologists as decision support tools. Translation is limited by heterogeneity and limited external validation. Future studies should move beyond detection-alone tasks towards clinically meaningful outputs that are actionable for radiologists and downstream clinicians, including urologists and nephrologists.