Advancements in artificial intelligence for detecting urinary tract stones: A systematic review of imaging-based approaches

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Abstract
Objective: Kidney stone disease is a growing global health problem, with increasing diagnostic challenges due to heterogeneous clinical presentations and imaging demands. This review aims to evaluate the role of artificial intelligence (AI) in imaging-based detection and assessment of urinary tract stones. Methods: A systematic review was conducted by searching PubMed/MEDLINE, EMBASE, Cochrane CENTRAL, and Web of Science from database inception to May 2025 for studies evaluating AI-based imaging in the diagnosis of urinary tract stones. The search followed Preferred Reporting Items for Systematic reviews and Meta-Analyses literature search extension (PRISMA-S) guidance and used a Population, Intervention, Comparison, Outcomes and Study (PICOS) framework to identify eligible adult studies applying AI models to computed tomography (CT), ultrasound, or radiographic imaging, with expert interpretation as reference and diagnostic performance outcomes. Results: From 1142 records identified, 11 studies published between 2017 and 2025 met the inclusion criteria. Most studies used CT imaging and reported high diagnostic performance, with accuracies exceeding 90% in eight studies and reaching over 95% in several deep learning–based approaches, while ultrasound-based AI models also demonstrated strong performance with sensitivities and accuracies above 90%. Conclusion: AI–based imaging demonstrates high diagnostic accuracy for urinary tract stone detection, particularly with CT. AI-enhanced ultrasound represents a practical and cost-effective alternative for implementation in resource-limited and rural settings. Level of evidence: 2

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