Evaluating artificial intelligence–enhanced digital urine cytology for bladder cancer diagnosis

Abstract

BackgroundThis study evaluated the diagnostic effectiveness of the AIxURO platform, an artificial intelligence–based tool, to support urine cytology for bladder cancer management, which typically requires experienced cytopathologists and substantial diagnosis time.MethodsOne cytopathologist and two cytotechnologists reviewed 116 urine cytology slides and corresponding whole‐slide images (WSIs) from urology patients. They used three diagnostic modalities: microscopy, WSI review, and AIxURO, per The Paris System for Reporting Urinary Cytology (TPS) criteria. Performance metrics, including TPS‐guided and binary diagnosis, inter‐ and intraobserver agreement, and screening time, were compared across all methods and reviewers.ResultsAIxURO improved diagnostic accuracy by increasing sensitivity (from 25.0%–30.6% to 63.9%), positive predictive value (PPV; from 21.6%–24.3% to 31.1%), and negative predictive value (NPV; from 91.3%–91.6% to 95.3%) for atypical urothelial cell (AUC) cases. For suspicious for high‐grade urothelial carcinoma (SHGUC) cases, it improved sensitivity (from 15.2%–27.3% to 33.3%), PPV (from 31.3%–47.4% to 61.1%), and NPV (from 91.6%–92.7% to 93.3%). Binary diagnoses exhibited an improvement in sensitivity (from 77.8%–82.2% to 90.0%) and NPV (from 91.7%–93.4% to 95.8%). Interobserver agreement across all methods showed moderate consistency (κ = 0.57–0.61), with the cytopathologist demonstrating higher intraobserver agreement than the two cytotechnologists across the methods (κ = 0.75–0.88). AIxURO significantly reduced screening time by 52.3%–83.2% from microscopy and 43.6%–86.7% from WSI review across all reviewers. Screening‐positive (AUC+) cases required more time than negative cases across all methods and reviewers.ConclusionsAIxURO demonstrates the potential to improve both sensitivity and efficiency in bladder cancer diagnostics via urine cytology. Its integration into the cytopathological screening workflow could markedly decrease screening times, which would improve overall diagnostic processes.