Enhancing urothelial carcinoma diagnosis with artificial intelligence–integrated urine cytology: Biopsy‐validated accuracy and efficiency gain

Abstract Background Urine cytology is a noninvasive tool for detecting urothelial carcinoma, yet its performance depends heavily on expert cytologists and time‐intensive glass slide review. Artificial intelligence (AI)–assisted digital cytology has emerged as a potential solution to improve diagnostic sensitivity and workflow efficiency. This retrospective study aimed to evaluate the diagnostic performance and workflow impact of AIxURO, an AI‐assisted digital urine cytology system, in a high‐volume US medical center. Methods Two hundred ThinPrep cytology slides were digitized with two distinct customized scanners integrated into the AIxURO imaging system (AIS‐1 and AIS‐2). Three reviewers independently assessed each case across three diagnostic modalities: microscopy, AIxURO–AIS‐1, and AIxURO–AIS‐2. Performance for binary bladder cancer detection was compared against expert consensus cytology diagnoses; review time, biopsy correlation, and performance in patients presenting with hematuria were also evaluated. Results AIxURO (AIS‐1 and AIS‐2) improved diagnostic sensitivity (85.0% and 88.3%) and negative predictive value (NPV) (85.1% and 87.6%) relative to microscopy (79.3% and 82.0%), whereas accuracy remained comparable across modalities. These sensitivity gains were accompanied by lower specificity (85.7% and 82.3% vs. 94.3%) and positive predictive value (PPV) (85.6% and 83.3% vs. 93.3%) compared with microscopy. Both AI modalities reduced classifying atypical urothelial cells and above cases as negative for high‐grade urothelial carcinoma cases, and shortened median review time by 66%–78% ( p < .0001). Among the 200 cases, 98 (49%) had biopsy confirmation, in which AIxURO showed higher sensitivity and NPV than microscopy. In 16 hematuria cases with biopsy correlation, AI‐assisted screening achieved superior diagnostic performance. Conclusions AIxURO enhances sensitivity for detecting urothelial carcinoma and markedly improves screening efficiency, with a tradeoff of reduced specificity/PPV compared with microscopy.