Accurate focal‐plane selection is crucial for artificial intelligence assessment of three‐dimensional urine cytology specimens for bladder cancer screening and surveillance

BACKGROUND: Bladder cancer is a common and highly recurrent malignancy requiring lifelong surveillance. Urine cytology serves as a noninvasive triage tool to guide cystoscopy but is limited by variable sensitivity and manual review. Although deep learning enables quantitative cell-level assessment, limited work has examined three-dimensional urine cytology preparations (e.g., SurePath) containing residual cellular fragments, where restricting analysis to a single nominal focal plane may obscure diagnostically relevant features. This study aimed to quantify focal-plane heterogeneity, measure degradation of nuclear-to-cytoplasmic (NC) ratio and nuclear area estimates off plane, and evaluate focal-plane selection algorithms for performance recovery. METHODS: A total of 325 SurePath whole-slide images scanned as 11-plane Z-stacks were analyzed that spanned negative through high-grade urothelial carcinoma cases. A detection model identified cells and clusters across planes, and 343 clusters (2435 urothelial cells) were reannotated at the optimal nuclear and cytoplasmic focal depths. Classical focus metrics and vision-transformer models were evaluated for focal-plane prediction. A U-Net segmentation model generated NC ratios and nuclear areas, and Spearman correlations compared annotated and predicted measurements across optimal, off-plane, and algorithm-selected conditions. RESULTS: Focal-plane prediction accuracy ranged from 42% to 88%, with classical focus metrics outperforming deep-learning approaches. NC ratio correlation was 0.774 at optimal focus and declined progressively off plane (∼0.50 at ±5 planes). Algorithm-selected planes partially recovered performance (up to 0.748). Similar trends were observed for nuclear area estimation. CONCLUSIONS: Accurate focal-plane selection is critical for artificial intelligence-based assessment of three-dimensional urine cytology. Future work will extend this analysis to cluster- and patient-level outcomes in multi-institutional validation studies.