Interpretable machine learning for urothelial cells classification and risk scoring in urine cytology

< 0.0001). Through alignment with established cytologic criteria, this feature-based framework provides a transparent and quantitative approach that may improve consistency, efficiency, and interpretability in digital urinary cytology.