To evaluate the potential of artificial intelligence (AI) and machine learning (ML) to objectively classify uroflowmetry curves, aiming to reduce variability and enhance diagnostic accuracy.

This cross-sectional study analyzed 586 uroflowmetry curves from children aged 5-17 years, excluding tests with voided volumes below 50% of expected bladder capacity. Curves were standardized per ICS recommendations (1 mm = 1 s on x-axis, 1 mL/s on y-axis) and classified by three pediatric urology specialists into bell, tower, plateau, staccato, or interrupted patterns per ICCS definitions. The YOLOv5×6 algorithm was trained on 85% of the dataset, with 15% for validation, using a high-performance system. Performance was assessed via accuracy, precision, recall, F1-score, and mean Average Precision (mAP).

Inter-rater agreement was high (Fleiss’ kappa: 0.948 ± 0.007). The AI model achieved 85.8% accuracy, with 96% success in identifying bell-shaped curves. Plateau curves showed the highest precision (1.00), while staccato had the lowest (0.64). mAP@0.5 reached ∼90%, stabilizing after 50 epochs.

AI-driven classification of uroflowmetry curves offers high accuracy and reduces observer variability. Future work should focus on multicenter datasets and standardized reporting to enhance clinical utility and integration into uroflowmetry devices for real-time analysis.