Evaluation of proudP: A sound-based approach to uroflowmetry.

Abstract

Introduction: We sought to assess the performance of the proudP AI algorithm, integrated into a mobile application, in estimating uroflow curves and parameters using recorded urination sounds.

Methods: A direct comparison was made between the peak flow rate (Qmax), voided volume, and uroflow curves predicted by the proudP algorithm and those obtained through established validation methods. A hardware uroflow simulator replicated uroflow profiles by precisely controlling water flow rates and extracting corresponding sound data. Ten uroflow profiles, representing typical patterns observed in male subjects, were selected. Simulation experiments with proudP were conducted using a standard toilet setup. The uroflow simulator was calibrated to reproduce uroflow profiles, and validation was performed against a Flowmaster uroflowmetry device. Statistical analysis included descriptive summaries, Bland-Altman analysis, and Concordance Correlation Coefficient (CCC) analysis.

Results: The proudP accurately captured various uroflow patterns generated by the simulator, with low standard deviations in Qmax predictions and biases near zero. The SDs of voided volume were slightly larger, primarily due to uroflow patterns with extended voiding times. The study validated the accuracy of proudP against in-office uroflowmetry, demonstrating robustness across different smartphone models.

Conclusions: proudP proved to be as accurate as in-office uroflowmetry in estimating uroflow rate across various patterns. Its convenience in home monitoring offers patients a means to observe their urination patterns accurately, while enabling healthcare professionals to gain detailed insights remotely. proudP emerges as an essential solution for clinical practice and urological research.