Artificial intelligence-assisted uroflowmetry and automated evaluation of lower urinary system symptoms

Abstract

Purpose:

This study aimed to develop and validate an AI-assisted framework for the automated evaluation of uroflowmetry data in patients presenting with lower urinary tract symptoms. The primary goal was to overcome the limitations of traditional manual interpretations by leveraging advanced machine learning techniques to achieve higher diagnostic accuracy, objectivity, and clinical applicability in urological assessments.

Materials and methods:

A retrospective analysis was conducted using a large, de-identified dataset comprising uroflowmetry recordings, patient-reported symptom scores, and comprehensive demographic data. The data underwent rigorous preprocessing—including noise reduction, baseline correction, normalization, and feature extraction—with key parameters such as peak flow rate, voided volume, average flow rate, and voiding time being analyzed. Multiple machine learning models—including a deep neural network, support vector machine, and random forest classifier—were developed and validated through cross-validation and extensive statistical testing. Performance metrics such as accuracy, sensitivity, specificity, and area under the ROC curve (AUC-ROC) were calculated, while multivariate regression analyses were performed to explore the relationships between uroflowmetry parameters and symptom severity.

Results:

The AI framework, particularly the deep neural network model, exhibited outstanding diagnostic performance with an accuracy of 92.5%, sensitivity of 90.0%, specificity of 94.0%, and an AUC-ROC of 0.96. Statistical analyses demonstrated significant correlations between key uroflowmetry parameters and clinical symptoms, with lower peak flow rates showing a strong association with increased symptom severity (p < 0.001). These findings confirm that the integration of multi-dimensional data through AI significantly enhances the objectivity and precision of urinary function evaluation compared to conventional methods.

Conclusion:

The study successfully established an AI-assisted diagnostic framework that markedly improves the automated evaluation of uroflowmetry data and lower urinary tract symptoms. This innovative approach offers a robust alternative to traditional diagnostic practices by reducing subjectivity and enhancing diagnostic accuracy, thereby paving the way for more personalized and effective management of urinary disorders.