Flow prediction in sound-based uroflowmetry.

Sound-based uroflowmetry (SU) offers a non-invasive alternative to traditional uroflowmetry (UF) for evaluating lower urinary tract dysfunctions, enabling home-based testing and reducing the need for clinic visits. This study compares SU and UF in estimating urine flow rate and voided volume in 50 male volunteers (aged 18-60), with UF results from a Minze uroflowmeter as the reference standard. Audio signals recorded during voiding were segmented and machine learning algorithms (gradient boosting, random forest, and support vector machine) estimated flow parameters from three devices: Ultramic384k, Mi A1 smartphone, and Oppo smartwatch.

Classification of the Physical Surface in Sound-based Uroflowmetry.

This work constitutes a first approach to automatically classify the urination medium for non-invasive sound based uroflowmetry tests. Often the voiding flow impacts the toilet wall (often made of ceramic) instead of the water. This causes a reduction in the amplitude of the recorded audio signal, and thus a reduction in the amplitude of the extracted envelope.

Privacy-Preserving Automatic Collection of Acoustic Voiding Events.

Uroflowmetry is a non-invasive diagnostic test used to evaluate the function of the urinary tract. Despite its benefits, it has two main limitations: high intra-subject variability of flow parameters and the requirement for patients to urinate on demand. To overcome these limitations, we have developed a low-cost ultrasonic platform that utilizes machine learning (ML) models to automatically detect and record natural in-home voiding events, without any need for user intervention.