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Related Experiment Video

Updated: Jun 4, 2025

Ultrasonography of the Adult Male Urinary Tract for Urinary Functional Testing
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Flow prediction in sound-based uroflowmetry.

Marcos Lazaro Alvarez1, Laura Arjona2, Mario Jojoa-Acosta3

  • 1Faculty of Engineering, University of Deusto, 48007, Bilbao, Spain. alvarez.marcoslazaro@deusto.es.

Scientific Reports
|January 3, 2025
PubMed
Summary

Sound-based uroflowmetry (SU) provides a reliable, non-invasive method for assessing urinary function at home. This study confirms SU accurately estimates urine flow rate and voided volume, offering a convenient alternative to traditional uroflowmetry.

Keywords:
Acoustic voiding signalsFlow predictionMachine learningSound-based uroflowmetry

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Area of Science:

  • Biomedical Engineering
  • Urology
  • Signal Processing

Background:

  • Traditional uroflowmetry (UF) requires clinic visits for evaluating lower urinary tract dysfunctions.
  • Non-invasive methods like sound-based uroflowmetry (SU) offer potential for home-based monitoring.
  • Accurate estimation of urine flow rate and voided volume is crucial for diagnosing urinary issues.

Purpose of the Study:

  • To compare the accuracy of sound-based uroflowmetry (SU) with traditional uroflowmetry (UF) in estimating urine flow rate and voided volume.
  • To evaluate the effectiveness of machine learning algorithms in analyzing audio signals for uroflowmetry parameters.
  • To determine optimal parameters for SU, including device, frequency range, and segment size.

Main Methods:

  • Fifty male volunteers (aged 18-60) participated, with UF using a Minze uroflowmeter as the reference.
  • Audio signals during voiding were recorded using Ultramic384k, smartphone, and smartwatch.
  • Machine learning models (gradient boosting, random forest, SVM) estimated flow parameters from segmented audio data.

Main Results:

  • Mean absolute errors for flow rate estimation were low (2.5-2.9 ml/s) with high R² values (79-84%).
  • The 0-8 kHz frequency band captured 83% of significant audio components, indicating higher sampling rates are not essential.
  • Optimal segment size was 1000 ms; Lin's concordance coefficients for smartwatch data were 0.9 (flow rate) and 0.85 (volume).

Conclusions:

  • Sound-based uroflowmetry is a reliable and cost-effective alternative to traditional uroflowmetry.
  • SU enables convenient home-based testing for lower urinary tract dysfunction evaluation.
  • Machine learning analysis of audio signals effectively estimates key uroflowmetry parameters.