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Updated: Sep 4, 2025

Ultrasonography of the Adult Male Urinary Tract for Urinary Functional Testing
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Development and Validation of a Deep Learning System for Sound-based Prediction of Urinary Flow.

Han Jie Lee1, Edwin Jonathan Aslim1, B T Balamurali2

  • 1Department of Urology, Singapore General Hospital, Singapore.

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|July 14, 2022
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Summary

A new deep learning algorithm, Audioflow, accurately predicts urinary flow rates and identifies abnormal patterns using only sound. This offers a promising, convenient home-based alternative for monitoring lower urinary tract symptoms (LUTS).

Keywords:
Lower urinary tract symptomsMachine learningUroflowmetryVoiding sounds

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Area of Science:

  • Urology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Uroflowmetry is crucial for assessing lower urinary tract symptoms (LUTS) but can be limited by variations in flow rates.
  • Voiding acoustics show potential as a convenient, home-based method for monitoring urinary flow.

Purpose of the Study:

  • To evaluate a deep learning algorithm (Audioflow) that uses sound to predict uroflowmetry parameters.
  • To assess Audioflow's ability to identify abnormal urinary flow patterns.

Main Methods:

  • A prospective study recorded voiding acoustics via smartphone alongside conventional uroflowmetry in 331 male participants.
  • Two deep learning models were developed: one for predicting flow parameters (Qmax, Qave, VV) and another for classifying normal/abnormal flows.

Main Results:

  • Audioflow demonstrated strong agreement with conventional uroflowmetry for maximum flow rate (Qmax), average flow rate (Qave), and voided volume (VV) (correlation coefficients 0.77-0.85).
  • The algorithm achieved 83.8% agreement in identifying abnormal flows, comparable to urology experts, with an AUC of 0.892.

Conclusions:

  • A deep learning algorithm utilizing voiding sounds can accurately predict uroflowmetry parameters and detect abnormal urinary flow.
  • Audioflow presents a promising, simple, home-based alternative for LUTS assessment and monitoring.