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Updated: Jan 16, 2026

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Can unsupervised machine learning gain new insights into urodynamic pressure flow pattern analysis?

Wouter van Dort1, Peter F W M Rosier1, Thomas R F van Steenbergen1

  • 1Department of Urology, University Medical Center Utrecht, Utrecht, The Netherlands.

BJU International
|October 1, 2025
PubMed
Summary
This summary is machine-generated.

Unsupervised machine learning (UML) identified distinct pressure flow study (PFS) curve patterns in men, revealing subtypes associated with detrusor voiding contraction (DVC) strength and prostate size. This advances understanding of urethral resistance dynamics.

Keywords:
artificial intelligencebladder outflow obstructionmachine learningmale LUTSpressure flow studyurodynamics

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Area of Science:

  • Urology
  • Medical Informatics
  • Biomedical Engineering

Background:

  • Pressure flow study (PFS) curves are traditionally viewed as uniform in shape for males.
  • Understanding variations in PFS patterns is crucial for diagnosing lower urinary tract symptoms (LUTS).

Purpose of the Study:

  • To apply unsupervised machine learning (UML) for analyzing post-maximum flow segments of the PFS curve.
  • To investigate urodynamic and patient characteristics within identified PFS curve clusters in men.

Main Methods:

  • Analysis of 1650 PFS datasets from men with LUTS.
  • Utilized the k-Shape clustering algorithm on normalized PFS curve segments.
  • Explored differences in patient and urodynamic parameters across identified clusters.

Main Results:

  • Identified four distinct clusters with significant variations in patient and urodynamic characteristics.
  • Two clusters showed similar visual patterns and urethral resistance but differed in detrusor voiding contraction (DVC) and prostate size.
  • Two clusters exhibited PFS patterns deviating from the typical 'normal' urethral resistance pattern in elderly men.

Conclusions:

  • Unsupervised machine learning (UML) effectively identifies distinct pressure flow study (PFS) curve patterns in men, challenging the notion of uniformity.
  • These identified PFS subtypes correlate with detrusor voiding contraction (DVC) strength and prostate size.
  • UML clustering of urodynamic PFS data shows potential for enhanced diagnosis of urethral resistance and DVC dynamics in men.