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Artificial Intelligence-Based Analysis of Uroflowmetry Patterns in Children: a Machine Learning Perspective

NCT06814847 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 500

Last updated 2025-02-25

No results posted yet for this study

Summary

Uroflowmetry is the one of the most commonly used non-invasive test for evaluating children with lower urinary tract symptoms (LUTS). However, studies have highlighted a weak agreement among experts in interpreting uroflowmetry patterns. This study aims to assess the impact of machine learning models, which have become increasingly prevalent in medicine, on the interpretation of uroflowmetry patterns.

Conditions

  • Voiding Dysfunction
  • Voiding Disorders
  • Machine Learning

Sponsors & Collaborators

  • Marmara University

    lead OTHER

Eligibility

Min Age
4 Years
Max Age
17 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2024-10-01
Primary Completion
2025-01-01
Completion
2025-02-01

Countries

  • Turkey (Türkiye)

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06814847 on ClinicalTrials.gov