NCT06814847

Brief 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.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
500

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Oct 2024

Shorter than P25 for all trials

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

October 1, 2024

Completed
3 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

January 1, 2025

Completed
1 month until next milestone

Study Completion

Last participant's last visit for all outcomes

February 1, 2025

Completed
2 days until next milestone

First Submitted

Initial submission to the registry

February 3, 2025

Completed
4 days until next milestone

First Posted

Study publicly available on registry

February 7, 2025

Completed
Last Updated

February 25, 2025

Status Verified

February 1, 2025

Enrollment Period

3 months

First QC Date

February 3, 2025

Last Update Submit

February 22, 2025

Conditions

Voiding DysfunctionVoiding DisordersMachine Learning

Keywords

Uroflowmetry voiding patternsInterpretation differencesMachine learningArtificial intelligence

Outcome Measures

Primary Outcomes (1)

  • Performance of Machine Learning Models in Evaluating Voiding Patterns

    5 different machine learning models were used. Accuracy rates were determined for each model.

    From October 2024 to January 2025

Eligibility Criteria

Age4 Years - 17 Years
Sexall
Healthy VolunteersYes
Age GroupsChild (0-17)
Sampling MethodProbability Sample
Study Population

Children aged 4-17

You may qualify if:

  • Aged between 4 and 17 years with LUTS
  • Urinate more than 50% of the expected bladder capacity on UF

You may not qualify if:

  • Patients who were unable to cooperate with the voiding command
  • Had neurological disorders
  • Urinate less than 50% of the expected bladder capacity on UF
  • Under 4 years of age, and were over 18 years of age

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Marmara University School of Medicine, Urology Department

Istanbul, Istanbul, 34890, Turkey (Türkiye)

Location

Related Publications (1)

  • Arslan F, Algorabi O, Ozkan OC, Turkan YS, Ersin Namli, Genc YE, Sekerci CA, Yucel S, Cam K, Tarcan T. Artificial Intelligence-Based Analysis of Uroflowmetry Patterns in Children: A Machine Learning Perspective. Neurourol Urodyn. 2025 Nov;44(8):1575-1582. doi: 10.1002/nau.70139. Epub 2025 Sep 4.

    PMID: 40908659DERIVED

Study Design

Study Type
observational
Observational Model
CASE ONLY
Time Perspective
PROSPECTIVE
Target Duration
3 Months
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

February 3, 2025

First Posted

February 7, 2025

Study Start

October 1, 2024

Primary Completion

January 1, 2025

Study Completion

February 1, 2025

Last Updated

February 25, 2025

Record last verified: 2025-02

Data Sharing

IPD Sharing
Will not share

Locations

TU(1)