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

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.

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Key information

Conditions

Voiding Dysfunction Machine Learning Voiding Disorders

Age range

4 year–17 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Marmara University School of Medicine, Urology Department

Istanbul, 34890, Turkey (Türkiye)

About this study

The study included uroflowmetry tests of children aged 4-17 years who were referred to our clinic with lower urinary tract symptoms. Uroflowmetry patterns were independently interpreted by three pediatric urology experts. Discrepancies in interpretations were jointly re-evaluated by the three observers, and a consensus was reached. Voiding volume, voiding duration, and urine flow rates at 0.5-second intervals were converted into numerical data for analysis. Eighty percent of the dataset was used as training data for machine learning, while there maining 20% was reserved for testing. A total of five different machine learning models were employed for classification: Decision Tree, Random Forest, CatBoost, XGBoost, and LightGBM. The models that most accurately identified each uroflowmetry pattern were determined.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

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

Treatment and study plan

Primary outcomes

Performance of Machine Learning Models in Evaluating Voiding Patterns

Time frame: From October 2024 to January 2025

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

Sponsors and collaborators

Lead sponsor

Marmara University

Other

Registry information

Official study title

Interpretation of Uroflowmetry Samples from Pediatric Patients by Clinicians and Introduction to Artificial Intelligence, and Interpretation of the Samples by Artificial Intelligence

Important dates

Study start: 2024
Primary completion: 2025
Study completion: 2025
First posted: Feb 7, 2025
Registry last updated: Feb 25, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed