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

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

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.

Study Overview

• Status: Completed
• Conditions: Voiding Disorders; Machine Learning; Voiding Dysfunction

Detailed Description

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.

• Study Type: Observational
• Enrollment (Actual): 500

Contacts and Locations

Study Locations

• Turkey
  • Istanbul, Turkey, 34890
    • Marmara University School of Medicine, Urology Department

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

Children aged 4-17

Description

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

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Marmara University

Study record dates

Study Major Dates

• Study Start (Actual): October 1, 2024
• Primary Completion (Actual): January 1, 2025
• Study Completion (Actual): February 1, 2025

Study Registration Dates

• First Submitted: February 3, 2025
• First Submitted That Met QC Criteria: February 3, 2025
• First Posted (Actual): March 25, 2025

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: February 22, 2025
• Last Verified: February 1, 2025

More Information

Terms related to this study

• Keywords: Artificial intelligence; Machine learning; Uroflowmetry voiding patterns; Interpretation differences
• Other Study ID Numbers: MAR.UAD.0019

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No