NCT06814847

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

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 500 (actual)
• Sponsor: Marmara University · Academic / Other
• Sex: All
• Age: 4 Years – 17 Years
• Healthy volunteers: Accepted

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.

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.

Conditions

• Voiding Dysfunction
• Voiding Disorders
• Machine Learning

Timeline

• Start date: 2024-10-01
• Primary completion: 2025-01-01
• Completion: 2025-02-01
• First posted: 2025-02-07
• Last updated: 2025-02-25

Locations

1 site across 1 country: Turkey (Türkiye)