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

Marmara University

Status

Completed

Conditions

Machine Learning

Voiding Disorders

Voiding Dysfunction

Study type

Observational

Funder types

Other

Identifiers

NCT06814847

MAR.UAD.0019

Details and patient eligibility

About

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.

Full 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.

Enrollment

500 patients

Sex

All

Ages

4 to 17 years old

Volunteers

Accepts Healthy Volunteers

Inclusion criteria