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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

500 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-10-01

Study Completion Date

2025-02-01

Brief Summary

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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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Detailed Description

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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

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Voiding Dysfunction Voiding Disorders Machine Learning

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

PROSPECTIVE

Eligibility Criteria

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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

Minimum Eligible Age

4 Years

Maximum Eligible Age

17 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes