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

Launched by MARMARA UNIVERSITY · Feb 3, 2025

Keywords

ClinConnect Summary

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...

Gender

ALL

Eligibility criteria

• 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