An automatic urodynamic diagnostic system for the diagnosis of Lower Urinary Tract Symptoms

Objectives: To establish a machine learning based diagnostic system for automatic detection of lower urinary tract symptoms (LUTS) using pressure-flow studies’ data. Methods: The six most common diagnoses of LUTS were included in the present study. A number of 527 eligible patients with complete data, from the year of 2015 to 2020, were enrolled in this study. Totally, two global features (patient age and gender) and 13 urodynamic features were considered to be the input for machine learning algorithms.