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.

研究目标：构建基于机器学习的诊断系统，用于利用压力流研究（pressure-flow studies）数据自动检测下尿路症状（lower urinary tract symptoms, LUTS）。研究方法：本研究纳入下尿路症状的6种最常见诊断类型。研究纳入了2015年至2020年间的527例数据完整的合格患者。最终选取2项全局特征（患者年龄与性别）以及13项尿动力学特征（urodynamic features）作为机器学习算法的输入变量。