For therapeutic decision making related to lower urinary tract symptoms (LUTS) in men, the distinction between detrusor underactivity (DU) and bladder outlet obstruction (BOO) is crucial. However, currently, accurate diagnosis of DU and BOO can only be made by pressure flow study (PFS), which is invasive and complex. To address this problem, this study focuses on the use of uroflowmetry (UFM) waveforms, which can be obtained in a non-invasive manner. More specifically, we construct a couple of one-dimensional convolutional neural networks (CNNs) that can estimate a patient's bladder contractility index (BCI) and bladder outlet obstruction index (BOOI) based on the patient's UFM waveform and classify the patient into one of four categories: normality, DU, BOO, and the combination of DU and BOO. The experimental results show that the constructed CNNs make the diagnosis more flexible and have a diagnostic accuracy comparable to that of a previous approach by human experts.
