Peter Rosier

Background and Aims: The urethral resistance (UR) is one of the key outcomes of the urodynamic pressure‐flow study (PFS) in men. Combined with a clinical meaningful cut‐off point, urodynamic bladder outflow obstruction (BOO) can be determined. This characterization of BOO provides guidance in the management of lower urinary tract symptoms (LUTS), enabling the differentiation between BOO and detrusor underactivity. In this way, the UR can be objectified and monitored, providing insight into the expected results of a clinical intervention. The route from the raw urodynamic measurement towards the diagnosis has faced fundamental challenges over the years. This narrative review aims to give an overview of the background of our current UR measures in men and provides an outlook towards new developments. Methods: A non‐structured literature search was conducted into the development of assessment of UR. Results: A narrative historic overview is given of the development of assessment of UR. Conclusion: The history of assessing urethral resistance centers on simplifying complex fluid dynamics into clinical tools, evolving from static calculations to dynamic, tissue‐elasticity. Today, a renewed focus on voiding physiology, paired with machine learning and computational fluid dynamics, promises mathematically valid, patient‐specific diagnostics to optimize the management of bladder outflow obstruction.

Objectives: To explore the use of unsupervised machine learning (UML) to analyse segments of the pressure flow study (PFS) curve after maximum flow, and subsequently to analyse the urodynamic and patient characteristics of men in the detected clusters. Subjects and Methods: In this study, we considered 1650 PFSs of men with lower urinary tract symptoms, without relevant interventions in the past. After datapoint reduction and normalisation of the PFS curve segments, the k‐Shape clustering algorithm was used to identify different pattern clusters. Differences in patient and urodynamic characteristics among those clusters were explored. Results: The UML approach identified four prominent clusters, with significantly different patient and urodynamic characteristics. Two pairs of these clusters were visually similar, and included similar urethral resistance values; however, they differed with regard to detrusor voiding contraction (DVC) and prostate size. In two clusters, the PFS curve pattern was significantly different from the commonly assumed ‘normal’ urethral resistance pattern in elderly men. Conclusion: In males, PFS patterns are considered to be uniform in shape. However, this study shows that UML can help to identify clusters of pressure–flow urethral resistance subtype patterns in men. We found that these subtype patterns were associated with DVC strength and prostate size. This feasibility study has shown that UML clustering of urodynamic PFSs in men holds promise for improving the diagnosis of urethral resistance and DVC properties and dynamics.

Introduction: Pressure‐flow study, consisting of flow rate combined with detrusor pressure, is the gold standard to determine bladder outflow obstruction and evaluate the detrusor voiding contraction. In very young children, not able to sit on a uroflowmetry device, the flow rate cannot be measured. This results in an incomplete evaluation of bladder and urethral functions. The aim of this study is to derive the flow rate using standard care video‐urodynamic study X‐ray images. Methods: We retrospectively included 50 video urodynamic studies in children able to void on a uroflowmetry toilet, to correlate the outcome with standard uroflowmetry. The X‐ray images taken during voiding were manually segmented and algorithmically converted into a flow rate, called “videoflow”. This videoflow was compared with the measured (standard) uroflowmetry. Results: An excellent cross‐correlation of 0.98 was found between the videoflow and normal uroflowmetry outcomes. The videoflow maximum flow rate ( Q _max ) was accurate with a not significant bias of +0.1 mL/s difference with standard uroflowmetry. Conclusions: In conclusion, the proposed videoflow is found feasible and accurate in children who are able to void in a uroflowmetry toilet. This method paves the way to measure urine flow rate in very young children who cannot yet sit upright. For the first time, this new technique will enable the validation of a pressure‐flow‐based urethral resistance measurement in this very young age group. Trial Registration: Clinical Registration Number: 23U‐0621.