A Novel Mobile Acoustic Uroflowmetry: Comparison With Contemporary Uroflowmetry

Abstract

This study aimed to evaluate the accuracy and reliability of a new smartphone-based acoustic uroflowmetry compared to conventional uroflowmetry. Methods: This was a prospective validation study enrolling 128 subjects from September 2017 to April 2018 comparing a novel acoustic uroflowmetry to conventional uroflowmetry in an outpatient urologic clinic at Seoul National University Bundang Hospital. Visual comparison of flow patterns and uroflow parameters such as maximum flow rate (Qmax), average flow rate (Qavg), and voided volume were compared between the 2 techniques. Reliability and accuracy of the uroflowmetry results were compared using Pearson correlation coefficient (PCC) and Student t-test, respectively. Results: One hundred twelve participants were included in the study. Of these, 77 had baseline urologic comorbidities while 35 were normal participants. Flow patterns between the 2 uroflowmetry techniques demonstrated strong visual correlation. When compared to conventional uroflowmetry, all 3 parameters of voiding in male participants showed a very robust correlation with PCC of 0.88, 0.91, and 0.95 for Qmax, Qavg, and voided volume, respectively. Among female participants, we observed a PCC of 0.78, 0.93, and 0.96 for Qmax, Qavg, and voided volume, respectively. The Qmax showed a statistically significant difference in both sexes between the 2 methods, although the absolute value was small. Conclusions: Uroflowmetry using acoustic analysis demonstrates comparable findings to conventional uroflowmetry. This provides an opportunity to perform uroflowmetry in the clinic or at home in a reliable, inexpensive manner. Future large-scale prospective studies are required to further validate our results.

A Prospective Comparative Study of Mobile Acoustic Uroflowmetry and Conventional Uroflowmetry

“…This difference relative to previous studies may be attributed to the method of generating the estimated sound-based UFM curve in the present study. The PRIVY application uses an algorithm from a former validation study involving an adult population [8]. Furthermore, the application showed the estimated Qmax as a result and not the peak sound intensity, as demonstrated in a previous study.…”

“…The application reports VT based on the duration of the urine flow, VV based on the calculated area under the curve, and Qmax based on the peak flow rate of the estimated flow curve. The accuracy of this application was previously validated in an adult population consisting of 66 men and 46 women [7,8]. In this pediatric study, we applied the flow prediction model using sound data from pediatric patients and used it to generate a predicted flow curve.…”

A Novel Acoustic Uroflowmetry‐Based Mobile App Voiding Diary: Comparison with Conventional Paper‐Based Voiding Diary

“…When a patient urinates into a toilet bowl, the app captures ambient sounds generated by the event and the machine learning algorithm infers key voiding parameters from the acoustic signals. Lee et al [ 10 ] conducted a clinical study to validate the baseline technology. In the study, we described our acoustic uroflowmetry system as follows: with a wireless, smartphone-based approach, the sound data was recorded in real-time with a smartphone application.…”

“…In our previous study, we introduced a new smartphone-based uroflowmetry (UFM) device using acoustic analysis and reported results comparable to those of contemporary office-based UFM [ 10 ]. Notably, the correlation between voided volume and predicted voided volume was excellent (Pearson's correlation coefficient [PCC, r ] = 0.96).…”

Development of an Automatic Interpretation Algorithm for Uroflowmetry Results: Application of Artificial Intelligence

“…Using this program, they can perform UFM tests in any location (at home, at work, on vacation, or anywhere else) at any time just as comfortably as they would void in daily life. However, even if users obtain accurate and representative measurements, it is difficult to judge whether the results are normal or whether they reflect abnormal conditions that require urological management [ 8 ]. To identify abnormal results and recommend that those users visit a urologic clinic, we aimed to develop an automatic interpretation system for UFM results by applying machine learning (ML) and deep learning (DL), 2 subsets of artificial intelligence (AI).…”