Comparison between uroflowmetry and sonouroflowmetry in recording of urinary flow in healthy men

Comparative study of mobile acoustic uroflowmetry and conventional uroflowmetry: A systematic review and meta analysis

INTRODUCTION

Uroflowmetry is a noninvasive measurement of the volume of urine excreted over time. Conventional uroflowmetry has become the main modality of urine flow measurement within time. However, this method requires the patient to be present in the hospital or healthcare setting, thus sometimes making the patients feel uncomfortable to undergo the examination. This led to multiple measurements which are inconvenient for the patients. Mobile acoustic uroflowmetry (sono-uroflowmetry) has been proposed as an alternative method of urine flow measurement due to its portability. This study aimed to evaluate the accuracy and reliability of sono-uroflowmetry as compared to conventional uroflowmetry.

METHODS

Electronic databases searching were done using prespecified search strategy to retrieve articles related with uroflowmetry. In addition, hand-search strategy was used to identify additional articles. Studies with participants who had undergone sono-uroflowmetry were included. Voided volume, voiding duration, maximum flow rate, and average flow rate were identified and used to determine the outcomes of measurement. The quality of included articles was conducted using checklist for Diagnostic Test Accuracy Studies by JBI.

RESULTS

Initial search yielded 335 articles with four additional papers identified through hand-searching process. Six papers were retrieved and further used in the narrative synthesis. Five studies enrolled male participants, while only one of the papers enrolled female participants as additional subgroup analysis. Therefore, the meta-analysis was performed by using only male participants. Based on the meta-analysis results, there were strong to very strong positive correlation in voided volume, voiding time, average flow, average flow rate, and maximum flow rate between sono and conventional uroflowmetry.

CONCLUSION

Sonouroflowmetry showed significant positive correlations to conventional uroflowmetry, signifying its use as an alternative of conventional uroflowmetry.

Validation of an algorithm for sound-based voided volume estimation

A voiding diary is commonly used in clinical practice to monitor urinary tract health. However, manual recording and use of a measuring cup can cause significant inaccuracy and inconvenience. Recently sound-based voided volume estimation algorithms such as proudP have shown potential to accurately measure the voided volumes of patients urination while overcoming these inconveniences. In order to validate the sound-based voided volume estimation algorithm, we chose bodyweight change after urination as a reference value. Total 508 subjects from the United States and Korea were enrolled. 584 data points that have matching bodyweights change data and urination sound data were collected, and fivefold cross validation was performed in order to evaluate the model on all data in the dataset. The mean voided volume estimated by the algorithm was 202.6 mL (SD: ± 114.8) while the mean bodyweight change after urination was 208.0 g (SD: ± 121.5), and there was a strong linear correlation with high statistical significance (Pearson's correlation coefficient = 0.92, p-value < 0.001). Two paired t-test showed the equivalence with bodyweight change data with 10 mL margin. Additionally, a Bland-Altman plot shows a mean difference of - 5.5 mL with LoA (- 98.0, 87.1). The results support high performance of the algorithm across the large population data from multi-site clinical trials.

Validation of acoustic voided volume measure: a pilot prospective study

PURPOSE

We evaluated the accuracy and reliability of a new smartphone-based acoustic voided volume (VV) measurement application compared to VV estimation based on the measurement of urine volume in a bladder by ultrasound bladder scan.

PATIENTS AND METHODS

A total of 53 subjects from 01/2021 to 09/2021 were prospectively enrolled. Bladder scan-based VV estimation is based on the difference in the volume of urine in a bladder measured before urination and volume measured after urination. The acoustic VV measurement is based on smartphone-based acoustic VV measurement mobile application. VV estimates for the same void were compared between two techniques. Urinary measures were obtained from 49 male subjects resulting in a total of 245 measurements for analysis. VV measures were compared using Pearson's correlation coefficient (PCC), evaluation of observed versus predicted VV measures using linear regression fit indices, and Bland-Altman method.

RESULTS

VV between the two techniques revealed strong correlation (PCC 0.811, p < 0.001). Means of the number of measurements per patient and inpatient days for measurements analyzed are 5 and 2.7, respectively. In 245 measurements, VV measured by bladder scan is 238.69 ± 122.32 mL, VV measured by mobile application is 254.69 ± 119.28 mL, and their difference of two measurements is 16 ± 74.29 mL.

CONCLUSION

Through the comparison with VV estimated by ultrasound bladder scan, which is a technology to measure the urine volume in a bladder, it was confirmed that the smartphone-based acoustic VV measurement application proudP^® is accurate.

Development and Validation of a Deep Learning System for Sound-based Prediction of Urinary Flow

BACKGROUND

Uroflowmetry remains an important tool for the assessment of patients with lower urinary tract symptoms (LUTS), but accuracy can be limited by within-subject variation of urinary flow rates. Voiding acoustics appear to correlate well with conventional uroflowmetry and show promise as a convenient home-based alternative for the monitoring of urinary flows.

OBJECTIVE

To evaluate the ability of a sound-based deep learning algorithm (Audioflow) to predict uroflowmetry parameters and identify abnormal urinary flow patterns.

DESIGN, SETTING, AND PARTICIPANTS

In this prospective open-label study, 534 male participants recruited at Singapore General Hospital between December 1, 2017 and July 1, 2019 voided into a uroflowmetry machine, and voiding acoustics were recorded using a smartphone in close proximity. The Audioflow algorithm consisted of two models-the first model for the prediction of flow parameters including maximum flow rate (Q_max), average flow rate (Q_ave), and voided volume (VV) was trained and validated using leave-one-out cross-validation procedures; the second model for discrimination of normal and abnormal urinary flows was trained based on a reference standard created by three senior urologists.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Lin's correlation coefficient was used to evaluate the agreement between Audioflow predictions and conventional uroflowmetry for Q_max, Q_ave, and VV. Accuracy of the Audioflow algorithm in the identification of abnormal urinary flows was assessed with sensitivity analyses and the area under the receiver operating curve (AUC); this algorithm was compared with an external panel of graders comprising six urology residents/general practitioners who separately graded flow patterns in the validation dataset.

RESULTS AND LIMITATIONS

A total of 331 patients were included for analysis. Agreement between Audioflow and conventional uroflowmetry for Q_max, Q_ave, and VV was 0.77 (95% confidence interval [CI], 0.72-0.80), 0.85 (95% CI, 0.82-0.88) and 0.84 (95% CI, 0.80-0.87), respectively. For the identification of abnormal flows, Audioflow achieved a high rate of agreement of 83.8% (95% CI, 77.5-90.1%) with the reference standard, and was comparable with an external panel of six residents/general practitioners. AUC was 0.892 (95% CI, 0.834-0.951), with high sensitivity of 87.3% (95% CI, 76.8-93.7%) and specificity of 77.5% (95% CI, 61.1-88.6%).

CONCLUSIONS

The results of this study suggest that a deep learning algorithm can predict uroflowmetry parameters and identify abnormal urinary voids based on voiding sounds, and shows promise as a simple home-based alternative to uroflowmetry in the management of patients with LUTS.

PATIENT SUMMARY

In this study, we trained a deep learning-based algorithm to measure urinary flow rates and identify abnormal flow patterns based on voiding sounds. This may provide a convenient, home-based alternative to conventional uroflowmetry for the assessment and monitoring of patients with lower urinary tract symptoms.

Male urethral stricture disease. Why management guidelines are challenging in low-income countries

Urethral stricture disease is one of the commonest urological pathologies in adult men in low or low-middle income countries, providing a significant work burden for the small number of specialist surgeons who are able to provide appropriate treatment. The underlying causes of anterior urethral stricture relate to urethral fibrosis from sexually transmitted infection, with posterior urethral disruption secondary to pelvic trauma being an equally common cause of stricture disease in many countries in sub-Saharan Africa. Anterior urethral strictures are often long, and multifocal, and bulbo-prostatic disruptions are usually due to relatively low-velocity pelvic trauma. The management options available in resource poor settings are often severely limited by the individual's ability to pay for care, the availability of a specialist surgeon and, importantly, a shortage of functioning endoscopic equipment for less-invasive treatments. Consequently, reconstructive surgery is often regarded by the patient, and surgeon, as the most cost-effective and, therefore, primary means of treating a urethral stricture once urethral dilatation has failed. Regional anaesthetic techniques have limited the adoption of free graft augmentation as an alternative to pedicled flaps of locally available skin for reconstruction, whilst an inability to provide tension-free bulbo-prostatic anastomoses has negatively impacted the outcome from the treatment of pelvic fracture disruption injuries in much of sub-Saharan Africa. Urolink has, however, found that local surgeons can be taught sustainable skills required for successful complex urethroplasty when supported by longitudinal mentorship in the management of difficult clinical issues. Evidence-based practice is known to improve the standard of care in specific conditions in high-income countries, including the management of male urethral stricture disease. However, guidelines developed in high-income countries aren't necessarily appropriate for stricture management in less well-resourced healthcare environments, but could be adapted to help improve the delivery of stricture care for men in low- or low-middle income countries.

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

Purpose

To develop an automatic interpretation system for uroflowmetry (UFM) results using machine learning (ML), a form of artificial intelligence (AI).

Methods

A prospectively collected 1,574 UFM results (1,031 males, 543 females) with voided volume>150 mL was labelled as normal, borderline, or abnormal by 3 urologists. If the 3 experts disagreed, the majority decision was accepted. Abnormality was defined as a condition in which a urologist judges from the UFM results that further evaluation is required and that the patient should visit a urology clinic. To develop the optimal automatic interpretation system, we applied 4 ML algorithms and 2 deep learning (DL) algorithms. ML models were trained with all UFM parameters. DL models were trained to digitally analyze 2-dimensional images of UFM curves.

Results

The automatic interpretation algorithm achieved a maximum accuracy of 88.9% in males and 90.8% in females when using 6 parameters: voided volume, maximum flow rate, time to maximal flow rate, average flow rate, flow time, and voiding time. In females, the DL models showed a dramatic improvement in accuracy over the other models, reaching 95.4% accuracy in the convolutional neural network model. The performance of the DL models in clinical discrimination was outstanding in both genders, with an area under the curve of up to 0.957 in males and 0.974 in females.

Conclusions

We developed an automatic interpretation algorithm for UFM results by training AI models using 6 key parameters and the shape of the curve; this algorithm agreed closely with the decisions of urology specialists.

A Prospective Comparative Study of Mobile Acoustic Uroflowmetry and Conventional Uroflowmetry

PURPOSE

The aim of this study was to assess the performance of a mobile acoustic Uroflowmetry (UFM) application compared with standard UFM in the pediatric population.

METHODS

A mobile acoustic UFM application represents a noninvasive method to estimate the urine flow rate by recording voiding sounds with a smartphone. Male pediatric patients who were undergoing UFM testing were prospectively recruited, and the voiding sounds were recorded and analyzed. The intraclass correlation coefficient (ICC) was used to compare the maximum flow rate (Qmax), average flow rate (Qavg), voiding time (VT), and voiding volume (VV) as estimated by acoustic UFM with those calculated by standard UFM. Differences in Qmax, Qavg, VT, and VV between the 2 UFM tests were determined using 95% Bland-Altman limits of agreement.

RESULTS

A total of 16 male patients were evaluated. Their median age was 9 years. With standard UFM, the median Qmax, Qavg, VT, and VV were 18.7 mL/sec, 11.1 mL/sec, 15.2 seconds, and 157.8 mL, respectively. Strong correlations were observed between the 2 methods for Qmax (ICC=0.755, P=0.005), VT (ICC=0.974, P<0.001), and VV (ICC=0.930, P<0.001), but not for Qavg (ICC=0.442, P=0.135). The Bland-Altman plot showed good agreement between the 2 UFM tests. Flow patterns recorded by acoustic UFM and conventional UFM showed good visual correlations.

CONCLUSION

Acoustic UFM was comparable to standard UFM for male pediatric patients. Further validation of its performance in different toilet settings is necessary for broader use.

Classification of Bladder Emptying Patterns by LSTM Neural Network Trained Using Acoustic Signatures

(1) Background: Non-invasive uroflowmetry is used in clinical practice for diagnosing lower urinary tract symptoms (LUTS) and the health status of a patient. To establish a smart system for measuring the flowrate during urination without any temporospatial constraints for patients with a urinary disorder, the acoustic signatures from the uroflow of patients being treated for LUTS at a tertiary hospital were utilized. (2) Methods: Uroflowmetry data were collected for construction and verification of a long short-term memory (LSTM) deep-learning algorithm. The initial sample size comprised 34 patients; 27 patients were included in the final analysis. Uroflow sounds generated from flow impacts on a structure were analyzed by loudness and roughness parameters. (3) Results: A similar signal pattern to the clinical urological measurements was observed and applied for health diagnosis. (4) Conclusions: Consistent flowrate values were obtained by applying the uroflow sound samples from the randomly selected patients to the constructed model for validation. The flowrate predicted using the acoustic signature accurately demonstrated actual physical characteristics. This could be used for developing a new smart flowmetry device applicable in everyday life with minimal constraints from settings and enable remote diagnosis of urinary system diseases by objective continuous measurements of bladder emptying function.

Mobile sonouroflowmetry using voiding sound and volume

Uroflowmetry (UF) is a common clinic-based non-invasive test to diagnose Lower Urinary Tract Dysfunction (LUTD). Accurate home-based uroflowmetry methods are needed to conveniently conduct repeated uroflowmetries when patients are physiologically ready to urinate. To this end, we propose and evaluate a novel mobile sonouroflowmetry (SUF) method that estimates the urinary flow rate from a sound signal recorded using a mobile phone. By linearly mapping the total sound energy to the total voided volume, the sound energy curve is transformed to a flow rate curve allowing the estimation of the flow rate over time. An evaluation using data from 44 healthy young men showed high similarity between the UF and SUF flow rates with a mixed-effects model correlation coefficient of 0.993 and a mean root mean square error of 2.37 ml/s. Maximum flow rates were estimated with an average absolute error of 2.41 ml/s. Future work on mobile uroflowmetry can use these results as an initial benchmark for flow rate estimation accuracy.

A Novel Mobile Acoustic Uroflowmetry: Comparison With Contemporary Uroflowmetry

Purpose

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 mountable toilet system for personalized health monitoring via the analysis of excreta

Technologies for the longitudinal monitoring of a person's health are poorly integrated with clinical workflows, and have rarely produced actionable biometric data for healthcare providers. Here, we describe easily deployable hardware and software for the long-term analysis of a user's excreta through data collection and models of human health. The 'smart' toilet, which is self-contained and operates autonomously by leveraging pressure and motion sensors, analyses the user's urine using a standard-of-care colorimetric assay that traces red-green-blue values from images of urinalysis strips, calculates the flow rate and volume of urine using computer vision as a uroflowmeter, and classifies stool according to the Bristol stool form scale using deep learning, with performance that is comparable to the performance of trained medical personnel. Each user of the toilet is identified through their fingerprint and the distinctive features of their anoderm, and the data are securely stored and analysed in an encrypted cloud server. The toilet may find uses in the screening, diagnosis and longitudinal monitoring of specific patient populations.

What developments are needed to achieve less-invasive urodynamics? ICI-RS 2019

AIMS

To assess the state of technologies for urodynamics that are less invasive than standard cystometry and pressure-flow studies and to suggest areas needing research to improve this.

METHODS

A summary of a Think Tank debate held at the 2019 meeting of the International Consultation on Incontinence Research Society is provided, with subsequent analysis by the authors. Less-invasive techniques were summarized, classified by method, and possible developments considered. Discussions and recommendations were summarized by the co-chairs and edited into the form of this paper by all authors.

RESULTS

There is a full spectrum of technologies available for less-invasive assessment, ranging from simple uroflowmetry through imaging techniques to emerging complex technologies. Less-invasive diagnostics will not necessarily need to replace diagnosis by, or even provide the same level of diagnostic accuracy as, invasive urodynamics. Rather than aiming for a technique that is merely less invasive, the priority is to develop methods that are either as accurate as current invasive methods, or spare patients from the necessity of invasive methods by improving early triaging.

CONCLUSIONS

Technologies offering less-invasive urodynamic measurement of specific elements of function can be potentially beneficial. Less-invasive techniques may sometimes be useful as an adjunct to invasive urodynamics. The potential for current less-invasive tests to completely replace invasive urodynamic testing is considered, however, to be low. Less-invasive techniques must, therefore, be tested as screening/triaging tools, with the aim to spare some patients from invasive urodynamics early in the treatment pathway.

Can men with lower urinary tract symptoms (LUTs) predict voided volumes?

PURPOSE

To determine if patients can accurately estimate volumes voided in a bladder diary, and to determine the patient characteristics that are most predictive of accuracy in volume estimation in the workup of lower urinary tract symptoms (LUTs).

METHODS

We prospectively collected data on 180 consecutive patients undergoing a workup for LUTs at a tertiary care facility. Data collected include American Urological Association Symptom Scores (AUASS), flow time and rate, and one time measurement of voided volume into a blinded uroflow. Baseline characteristics and demographics were recorded. Descriptive statistics and linear regression analysis were performed to examine predictors of estimated voiding volume (mL) in SAS Version 9.4 (SAS Institute, Inc., Cary, NC, USA).

RESULTS

Median age and BMI were 64 years (SD = 15.4) and 26.9 kg/m² (SD = 4.6), respectively. The median estimated voided volume and actual voided volume were 120 mL (range 1-480) and 101.5 mL (range 6.5-622.0), respectively. On linear regression analysis, 47.1% of patients estimated volume voided with a 20% margin of error, and 63.2% of patients estimated with a 30% margin of error. Each 1-year increase in age correlated with a 2% decrease in the odds of estimating voided volume within 20% of actual volume (p < 0.05). For each 1 unit increase in flow rate, there was an 8% (p < 0.005) increase in the odds of estimating voided volume within 20% of actual volume.

CONCLUSIONS

Just under half of patients can accurately estimate volume voided with a margin of error of 20%.

Evaluation of Voiding Parameters in Healthy Women Using Sound Analysis

OBJECTIVES

Sonouroflowmetry represents a novel method for estimating urinary flow parameters. The aim of this study was to compare the urinary flow parameters acquired using sonouroflowmetry with those of standard uroflowmetry in healthy female volunteers.

METHODS

Thirty-six healthy female volunteers (aged 25-54 years) were subjected to standard uroflowmetry. Simultaneously, subjects dialed a dedicated number on a mobile phone and kept recording until urination was finished. Sound data were analyzed and compared to the uroflowmetry data. Of 218 recordings, 183 were included in the final analysis. Thirty-four measurements were excluded for voided volume <150 mL or technical problems during the recording. A linear model was fitted to calculate the urinary flow parameters and the voided volume from data obtained by sonouroflowmetry. Subsequently the matching datasets of UF and SUF were compared with respect to flow time, voided volume, maximum (Q_max ) and average (Q_ave ) flow rate. Pearson's correlation coefficient (PCC) was used to compare parameters recorded by uroflowmetry with those calculated based on sonouroflowmetry recordings.

RESULTS

A strong correlation (PCC = 0.95) was noted between uroflowmetry recorded flow time and duration of the sonouroflowmetry sound signal. The voided volume measured by uroflowmetry showed a moderate correlation (PCC = 0.68) with the calculated area under the sonouroflowmetry curve. Q_max recorded using uroflowmetry and sonouroflowmetry recorded peak sound intensity showed a weak correlation (PCC = 0.38).

CONCLUSIONS

This study validates the basic concept of using sound analysis to estimate urinary flow parameters and voided volume.