A Prospective Comparative Study of Mobile Acoustic Uroflowmetry and Conventional Uroflowmetry

Lee, Dong-Gi; Gerber, Jonathan A.; Bhatia, Vinaya; Janzen, Nicolette; Austin, Paul F.; Koh, Chester J.; Song, Sang Hoon

doi:10.5213/inj.2142154.077

Cited by 5 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a pediatric population, Lee et al [40] proved equal efficacy of a mobile acoustic uroflowmetry (UFM) application in comparison to standard UFM. Kim et al [41 ▪▪ ] also evaluated the utility of a mobile app based on UFM and compared it to the conventional paper-based voiding diary (VD).…”

Section: Mobile Applications In Urologymentioning

confidence: 99%

Digital health innovations in urology: telemedicine, wearables, and mobile applications – a systematic review of literature

Naik,

Talyshinskii,

Rassweiler

et al. 2023

Current Opinion in Urology

View full text Add to dashboard Cite

Purpose of review There are enough publications on the use of telemedicine, wearable devices, and mobile applications in urology; however, their collective impact on urological care has not been adequately studied. This review seeks to address this deficiency by providing a descriptive analysis of the recent use of telemedicine, wearable technology, and mobile applications in urology as well as elucidating their associated challenges. Recent findings There are studies that were dedicated to the use of telemedicine, wearables, and mobile apps in urology according to inclusion criteria, respectively. They were successfully implemented in different urological subfields, such as urogynecology, endourology, pediatric urology, and uro-oncology, and led to time safety, remote monitoring, and better patient awareness. However, several concerns also exist, such as issues with data safety, measurement deviations, technical limitations, and lack of quality. Summary Telemedicine, wearables, and mobile apps have already shown their potential in urological practice. However, further studies are needed to expand both our understanding of their current state and their potential for further development and clinical use.

show abstract

Section: Mobile Applications In Urologymentioning

confidence: 99%

Digital health innovations in urology: telemedicine, wearables, and mobile applications – a systematic review of literature

Naik,

Talyshinskii,

Rassweiler

et al. 2023

Current Opinion in Urology

View full text Add to dashboard Cite

show abstract

Validation of acoustic voided volume measure: a pilot prospective study

Kim

Jung

et al. 2022

World J Urol

View full text Add to dashboard Cite

Automatic classification of the physical surface in sound uroflowmetry using machine learning methods

Alvarez,

Arjona,

Iglesias Martínez

et al. 2024

J AUDIO SPEECH MUSIC PROC.

View full text Add to dashboard Cite

This work constitutes the first approach for automatically classifying the surface that the voiding flow impacts in non-invasive sound uroflowmetry tests using machine learning. Often, the voiding flow impacts the toilet walls (traditionally made of ceramic) instead of the water in the toilet. This may cause a reduction in the strength of the recorded audio signal, leading to a decrease in the amplitude of the extracted envelope. As a result, just from analysing the envelope, it is impossible to tell if that reduction in the envelope amplitude is due to a reduction in the voiding flow or an impact on the toilet wall. In this work, we study the classification of sound uroflowmetry data in male subjects depending on the surface that the urine impacts within the toilet: the three classes are water, ceramic and silence (where silence refers to an interruption of the voiding flow). We explore three frequency bands to study the feasibility of removing the human-speech band (below 8 kHz) to preserve user privacy. Regarding the classification task, three machine learning algorithms were evaluated: the support vector machine, random forest and k-nearest neighbours. These algorithms obtained accuracies of 96%, 99.46% and 99.05%, respectively. The algorithms were trained on a novel dataset consisting of audio signals recorded in four standard Spanish toilets. The dataset consists of 6481 1-s audio signals labelled as silence, voiding on ceramics and voiding on water. The obtained results represent a step forward in evaluating sound uroflowmetry tests without requiring patients to always aim the voiding flow at the water. We open the door for future studies that attempt to estimate the flow parameters and reconstruct the signal envelope based on the surface that the urine hits in the toilet.

show abstract

A Prospective Comparative Study of Mobile Acoustic Uroflowmetry and Conventional Uroflowmetry

Cited by 5 publications

References 17 publications

Digital health innovations in urology: telemedicine, wearables, and mobile applications – a systematic review of literature

Digital health innovations in urology: telemedicine, wearables, and mobile applications – a systematic review of literature

Validation of acoustic voided volume measure: a pilot prospective study

Automatic classification of the physical surface in sound uroflowmetry using machine learning methods

Contact Info

Product

Resources

About