JMIR research protocols · 2026
Digital Acoustic Uroflowmetry for Noninvasive Urine Flow Rate Monitoring in Men Using Smartphone Acoustic Pattern Recognition: Protocol for the Development of a System and Mobile App.
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Yonathan Kevin, Rahardjo Harrina Erlianti, Wahyudi Irfan, Widia Fina, Raharja Putu Angga Risky, Sesari Saras Serani
Аннотация
BACKGROUND: Lower urinary tract symptoms (LUTS) constitute a significant global health burden with a severe impact. Uroflowmetry, the gold standard assessment for measuring urinary flow, may be inaccessible due to equipment availability or cost. Meanwhile, the proliferation of smartphones, even in resource-limited countries, offers a promising infrastructure for developing an innovative tool. Using the built-in microphone of smartphones to capture and analyze voiding sounds to estimate urine flow parameters has emerged as a potential solution to overcome these limitations.
OBJECTIVE: This study aims to develop a digital acoustic uroflowmetry system and a mobile app based on acoustic pattern recognition for the noninvasive estimation of key urine flow parameters.
METHODS: This protocol describes a staged observational study design comprising four distinct phases: (1) app development, (2) algorithm training and model optimization, (3) internal validation, and (4) independent clinical testing and comparison with conventional uroflowmetry. A smartphone app will be developed to record voiding sounds. Customized signal processing algorithms will be designed to analyze acoustic signals and estimate urine flow parameters based on these signals. Participants will be recruited from urology clinics, and each participant will undergo measurement using both conventional uroflowmetry and the acoustic uroflowmetry app. The app will guide users on proper smartphone placement during voiding to ensure input quality. Acoustic features will be extracted, and models will be trained and validated. The primary outcome will be the correlation and agreement between the values of maximum flow rate (Qmax), average flow rate (Qavg), and voided volume (VV) measured by the acoustic uroflowmetry app and those measured by conventional uroflowmetry.
RESULTS: This study is currently at the protocol development stage and received funding in May 2026. The first phase (development of the system and mobile app) started in December 2025, and the recruitment of participants for the first phase is planned to start in July 2026. The study results are expected to be available by early 2027.
CONCLUSIONS: The development of a digital acoustic uroflowmetry system using acoustic pattern recognition is intended to enhance the accessibility and convenience of urine flow monitoring, particularly for patients in regions with limited health care infrastructure.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/102842.
OBJECTIVE: This study aims to develop a digital acoustic uroflowmetry system and a mobile app based on acoustic pattern recognition for the noninvasive estimation of key urine flow parameters.
METHODS: This protocol describes a staged observational study design comprising four distinct phases: (1) app development, (2) algorithm training and model optimization, (3) internal validation, and (4) independent clinical testing and comparison with conventional uroflowmetry. A smartphone app will be developed to record voiding sounds. Customized signal processing algorithms will be designed to analyze acoustic signals and estimate urine flow parameters based on these signals. Participants will be recruited from urology clinics, and each participant will undergo measurement using both conventional uroflowmetry and the acoustic uroflowmetry app. The app will guide users on proper smartphone placement during voiding to ensure input quality. Acoustic features will be extracted, and models will be trained and validated. The primary outcome will be the correlation and agreement between the values of maximum flow rate (Qmax), average flow rate (Qavg), and voided volume (VV) measured by the acoustic uroflowmetry app and those measured by conventional uroflowmetry.
RESULTS: This study is currently at the protocol development stage and received funding in May 2026. The first phase (development of the system and mobile app) started in December 2025, and the recruitment of participants for the first phase is planned to start in July 2026. The study results are expected to be available by early 2027.
CONCLUSIONS: The development of a digital acoustic uroflowmetry system using acoustic pattern recognition is intended to enhance the accessibility and convenience of urine flow monitoring, particularly for patients in regions with limited health care infrastructure.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/102842.