Lower urinary tract symptoms (LUTS) encompass a spectrum of bothersome conditions affecting a substantial proportion of the global population, significantly diminishing quality of life and imposing a considerable economic burden on health care systems worldwide [1]. An estimated 2.3 billion people globally are affected by LUTS [1]. The assessment of urinary flow dynamics (uroflowmetry) is the gold standard noninvasive method used in the diagnostic evaluation of these conditions. It provides critical parameters, such as maximum flow rate (Q_max), average flow rate (Q_avg), voided volume (VV), and flow time (FT), which are essential for forming diagnoses, guiding treatment decisions, and monitoring therapeutic outcomes [2]. Traditionally, uroflowmetry is performed in specialized clinical settings using dedicated equipment. Although it is considered the clinical gold standard, it has several inherent limitations. Access to uroflowmetry can be constrained by geographic location, especially in underdeveloped regions, as well as by the availability of specialized equipment and trained personnel. Furthermore, the need to travel to a suitable clinic for testing can be inconvenient and time consuming, leading to delays in diagnosis and suboptimal monitoring [3]. These barriers are particularly pronounced in resource-limited countries with uneven health care infrastructures.

The rapid adoption of smartphones, which are equipped with increasingly sophisticated sensors and powerful processors, has opened new frontiers in health care delivery, potentially improving access to various health care infrastructures. Even in resource-limited countries, the ownership and use of smartphones continue to rise. For example, data from Indonesia in 2024 indicated that 68.65% of the population owned mobile phones [4]. Previous studies have found that acoustic uroflowmetry is an alternative to conventional uroflowmetry that leverages the built-in microphone of a smartphone to record the acoustic signals generated during urination. By analyzing specific characteristics of these voiding sounds, such as intensity, frequency spectrum, and temporal patterns, sophisticated algorithms can be developed to estimate urine flow parameters [5-7]. The fundamental premise is that the acoustic signature produced when the urine stream impacts the toilet water surface correlates with urine flow rate. This approach offers a compelling solution to the limitations of conventional uroflowmetry. It requires no additional hardware beyond the smartphone itself, is highly portable, and enables patients to perform tests in the privacy and convenience of their own homes [5]. This creates a potential for home-based monitoring, enabling more frequent assessments, early detection of changes in urinary function, and timely intervention. Moreover, it may also serve as an alternative to traditional bladder diaries by providing a more precise and convenient approach for patients [5].

Despite these promising developments, the path to widespread clinical adoption of smartphone-based acoustic uroflowmetry apps is fraught with challenges that must be systematically addressed. The acoustic characteristics of voiding sounds are inherently variable and can be influenced by a multitude of factors, including the specifics of smartphones, the distance and orientation of the phone relative to the sound source, the shape and size of the toilet bowl, the water level, ambient environmental noise, and even individual variations in urinary stream characteristics [1,4,8]. These variabilities pose significant demands on the generalizability of the app’s underlying algorithms. Accurately translating complex acoustic signals into reliable and clinically meaningful urine flow parameters necessitates advanced signal processing techniques and robust model development [9,10]. Moreover, clinical validation must be used to demonstrate the accuracy, reliability, and consistency of the app when compared with the established gold standard [11].

This protocol is designed to provide a standardized framework for the development and validation of an acoustic uroflowmetry mobile app, using a clearly staged design that progresses from app development to algorithm training and model development, followed by clinical validation against conventional uroflowmetry.

The study was funded in May 2026 following peer review by the funder (Multimedia Appendix 1). 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.

The development of a reliable acoustic uroflowmetry app has the potential to transform LUTS management by providing a readily accessible tool for urine flow monitoring. This protocol outlines a comprehensive framework for the development and clinical validation of such an app, addressing key aspects ranging from technical implementation to statistical analysis.

Although urodynamic assessment already has widespread use in developed countries, access to such assessments remains limited, particularly for individuals in remote or underdeveloped areas and those with mobility limitations. This limitation is addressed using the app by enabling testing in the home environment [1,3,4]. The decentralization of care could lead to earlier detection of urinary flow abnormalities, facilitate more timely interventions, and improve long-term outcomes for patients with lower urinary tract conditions such as BPH, urethral stricture, and neurogenic bladder [19]. The ability to perform frequent and convenient home-based monitoring could provide clinicians with more comprehensive datasets for assessment, moving beyond the limited snapshot provided by a single clinic-based measurement, which may be affected by factors such as insufficient urine volume or technical errors. Moreover, it could provide data similar to those obtained from a bladder diary with greater convenience by eliminating the need for manual urine collection during each micturition [20].

A primary concern associated with this innovation is ensuring the accuracy and reliability of acoustic-based flow estimations across a wide range of real-world conditions. Variability in smartphone hardware, environmental acoustics, and user technique can introduce significant noise and bias into the measurements [8,21-24]. Problems such as weak or absent signals and patient compliance need to be addressed. Our protocol addresses these challenges through attention to signal processing algorithms, baseline noise calibration, standardized user guidance for smartphone placement, reminder features, and the potential use of machine learning for pattern recognition. Moreover, clinical validation against the gold standard is a crucial step in establishing credibility and the potential clinical utility of the app.

Several pioneering studies have begun to explore the feasibility and validity of acoustic uroflowmetry, with variable results. One of the previous studies on this method was conducted by El Helou et al [1], who proposed and evaluated a novel mobile acoustic uroflowmetry method, demonstrating a strong correlation between flow rates estimated from smartphone-recorded sound signals and conventional uroflowmetry. Other studies, such as that by Dawidek et al [25], found that there was only a very low correlation for maximum flow measurement using audio-based uroflowmetry, thereby necessitating further research. Commercial ventures have also emerged, claiming to use AI to analyze urination sounds and provide flow metrics, signaling a growing interest in this technology within the consumer market [5]. Furthermore, comparative studies, such as those conducted by Song et al [7] and Rangganata et al [8], have evaluated mobile acoustic uroflowmetry apps against conventional uroflowmetry, further investigating their potential as reliable alternatives. A multicenter observational pilot validation study specifically focused on patients with BPH undergoing transurethral resection has also assessed the efficacy and reliability of app-based uroflowmetry for treatment monitoring [20]. These collective efforts underscore the growing recognition of the potential of acoustic uroflowmetry.

This study has several limitations. The generalizability of the findings may be initially limited to the specific population and smartphone models included in the validation study. Therefore, larger and more heterogeneous samples from a multicenter approach will eventually be necessary to confirm broader applicability. The restriction to male participants limits the applicability of the findings to female patients with LUTS. However, as detailed in the Methods section, a separate validation study among women may be performed in the future. Moreover, the potential for selection bias should also be acknowledged, as participants recruited from urology clinics may have more severe symptoms than the general population. The app does not aim to replace comprehensive urodynamic studies when indicated, but rather to provide a convenient screening and monitoring tool prior to formal urodynamic evaluation.