Previously submitted to: JMIR Human Factors (no longer under consideration since May 06, 2025)
Date Submitted: Nov 15, 2024
Open Peer Review Period: Dec 25, 2024 - Feb 19, 2025
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An emerging trend of at-home uroflowmetry— Designing a new vibration-based uroflowmeter with artificial intelligence pattern recognition of uroflow curves and comparing with other technologies
ABSTRACT
Background:
With an aging population, patients complaining about low urinary tract symptoms (LUTS) increased year by year. For functional evaluation of low urinary tract (LUT), bladder diary (BD) and uroflowmetry (UFM) are both common non-invasive examinations for patients with LUTS. Voiding pattern of a patient is currently measured by hospital uroflowmetry and/or home bladder diaries. However, bladder diaries are less objective and often contain missed data, while hospital uroflowmetry lacks repeated measurements and convenience. Thus, it is important to develop a convenient and artificial intelligence(AI) based at-home uroflow monitoring.
Objective:
This study developed an innovative home vibration-based uroflowmetry for uroflow-curve pattern recognition and voiding parameters measurement and compared it with other home uroflowmetry.
Methods:
Seventy-six male participants with informed consents received uroflowmetry for voiding symptoms. An accelerometer attached on the urine bucket of the uroflowmeter detects vibration signals generating root mean square (RMS), maximal amplitude (Mmax) and the uroflowmeter measures voiding parameters and creates uroflow curves simultaneously. Vibration signals were processed with an artificial intelligence (AI) model (Convolution Neural Network) to recognize six patterns of uroflow curves to assist diagnosis.
Results:
Seventy-six participants’ voiding volume ranged from 50 ml to 690 ml (average: 192.50 ±155.58 ml). The correlation analysis revealed positive correlations between voided volume and RMS (R=0.768, p<.001), maximal flow rate (Qmax) and Mmax (R=0.684, p<.001), voiding time and signal time (R=0.838, p<.001), time to Qmax and time to Mmax (R=0.477, p<.001). AI pattern recognition demonstrated high accuracy with all three indicators (precision, recall, and F1 score) surpassing 0.97.
Conclusions:
Usage of home uroflowmetry increases because of convenience, repeated measurement, and big data application. This vibration-based home uroflowmetry with AI assistance is feasible for at-home voiding parameters measurement and uroflow-curve pattern recognition. Compared with other technologies, vibration-based uroflowmetry demonstrates advantages for intuitive and contact-free usage of home uroflowmetry. Clinical Trial: The research protocol was approved by the institutional review board (IRB: 107-B-10-01).
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