RT info:eu-repo/semantics/article
T1 Flow prediction in sound-based uroflowmetry
A1 Alvarez, Marcos Lazaro
A1 Arjona, Laura
A1 Jojoa Acosta, Mario Fernando
A1 Bahillo Martínez, Alfonso
K1 Acoustic voiding signals
K1 Flow prediction
K1 Machine learning
K1 Sound-based uroflowmetry
AB Sound-based uroflowmetry (SU) offers a non-invasive alternative to traditional uroflowmetry (UF) for evaluating lower urinary tract dysfunctions, enabling home-based testing and reducing the need for clinic visits. This study compares SU and UF in estimating urine flow rate and voided volume in 50 male volunteers (aged 18–60), with UF results from a Minze uroflowmeter as the reference standard. Audio signals recorded during voiding were segmented and machine learning algorithms (gradient boosting, random forest, and support vector machine) estimated flow parameters from three devices: Ultramic384k, Mi A1 smartphone, and Oppo smartwatch. The mean absolute error for flow rate estimation were 2.6, 2.5 and 2.9 ml/s, with R2 values of 84%, 83%, and 79%, respectively. Analysis of the Ultramic384k’s frequency range showed that the 0–8 kHz band contained 83% of significant components, suggesting higher sampling frequencies are unnecessary. A 1000 ms segment size was optimal for balancing computational efficiency and accuracy. Lin’s concordance coefficients for urine flow and voided volume using the smartwatch (0–8 kHz, 1000 ms) were 0.9 and 0.85, respectively, demonstrating that SU is a reliable, cost-effective alternative to UF for estimating key uroflowmetry parameters, with added patient convenience.
PB Springer Nature
SN 2045-2322
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/80188
UL https://uvadoc.uva.es/handle/10324/80188
LA eng
NO Scientific Reports, vol. 15, n. 643
NO Producción Científica
DS UVaDOC
RD 01-dic-2025