Flow prediction in sound-based uroflowmetry

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dc.contributor.authorÁlvarez Arteaga, Marcos Lázaro
dc.contributor.authorArjona Aguilera, Laura
dc.contributor.authorJojoa Acosta, Mario Fernando
dc.contributor.authorBahillo, Alfonso
dc.date.accessioned2025-05-07T13:15:28Z
dc.date.available2025-05-07T13:15:28Z
dc.date.issued2025-01-03
dc.date.updated2025-05-07T13:15:28Z
dc.description.abstractSound-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 convenienceen
dc.description.sponsorshipThis research was supported by the Spanish Ministry of Science and Innovation under SWALU project (ref. CPP2022-010045) and ’Ayuda para contratos predoctorales 2020 (ref. PRE2020-095612)’ funded by MICIU/AEI /10.13039/501100011033 and co-financed by FSE invierte en tu futuro. Additionally, partial support was provided by the Ministry under the Aginplace project (ref. PID2023-146254OB-C41 and ref. PID2023-146254OA-C44)en
dc.identifier.citationAlvarez, M. L., Arjona, L., Jojoa-Acosta, M., & Bahillo, A. (2025). Flow prediction in sound-based uroflowmetry. Scientific Reports, 15(1). https://doi.org/10.1038/S41598-024-84978-W
dc.identifier.doi10.1038/S41598-024-84978-W
dc.identifier.eissn2045-2322
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2682
dc.language.isoeng
dc.publisherNature Research
dc.rights© The Author(s) 2025, corrected publication 2025
dc.subject.otherAcoustic voiding signals
dc.subject.otherFlow prediction
dc.subject.otherMachine learning
dc.subject.otherSound-based uroflowmetry
dc.titleFlow prediction in sound-based uroflowmetryen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue1
oaire.citation.titleScientific Reports
oaire.citation.volume15
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc-nd/4.0/
oaire.versionCVoR
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