Evidence map›Paper›PMID 41381961›Full record

ArticleWorld journal of urology2025

Smartphone-AI videouroflowmetry evaluation (SAVE study): a concordance study with standard uroflowmetry.

Nariman Gadzhiev, Alexandr Panferov, Vitaly Yastrebov, Natalya Milostnaya, Yuri Volobuev, Maxim Bobyr, Igor Semyaniakin, Mikhail Chashchin, Vineet Gauhar, Bhaskar Kumar Somani

Abstract readComparative StudyValidation Study
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In one paragraph

Article in World journal of urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Nariman GadzhievSaint Petersburg State University Hospital, St. Petersburg, Russia.ORCID http://orcid.org/0000-0002-6255-0193
Alexandr PanferovUrology Center of the Medassist Medical Center, Department of Medical Bioengineering, Southwest State University, Kursk, Russia.ORCID http://orcid.org/0000-0001-8258-3454
Vitaly YastrebovUrology Center of the Medassist Medical Center, Kursk, Russia. yastrebov.vetaly@yandex.ru.ORCID http://orcid.org/0000-0003-1388-4194
Natalya MilostnayaDepartment for Training and Certification of Highly Qualified Personnel, Scientific Secretary of the Scientific and Technical Council, Southwest State University, Kursk, Russia.ORCID http://orcid.org/0000-0002-3779-9165
Yuri VolobuevResearch Institute of Space Instrumentation and Radioelectronic Systems, Southwest State University, Kursk, Russia.ORCID http://orcid.org/0000-0001-6122-695X
Maxim BobyrDepartment of Software Engineering, Southwest State University, Kursk, Russia.ORCID http://orcid.org/0000-0002-5400-6817
Igor SemyaniakinMoscow State University of Medicine and Dentistry, City Clinical Hospital Named After S.I. Spasokukotsky, Moscow, Russia.ORCID http://orcid.org/0000-0003-3246-7337
Mikhail ChashchinNational Medical Research Center for Therapy and Preventive Medicine, Moscow, Russia.ORCID http://orcid.org/0000-0001-6292-3837
Vineet GauharDivision of Urology, Ng Teng Fong General Hospital, National University Health System, Singapore, Singapore.ORCID http://orcid.org/0000-0002-3740-7141
Bhaskar Kumar SomaniDepartment of Urology, University Hospital Southampton NHS Foundation Trust, Southampton, UK.ORCID http://orcid.org/0000-0002-6248-6478

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo develop and validate a novel, hardware-free, smartphone-based video uroflowmetry system powered by Artificial Intelligence (AI-VUF), by comparing its diagnostic accuracy against standard weight-based uroflowmetry in male patients with lower urinary tract symptoms (LUTS), as part of the SAVE (Smartphone-AI Videouroflowmetry Evaluation) Study.

methodsA prospective study was conducted involving 103 male patients aged 48–79 years with LUTS. Each participant underwent standard weight-sensor uroflowmetry and simultaneous video recording of voiding using a smartphone. A proprietary AI algorithm was employed to extract the maximum flow rate (Qmax), while voided volume was visually estimated by an independent expert blinded to the results of standard uroflowmetry. Agreement between methods was assessed using Lin’s concordance correlation coefficient (CCC), Bland–Altman plots, absolute percentage error (APE), and responsiveness indices.

resultsAI-VUF showed near-perfect concordance with standard uroflowmetry: CCC was 0.968 for Qmax and 0.992 for voided volume. Mean difference in Qmax was 0.04 mL/s; median APE was 4.0%. Bland–Altman analysis revealed narrow limits of agreement (– 3.66 to + 3.74 mL/s). No significant proportional bias was detected. The system required no calibration and functioned solely on consumer-grade smartphones.

conclusionThis pilot SAVE study demonstrates that AI-assisted videouroflowmetry via smartphone achieves diagnostic performance comparable to standard uroflowmetry. This method offers a promising, scalable solution for remote or office-based assessment of voiding function, with potential to improve access to urological diagnostics.

Indexed as

Artificial IntelligenceLower Urinary Tract SymptomsRheologySmartphoneUrodynamicsVideo RecordingAgedHumansMaleMiddle AgedProspective StudiesArtificial intelligenceBenign prostatic hyperplasiaLower urinary tract symptomsSmartphone diagnosticsTelemedicineVideouroflowmetry

Identifiers

PMID41381961

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.