Evidence map›Paper›PMID 40422045›Full record

ArticleBiosensors2025

Improving the Accuracy of a Wearable Uroflowmeter for Incontinence Monitoring Under Dynamic Conditions: Leveraging Machine Learning Methods.

Faezeh Shanehsazzadeh, John O L DeLancey, James A Ashton-Miller

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Faezeh ShanehsazzadehDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.ORCID 0000-0001-5783-2674
John O L DeLanceyDepartment of Obstetrics & Gynecology, University of Michigan, Ann Arbor, MI 48109, USA.ORCID 0000-0002-1012-7713
James A Ashton-MillerDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.ORCID 0000-0003-1528-2787

Funding

Research Education CoreP30AG024824 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Lona Mody, RAYMOND L YUNG · 2004 to 2026
$29.2M
Development of 21st Century Concepts and Tools for Quantifying Urethral Failure Mechanisms that Cause Urinary IncontinenceRC2DK122379 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ASHTON-MILLER, JAMES ANTHONY, DELANCEY, JOHN O.L. · 2019 to 2024
$5.7M
NIA NIH HHS P30 AG024824NIDDK NIH HHS RC2 DK122379Procter & Gamble Through an institutional research contractU.S. Public Health Service 1 RC2 DK122379-01
6 · The paper itself

Abstract

Urinary incontinence affects many women, yet there are no monitoring devices capable of accurately capturing flow dynamics during everyday activities. Building on our initial development of a wearable personal uroflowmeter, this study enhances the device's performance under realistic, dynamic conditions similar to those encountered in daily living. We integrated an optimized eight-vane Etoile flow conditioner with a 0.2D opening into the device. Both computational fluid dynamics simulations and experimental tests demonstrated that this flow conditioner significantly reduced turbulence intensity by 82% and stabilized the axial velocity profile by 67%, increasing the R

Indexed as

FlowmetersMachine LearningUrinary IncontinenceWearable Electronic DevicesFemaleHumansMonitoring, PhysiologicSupport Vector Machinedynamic flow conditionsextreme gradient boostingflow conditionermachine learningsupport vector machinesurinary incontinenceuroflowmeterwearable device

Identifiers

PMID40422045
PMCPMC12110670

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.