inContAlert: Machine Learning Algorithms for Individual Bladder Filling Level Prediction

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

36 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-03-01

Study Completion Date

2024-07-31

Brief Summary

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The aim of this study is to evaluate the bladder filling level of the study participants using the inContAlert sensor. The generated data will be used for the evaluation and optimization of the machine learning algorithms to be able to make precise predictions about the individual bladder fill level.

In particular, the hypothesis that the bladder filling level can be estimated by the algorithm will be tested. When testing the hypothesis, it should be determined which deviation (measured by the mean absolute percentage error) of the estimation/prediction differs from the actual value (obtained by measuring the urine output using a measuring cup in combination with kitchen scales).

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Detailed Description

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Conditions

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Monitoring of the Bladder Filling

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

PROSPECTIVE

Interventions

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inContAlert

InContAlert is a non-invasive sensor technology to measure the bladder filling level for incontinence patients. The device is fixed about 2cm above the pubic bone using a patch or strap and does not require surgery. The data collected from the patient is analyzed using deep learning algorithms. The bladder filling level determined in this way is then displayed on an app.

Intervention Type DEVICE