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Completed

NCT Number: NCT05952700

inContAlert: Machine Learning Algorithms for Individual Bladder Filling Level Prediction

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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Key information

Conditions

Monitoring of the Bladder Filling Central Nervous System Diseases Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Male Urogenital Diseases Nervous System Diseases Neurologic Manifestations Pathological Conditions, Signs and Symptoms Signs and Symptoms Spinal Cord Diseases Spinal Cord Injuries Trauma, Nervous System Urinary Bladder Diseases Urinary Bladder, Neurogenic Urogenital Diseases Urologic Diseases Wounds and Injuries

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

inContAlert GmbH

Bayreuth, Germany

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • informed consent

Exclusion criteria

  • Missing informed consent

Treatment and study plan

inContAlert

Device

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.

Primary outcomes

  1. Difference between the predicted bladder filling level and the actual value

    Time frame: December 2023

    Difference (measured as mean absolute error in percent) of the predicted bladder filling level (measured in ml) and the actual value (determined by measuring the volume of urine in ml with a measuring cup in combination with a kitchen scale).

Sponsors and collaborators

Lead sponsor

inContAlert GmbH

Industry

Collaborators

  • University of Bayreuth

Registry information

Official study title

Evaluation and Optimization of Machine Learning Algorithms for Individual Bladder Filling Level Prediction by a Sensor System

Important dates

Study start
2023
Primary completion
2024
Study completion
2024
First posted
Jul 19, 2023
Registry last updated
Apr 20, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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