inContAlert: Machine Learning Algorithms for Individual Bladder Filling Level Prediction

Completed

• Conditions: Monitoring of the Bladder Filling

• Registration Number: NCT05952700
• Lead Sponsor: inContAlert GmbH

Brief Summary

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).

Detailed Description

Not available

Study Timeline

• Study Start: 2023-03-01
• Study First Submitted: 2023-06-05
• Study First Submitted QC: 2023-07-17
• Study First Posted: 2023-07-19
• Primary Completion: 2024-07-31
• Study Completion: 2024-07-31
• Status Verified: 2025-04
• Last Update Submitted: 2025-04-16
• Last Update Posted: 2025-04-20

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 36

Inclusion Criteria

• informed consent

Exclusion Criteria

• Missing informed consent

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Difference between the predicted bladder filling level and the actual value	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).

Trial Locations

Locations (1)

inContAlert GmbH; 🇩🇪 Bayreuth, Germany