Machine Learning Algorithms for Individual Bladder Filling Level Prediction

• Sponsor: inContAlert GmbH (Industry)
• Overall Status: Recruiting
• CT.gov ID: NCT05952700
• Collaborator: University of Bayreuth (Other)
• Enrollment: 30
• Location: 1
• Anticipated Duration (Months): 10
• Patients Per Site Per Month: 3

Study Details

Study Description

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).

Condition or Disease	Intervention/Treatment	Phase
Monitoring of the Bladder Filling	Device: inContAlert

Study Design

• Study Type: Observational
• Anticipated Enrollment: 30 participants
• Observational Model: Case-Only
• Time Perspective: Prospective
• Official Title: Evaluation and Optimization of Machine Learning Algorithms for Individual Bladder Filling Level Prediction by a Sensor System
• Actual Study Start Date: Mar 1, 2023
• Anticipated Primary Completion Date: Dec 31, 2023
• Anticipated Study Completion Date: Dec 31, 2023

Outcome Measures

Primary Outcome Measures

1. 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).

Eligibility Criteria

Criteria

• Ages Eligible for Study: 18 Years and Older
• Sexes Eligible for Study: All

Inclusion Criteria:

• informed consent

Exclusion Criteria:

• Missing informed consent

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	inContAlert GmbH	Bayreuth		Germany

Sponsors and Collaborators

• inContAlert GmbH
• University of Bayreuth

Investigators

• Study Director: Jannik Lockl, Dr., inContAlert GmbH

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• Responsible Party: inContAlert GmbH
• ClinicalTrials.gov Identifier: NCT05952700
• Other Study ID Numbers: Az. O 1305/1 -GB
• First Posted: Jul 19, 2023
• Last Update Posted: Jul 19, 2023
• Last Verified: Jun 1, 2023

• Individual Participant Data (IPD) Sharing Statement: No
• Plan to Share IPD: No
• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No
• Keywords provided by inContAlert GmbH: bladder monitoring; digital Health; bladder filling level; incontinence; neurourology; neurogenic bladder; spinal cord injury

Study Results

No Results Posted as of Jul 19, 2023