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inContAlert: Machine Learning Algorithms for Individual Bladder Filling Level Prediction

I

inContAlert

Status

Completed

Conditions

Monitoring of the Bladder Filling

Treatments

Device: inContAlert

Study type

Observational

Funder types

Other
Industry

Identifiers

NCT05952700
Az. O 1305/1 -GB

Details and patient eligibility

About

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).

Enrollment

36 patients

Sex

All

Ages

18+ years old

Volunteers

No Healthy Volunteers

Inclusion criteria

  • informed consent

Exclusion criteria

  • Missing informed consent

Trial contacts and locations

1

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Central trial contact

Jannik Lockl, Dr.

Data sourced from clinicaltrials.gov

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