Voiding DysfunctionNCT06814847
Artificial Intelligence-Based Analysis of Uroflowmetry Patterns in Children: a Machine Learning Perspective
An observational study of Voiding Dysfunction and Voiding Disorders, run by Marmara University at 1 site in Turkey (Türkiye).
Trial summary
Uroflowmetry is the one of the most commonly used non-invasive test for evaluating children with lower urinary tract symptoms (LUTS). However, studies have highlighted a weak agreement among experts in interpreting uroflowmetry patterns. Read more about this study
- Who is it for?
- Ages 4 to 17 yearsHealthy volunteers can take part
- Conditions the study is for
- Voiding Dysfunction, Voiding Disorders, Machine Learning
- How many people
- 500 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Oct 2024to 1 Feb 2025
- How long will I take part?
- 3 Months
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 4 to 17 years
- All sexes
- Healthy volunteers accepted
- Child
Study population: Children aged 4-17
Who may be able to join
- Aged between 4 and 17 years with LUTS
- Urinate more than 50% of the expected bladder capacity on UF
Who cannot join
- Patients who were unable to cooperate with the voiding command
- Had neurological disorders
- Urinate less than 50% of the expected bladder capacity on UF
- Under 4 years of age, and were over 18 years of age
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Marmara University School of Medicine, Urology Department
Istanbul, Istanbul, 34890
About this study
Official title: Interpretation of Uroflowmetry Samples from Pediatric Patients by Clinicians and Introduction to Artificial Intelligence, and Interpretation of the Samples by Artificial Intelligence
Uroflowmetry is the one of the most commonly used non-invasive test for evaluating children with lower urinary tract symptoms (LUTS). However, studies have highlighted a weak agreement among experts in interpreting uroflowmetry patterns. This study aims to assess the impact of machine learning models, which have become increasingly prevalent in medicine, on the interpretation of uroflowmetry patterns.
Read the detailed description
The study included uroflowmetry tests of children aged 4-17 years who were referred to our clinic with lower urinary tract symptoms. Uroflowmetry patterns were independently interpreted by three pediatric urology experts. Discrepancies in interpretations were jointly re-evaluated by the three observers, and a consensus was reached. Voiding volume, voiding duration, and urine flow rates at 0.5-second intervals were converted into numerical data for analysis. Eighty percent of the dataset was used as training data for machine learning, while there maining 20% was reserved for testing. A total of five different machine learning models were employed for classification: Decision Tree, Random Forest, CatBoost, XGBoost, and LightGBM. The models that most accurately identified each uroflowmetry pattern were determined.
- Conditions
- Voiding Dysfunction, Voiding Disorders, Machine Learning
- Keywords
- Uroflowmetry voiding patterns, Interpretation differences, Machine learning, Artificial intelligence
What is being measured
The primary outcome is the main question the study is designed to answer.
Performance of Machine Learning Models in Evaluating Voiding Patterns
5 different machine learning models were used. Accuracy rates were determined for each model.
Measured: From October 2024 to January 2025
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Feb 2025
- Study start1 Oct 2024
- Primary completion1 Jan 2025
- Study completion1 Feb 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Voiding DysfunctionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 500 participants (actual)
- Target duration
- 3 Months
Sponsor and investigators
- Lead sponsor
- Marmara University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06814847
- Sponsor's protocol ID
- MAR.UAD.0019
- Status last verified
- Feb 2025
- First submitted
- 3 Feb 2025
- Last updated
- 25 Feb 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Arslan F, Algorabi O, Ozkan OC, Turkan YS, Ersin Namli, Genc YE, Sekerci CA, Yucel S, Cam K, Tarcan T. Artificial Intelligence-Based Analysis of Uroflowmetry Patterns in Children: A Machine Learning Perspective. Neurourol Urodyn. 2025 Nov;44(8):1575-1582. doi: 10.1002/nau.70139. Epub 2025 Sep 4.
PubMed 40908659
Study at a glance
From the registry record, last updated by the sponsor on 25 Feb 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Interpretation of Uroflowmetry Samples from Pediatric Patients by Clinicians and Introduction to Artificial Intelligence, and Interpretation of the Samples by Artificial Intelligence
- ClinicalTrials.gov ID
- NCT06814847
- Acronym / sponsor ID
- MAR.UAD.0019
- Conditions
- Voiding Dysfunction, Voiding Disorders, Machine Learning
- Keywords
- Uroflowmetry voiding patterns, Interpretation differences, Machine learning, Artificial intelligence
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 500 participants (actual)
- Lead sponsor
- Marmara UniversityUniversity, hospital or other
- Study start
- 1 Oct 2024 (actual)
- Primary completion
- 1 Jan 2025 (actual)
- Study completion
- 1 Feb 2025 (actual)
- First posted
- 7 Feb 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Uroflowmetry is the one of the most commonly used non-invasive test for evaluating children with lower urinary tract symptoms (LUTS). However, studies have highlighted a weak agreement among experts in interpreting uroflowmetry patterns. This study aims to assess the impact of machine learning models, which have become increasingly prevalent in medicine, on the interpretation of uroflowmetry patterns.
Detailed description
The study included uroflowmetry tests of children aged 4-17 years who were referred to our clinic with lower urinary tract symptoms. Uroflowmetry patterns were independently interpreted by three pediatric urology experts. Discrepancies in interpretations were jointly re-evaluated by the three observers, and a consensus was reached. Voiding volume, voiding duration, and urine flow rates at 0.5-second intervals were converted into numerical data for analysis. Eighty percent of the dataset was used as training data for machine learning, while there maining 20% was reserved for testing. A total of five different machine learning models were employed for classification: Decision Tree, Random Forest, CatBoost, XGBoost, and LightGBM. The models that most accurately identified each uroflowmetry pattern were determined.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Target follow-up
- 3 Months
Who may qualify
- Ages
- 4 to 17 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child
- Study population
- Children aged 4-17
Key inclusion criteria (2 listed)
- Aged between 4 and 17 years with LUTS
- Urinate more than 50% of the expected bladder capacity on UF
Key exclusion criteria (4 listed)
- Patients who were unable to cooperate with the voiding command
- Had neurological disorders
- Urinate less than 50% of the expected bladder capacity on UF
- Under 4 years of age, and were over 18 years of age
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Performance of Machine Learning Models in Evaluating Voiding Patterns
Time frame: From October 2024 to January 2025
5 different machine learning models were used. Accuracy rates were determined for each model.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Feb 2025.
- Derived from this study: PubMed 40908659Arslan F, Algorabi O, Ozkan OC, Turkan YS, Ersin Namli, Genc YE, Sekerci CA, Yucel S, Cam K, Tarcan T. Artificial Intelligence-Based Analysis of Uroflowmetry Patterns in Children: A Machine Learning Perspective. Neurourol Urodyn. 2025 Nov;44(8):1575-1582. doi: 10.1002/nau.70139. Epub 2025 Sep 4.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Voiding Dysfunction and Voiding Disorders, run by Marmara University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 25 Feb 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Performance of Machine Learning Models in Evaluating Voiding Patterns (time frame: From October 2024 to January 2025). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06814847, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Feb 2025.