HypospadiasASHOKANCT05569863
Identification of Hypospadias Parameters Using Digital Photography and Artificial Intelligence
An observational study of Hypospadias, run by Universitas Indonesia.
Trial summary
The goal of this observational study is to develop and validate a digital pattern recognition system based on artificial neural network to determine various parameters in hypospadias. Read more about this study
- Who is it for?
- Ages Up to 18 years, men onlyHealthy volunteers can take part
- Conditions the study is for
- Hypospadias
- How many people
- 1,000 participants
- Where
- No sites listed yet
- Study dates
- 14 Nov 2022to 31 Dec 2023
- How long will I take part?
- 1 Month
- 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 Up to 18 years
- Men only
- Healthy volunteers accepted
- Child, Adult
Study population: The diagnosis of having hypospadias (or not) is established via clinical examination by a pediatric urologist.
Who may be able to join
- Children aged <18 years old
- Suspected of having hypospadias (hypospadias group)
- Diagnosed as not having hypospadias (control group)
Who cannot join
- History of hypospadias repair
- Refusal to participate in the study
Trial locations and contacts
No sites are listed yet.
About this study
Official title: Digital Pattern Recognition for Identification of Various Hypospadias Parameters Based on Digital Photography Using Artificial Neural Network
The goal of this observational study is to develop and validate a digital pattern recognition system based on artificial neural network to determine various parameters in hypospadias. The main question it aims to answer is:
How accurate is the digital pattern recognition system based on artificial neural network to determine various parameters in hypospadias?
Read more
Participants in this study are hypospadias patients aged < 18 years old. The guardian (and the patient, if applicable) will be informed about the study and asked for consent. The digital picture of participants' penis will be taken from different angles according to the predetermined angle.
The clinical characteristics of the photographed penis are then inputted and used to train a customized artificial neural network (ANN). The machine is then used to predict various hypospadias parameters presenting at the patients' penis. The accuracy of the machine is then compared to the measurement done by pediatric urologists.
Read the detailed description
This is a development protocol for a digital pattern recognition program for identification of various hypospadias parameters using artificial neural network. The population for this study are children suspected of having hypospadias who are admitted or referred to hospitals in Indonesia.
The consent for the capture of the images needed in the study will be obtained from the parents of hypospadias patients as part of the standard of care to be used as a clinical reference. The photograph taken for this study were kept in an encrypted database. The inclusion criteria of this study are children aged <18 years old who are suspected of having hypospadias, with age-matched control. Those with history of hypospadias repair or those who refuse to participate in the study are excluded.
The clinical outcomes measured in this study are:
- Hypospadias status: hypospadias or non-hypospadias
- Meatal location: glanular, coronal, distal shaft, proximal shaft, penoscrotal
- Meatal shape: normal, abnormal
- Quality of the urethral plate: good, bad
- Glans diameter: in mm
- Glans shape: normal, abnormal The clinical outcomes were measured preoperatively and 1 month postoperatively
A hypospadias and normal penis image with matched age database was used to train the artificial neural network. Three image aspects of the penis (ventral, dorsal, and lateral aspect which include the glans, shaft, and scrotum) were taken from each subjects. The data was labeled with hypospadias parameters: hypospadias status, meatal location, meatal shape, quality of the urethral plate, glans diameter, and glans shape. The data were uploaded to train the artificial neural network.
The statistical analysis plan will be followed for all clinical outcomes analyses. Standard error, 95% confidence intervals, and P values will be reported whenever possible. Intra and inter rater analysis will be performed using the Fleiss Kappa statistical analysis. The data is deemed to be statistically significant if the p-value is less than 0.05. In addition, accuracy, precision, recall, and f1 score values will be computed to measure the performance of recognition model.
- Conditions
- Hypospadias
- Keywords
- artificial intelligence, digital recognition, digital photography, hypospadias, machine learning
- Medical subject headings
- Hypospadias
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Hypospadias group | Patients with hypospadias diagnosis | |
| Control group | Patients without hypospadias |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of The Digital Pattern Recognition Model
Accuracy of the digital pattern recognition model compared to clinical assessment by pediatric urologists in measuring:
- Hypospadias status: hypospadias or non-hypospadias
- Meatal location: glanular, coronal, distal shaft, proximal shaft, penoscrotal
- Meatal shape: normal, abnormal
- Quality of the urethral plate: good, bad
- Glans diameter: in mm
- Glans shape: normal, abnormal
Measured: 1 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Oct 2022
- Study start14 Nov 2022 (estimated)
- Primary completion1 Jun 2023 (estimated)
- Study completion31 Dec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for HypospadiasStudies of the same condition, open and under way.
- More trials run by Universitas IndonesiaThe sponsor's other registered studies.
- 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
- Target duration
- 1 Month
Sponsor and investigators
- Lead sponsor
- Universitas Indonesia
- Sponsor type
- University, hospital or other
- Collaborators
- University Ghent
- Responsible party
- Principal investigator, Irfan Wahyudi, MD, PhD, Universitas Indonesia
- Investigators
- Irfan Wahyudi, MD, PhD, Principal investigator, Department of Urology, Faculty of Medicine, Universitas Indonesia
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05569863
- Sponsor's protocol ID
- 22-03-0310
- Other IDs
- NKB-1235/UN2.RST/HKP.05.00, KET-413/UN2.F1/ETIK/PPM.00.02
- Acronym
- ASHOKA
- Status last verified
- Oct 2022
- First submitted
- 4 Oct 2022
- Last updated
- 6 Oct 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Turk E, Guven A, Karaca F, Edirne Y, Karaca I. Using the parents' video camera for the follow-up of children who have undergone hypospadias surgery decreases hospital anxiety of children. J Pediatr Surg. 2013 Nov;48(11):2332-5. doi: 10.1016/j.jpedsurg.2013.04.012.
PubMed 24210208Han JH, Lee JH, Jun J, Park MU, Lee JS, Park S, Song SH, Kim KS. Validity and reliability of a home-based, guardian-conducted video voiding test for voiding evaluation after hypospadias surgery. Investig Clin Urol. 2020 Jul;61(4):425-431. doi: 10.4111/icu.2020.61.4.425. Epub 2020 Jun 19.
PubMed 32666000Fernandez N, Lorenzo AJ, Rickard M, Chua M, Pippi-Salle JL, Perez J, Braga LH, Matava C. Digital Pattern Recognition for the Identification and Classification of Hypospadias Using Artificial Intelligence vs Experienced Pediatric Urologist. Urology. 2021 Jan;147:264-269. doi: 10.1016/j.urology.2020.09.019. Epub 2020 Sep 26.
PubMed 32991907
Study at a glance
From the registry record, last updated by the sponsor on 6 Oct 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Digital Pattern Recognition for Identification of Various Hypospadias Parameters Based on Digital Photography Using Artificial Neural Network
- ClinicalTrials.gov ID
- NCT05569863
- Acronym / sponsor ID
- ASHOKA / 22-03-0310
- Conditions
- Hypospadias
- Keywords
- artificial intelligence, digital recognition, digital photography, hypospadias, machine learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Universitas IndonesiaUniversity, hospital or other
- Collaborators
- University Ghent
- Study start
- 14 Nov 2022 (estimated)
- Primary completion
- 1 Jun 2023 (estimated)
- Study completion
- 31 Dec 2023 (estimated)
- First posted
- 6 Oct 2022 (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.
The goal of this observational study is to develop and validate a digital pattern recognition system based on artificial neural network to determine various parameters in hypospadias. The main question it aims to answer is:
How accurate is the digital pattern recognition system based on artificial neural network to determine various parameters in hypospadias?
Participants in this study are hypospadias patients aged < 18 years old. The guardian (and the patient, if applicable) will be informed about the study and asked for consent. The digital picture of participants' penis will be taken from different angles according to the predetermined angle.
The clinical characteristics of the photographed penis are then inputted and used to train a customized artificial neural network (ANN). The machine is then used to predict various hypospadias parameters presenting at the patients' penis. The accuracy of the machine is then compared to the measurement done by pediatric urologists.
Detailed description
This is a development protocol for a digital pattern recognition program for identification of various hypospadias parameters using artificial neural network. The population for this study are children suspected of having hypospadias who are admitted or referred to hospitals in Indonesia.
The consent for the capture of the images needed in the study will be obtained from the parents of hypospadias patients as part of the standard of care to be used as a clinical reference. The photograph taken for this study were kept in an encrypted database. The inclusion criteria of this study are children aged <18 years old who are suspected of having hypospadias, with age-matched control. Those with history of hypospadias repair or those who refuse to participate in the study are excluded.
The clinical outcomes measured in this study are:
- Hypospadias status: hypospadias or non-hypospadias
- Meatal location: glanular, coronal, distal shaft, proximal shaft, penoscrotal
- Meatal shape: normal, abnormal
- Quality of the urethral plate: good, bad
- Glans diameter: in mm
- Glans shape: normal, abnormal The clinical outcomes were measured preoperatively and 1 month postoperatively
A hypospadias and normal penis image with matched age database was used to train the artificial neural network. Three image aspects of the penis (ventral, dorsal, and lateral aspect which include the glans, shaft, and scrotum) were taken from each subjects. The data was labeled with hypospadias parameters: hypospadias status, meatal location, meatal shape, quality of the urethral plate, glans diameter, and glans shape. The data were uploaded to train the artificial neural network.
The statistical analysis plan will be followed for all clinical outcomes analyses. Standard error, 95% confidence intervals, and P values will be reported whenever possible. Intra and inter rater analysis will be performed using the Fleiss Kappa statistical analysis. The data is deemed to be statistically significant if the p-value is less than 0.05. In addition, accuracy, precision, recall, and f1 score values will be computed to measure the performance of recognition model.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Month
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Hypospadias group | – | –Patients with hypospadias diagnosis |
| Control group | – | –Patients without hypospadias |
Who may qualify
- Ages
- Up to 18 years
- Sex
- Male only
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
- Study population
- The diagnosis of having hypospadias (or not) is established via clinical examination by a pediatric urologist.
Key inclusion criteria (3 listed)
- Children aged <18 years old
- Suspected of having hypospadias (hypospadias group)
- Diagnosed as not having hypospadias (control group)
Key exclusion criteria (2 listed)
- History of hypospadias repair
- Refusal to participate in the study
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
Accuracy of The Digital Pattern Recognition Model
Time frame: 1 month
Accuracy of the digital pattern recognition model compared to clinical assessment by pediatric urologists in measuring: 1. Hypospadias status: hypospadias or non-hypospadias 2. Meatal location: glanular, coronal, distal shaft, proximal shaft, penoscrotal 3. Meatal shape: normal, abnormal 4. Quality of the urethral plate: good, bad 5. Glans diameter: in mm 6. Glans shape: normal, abnormal
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Oct 2022.
- Background: PubMed 24210208Turk E, Guven A, Karaca F, Edirne Y, Karaca I. Using the parents' video camera for the follow-up of children who have undergone hypospadias surgery decreases hospital anxiety of children. J Pediatr Surg. 2013 Nov;48(11):2332-5. doi: 10.1016/j.jpedsurg.2013.04.012.
- Background: PubMed 32666000Han JH, Lee JH, Jun J, Park MU, Lee JS, Park S, Song SH, Kim KS. Validity and reliability of a home-based, guardian-conducted video voiding test for voiding evaluation after hypospadias surgery. Investig Clin Urol. 2020 Jul;61(4):425-431. doi: 10.4111/icu.2020.61.4.425. Epub 2020 Jun 19.
- Background: PubMed 32991907Fernandez N, Lorenzo AJ, Rickard M, Chua M, Pippi-Salle JL, Perez J, Braga LH, Matava C. Digital Pattern Recognition for the Identification and Classification of Hypospadias Using Artificial Intelligence vs Experienced Pediatric Urologist. Urology. 2021 Jan;147:264-269. doi: 10.1016/j.urology.2020.09.019. Epub 2020 Sep 26.
- 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.
Irfan Wahyudi, MD, PhD principal investigator
Department of Urology, Faculty of Medicine, Universitas Indonesia
Questions about this trial
What is this clinical trial studying?
An observational study of Hypospadias, run by Universitas Indonesia.
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 3 inclusion and 2 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: status not confirmed, as of the sponsor's last update on 6 Oct 2022. Status can change between updates, so confirm with the study contact before making plans.
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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Kevin Yonathan, MD (+6282247426320, kevinyonathan168@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of The Digital Pattern Recognition Model (time frame: 1 month). 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 NCT05569863, which also lists 3 linked publications.
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 6 Oct 2022.