Identification of Hypospadias Parameters Using Digital Photography and Artificial Intelligence
NCT ID: NCT05569863
Last Updated: 2022-10-06
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
1000 participants
OBSERVATIONAL
2022-11-14
2023-12-31
Brief Summary
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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.
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Detailed Description
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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:
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 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
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Hypospadias group
Patients with hypospadias diagnosis
No interventions assigned to this group
Control group
Patients without hypospadias
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
* Suspected of having hypospadias (hypospadias group)
* Diagnosed as not having hypospadias (control group)
Exclusion Criteria
* Refusal to participate in the study
18 Years
MALE
Yes
Sponsors
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University Ghent
OTHER
Indonesia University
OTHER
Responsible Party
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Irfan Wahyudi, MD, PhD
Dr. dr. Irfan Wahyudi, Sp.U(K)
Principal Investigators
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Irfan Wahyudi, MD, PhD
Role: PRINCIPAL_INVESTIGATOR
Department of Urology, Faculty of Medicine, Universitas Indonesia
Central Contacts
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References
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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.
Han 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.
Fernandez 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.
Other Identifiers
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NKB-1235/UN2.RST/HKP.05.00
Identifier Type: OTHER_GRANT
Identifier Source: secondary_id
KET-413/UN2.F1/ETIK/PPM.00.02
Identifier Type: OTHER
Identifier Source: secondary_id
22-03-0310
Identifier Type: -
Identifier Source: org_study_id
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