NCT05569863

Brief 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. 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.

Trial Health

35
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
1,000

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Nov 2022

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

October 4, 2022

Completed
2 days until next milestone

First Posted

Study publicly available on registry

October 6, 2022

Completed
1 month until next milestone

Study Start

First participant enrolled

November 14, 2022

Completed
7 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

June 1, 2023

Completed
7 months until next milestone

Study Completion

Last participant's last visit for all outcomes

December 31, 2023

Completed
Last Updated

October 6, 2022

Status Verified

October 1, 2022

Enrollment Period

7 months

First QC Date

October 4, 2022

Last Update Submit

October 4, 2022

Conditions

Hypospadias

Keywords

artificial intelligencedigital recognitiondigital photographyhypospadiasmachine learning

Outcome Measures

Primary Outcomes (1)

  • Accuracy of The Digital Pattern Recognition Model

    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

    1 month

Study Arms (2)

Hypospadias group

Patients with hypospadias diagnosis

Control group

Patients without hypospadias

Eligibility Criteria

AgeUp to 18 Years
Sexmale
Healthy VolunteersYes
Age GroupsChild (0-17), Adult (18-64)
Sampling MethodNon-Probability Sample
Study Population

The diagnosis of having hypospadias (or not) is established via clinical examination by a pediatric urologist.

You may qualify if:

  • Children aged \<18 years old
  • Suspected of having hypospadias (hypospadias group)
  • Diagnosed as not having hypospadias (control group)

You may not qualify if:

  • History of hypospadias repair
  • Refusal to participate in the study

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Related Publications (3)

  • 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.

    PMID: 24210208BACKGROUND

  • 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.

    PMID: 32666000BACKGROUND

  • 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.

    PMID: 32991907BACKGROUND

MeSH Terms

Conditions

Hypospadias

Condition Hierarchy (Ancestors)

Urogenital AbnormalitiesFemale Urogenital DiseasesFemale Urogenital Diseases and Pregnancy ComplicationsUrogenital DiseasesPenile DiseasesGenital Diseases, MaleGenital DiseasesMale Urogenital DiseasesCongenital AbnormalitiesCongenital, Hereditary, and Neonatal Diseases and Abnormalities

Study Officials

  • Irfan Wahyudi, MD, PhD

    Department of Urology, Faculty of Medicine, Universitas Indonesia

    PRINCIPAL INVESTIGATOR

Central Study Contacts

Kevin Yonathan, MD

CONTACT

+6282247426320kevinyonathan168@gmail.com

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Target Duration
1 Month
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Dr. dr. Irfan Wahyudi, Sp.U(K)

Study Record Dates

First Submitted

October 4, 2022

First Posted

October 6, 2022

Study Start

November 14, 2022

Primary Completion

June 1, 2023

Study Completion

December 31, 2023

Last Updated

October 6, 2022

Record last verified: 2022-10

Data Sharing

IPD Sharing
Will not share