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Accepted for/Published in: JMIR Research Protocols

Date Submitted: Aug 27, 2024
Date Accepted: Jul 28, 2025

The final, peer-reviewed published version of this preprint can be found here:

Deep Learning–Based Pattern Recognition for Detecting Penile Abnormalities: Protocol for Developing a Mobile App for Circumcision Eligibility

Wahyudi I, Utomo CP, Djauzi S, Fathurahman M, Situmorang GR, Rodjani A, Raharja PAR, Yonathan K, Santoso B, Khresna D, Raditya M

Deep Learning–Based Pattern Recognition for Detecting Penile Abnormalities: Protocol for Developing a Mobile App for Circumcision Eligibility

JMIR Res Protoc 2025;14:e65811

DOI: 10.2196/65811

PMID: 40929720

PMCID: 12461175

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Digital Pattern Recognition Using Deep Learning Architectures to Detect Penile Abnormalities: Protocol for the Development of a Mobile Application for Circumcision Eligibility

  • Irfan Wahyudi; 
  • Chandra Prasetyo Utomo; 
  • Samsuridjal Djauzi; 
  • Muhamad Fathurahman; 
  • Gerhard Reinaldi Situmorang; 
  • Arry Rodjani; 
  • Putu Angga Risky Raharja; 
  • Kevin Yonathan; 
  • Budi Santoso; 
  • Dwidian Khresna; 
  • Marco Raditya

ABSTRACT

Background:

Circumcision is a common surgical procedure with significant cultural and medical implications, requiring careful evaluation to identify suitable candidates and minimize risks, especially those associated with penile abnormalities. Despite advancements in healthcare, the accurate diagnosis of these abnormalities remains challenging, particularly in resource-limited settings.

Objective:

This study proposes the development of a digital image recognition system to aid in the early detection of penile abnormalities and determine circumcision eligibility.This study is currently initiating model development phase.

Methods:

Artificial intelligence is developed using deep learning techniques, trained using digital images of the male genitalia taken from three different angles: ventral, dorsal, and lateral sides. Upon completion, the system will be integrated into a mobile application, enabling real-time analysis and decision support. This approach aims to improve diagnostic accuracy and healthcare accessibility, thereby enhancing patient outcomes and clinical decision-making processes. Ethical considerations, including informed consent and data security, will be rigorously maintained throughout the study.

Results:

This study is currently initiating model development phase. The study aims to conclude by December 2024.

Conclusions:

By the end of this study, we anticipate that the proposed system will enhance healthcare accessibility and inform clinical decision-making regarding circumcision suitability, thereby contributing to improved patient outcomes and healthcare delivery.


 Citation

Please cite as:

Wahyudi I, Utomo CP, Djauzi S, Fathurahman M, Situmorang GR, Rodjani A, Raharja PAR, Yonathan K, Santoso B, Khresna D, Raditya M

Deep Learning–Based Pattern Recognition for Detecting Penile Abnormalities: Protocol for Developing a Mobile App for Circumcision Eligibility

JMIR Res Protoc 2025;14:e65811

DOI: 10.2196/65811

PMID: 40929720

PMCID: 12461175

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