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Updated: Jun 14, 2026

Microscopic Replantation of Penile Glans Amputation Due to Circumcision
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Deep Learning-Based Pattern Recognition for Detecting Penile Abnormalities: Protocol for Developing a Mobile App for

Irfan Wahyudi1, Chandra Prasetyo Utomo2, Samsuridjal Djauzi3

  • 1Department of Urology, Faculty of Medicine, Universitas Indonesia - Cipto Mangunkusumo Hospital, Jakarta, Indonesia.

JMIR Research Protocols
|September 10, 2025
PubMed
Summary
This summary is machine-generated.

This study developed an AI mobile app to detect penile abnormalities, aiding circumcision decisions in underserved areas. The system supports early diagnosis and referral, improving access to specialized care.

Keywords:
artificial intelligencecircumcisionmobile apppenile abnormalitiesprospective cohort study

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Area of Science:

  • Pediatric Urology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Circumcision is common, but penile abnormalities can contraindicate the procedure.
  • Limited access to pediatric urologists in low-resource settings causes diagnostic delays.
  • Artificial intelligence (AI) offers a scalable solution for early detection of penile abnormalities.

Purpose of the Study:

  • Develop and validate an AI-powered image classification system for detecting penile abnormalities.
  • Integrate the AI system into a mobile app for preliminary screening.
  • Support general practitioners and caregivers in underserved areas for informed circumcision decisions.

Main Methods:

  • Prospective cohort study at Cipto Mangunkusumo Hospital, Jakarta.
  • Collected high-resolution penile images (ventral, dorsal, lateral angles).
  • Developed AI models using deep learning (TensorFlow, Keras) with transfer learning; evaluated using accuracy, sensitivity, specificity, and F1-score.

Main Results:

  • AI model development is ongoing, with preliminary models trained.
  • Integration into a mobile app and deployment testing are planned through January 2026.
  • Refinement is underway to enhance diagnostic accuracy and usability.

Conclusions:

  • AI-based system shows promise for early diagnosis of penile abnormalities in resource-limited settings.
  • Mobile app integration facilitates preliminary screening outside specialized centers.
  • The system supports telemedicine and optimizes referral pathways for pediatric urological care.