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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
Summary
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.
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.
