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

Abstract

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.

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.

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.

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

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.