Introduction: Penile squamous cell carcinoma (PSCC) is a rare disease with devastating psychosocial consequences. Early recognition and treatment are paramount to preserving function and long-term survival. However, many men present with advanced disease due to a lack of awareness, social stigma, and limited access to culturally appropriate care. There is an urgent need to reduce barriers to subspecialist penile cancer care, especially for men from low socioeconomic backgrounds. Machine learning combined with smartphone photography has demonstrated growing utility by outperforming clinicians in diagnosing skin cancer. This project aims to evaluate the accuracy of an artificial intelligence algorithm for stratifying penile lesions. Methods: A Google image search was performed for high-quality colour images of penile lesions in peer-reviewed English language articles with a formal diagnosis discussed within the article. The search terms used were “penile cancer”, “penile squamous cell carcinoma (SCC)”, “penile lesion”, “benign penile lesion”, “penile carcinoma in situ (CIS)”, “penile neoplasm in situ (PeIN)”. Images that fit inclusion criteria were downloaded as JPEG files and categorized as benign, pre-malignant, and penile SCC. Two penile cancer subspecialist urologists independently reviewed all images to confirm categorization. A deep learning algorithm was created to extract and automatically segment images into pixels. Contours of pixel edges were used to generate a “mask” representation of the lesion from normal skin. Features such as lesion elevation, erythema, ulceration, redness, and irregularity were extracted and compared. A hold-out validation methodology was performed for training and internally assessing accuracy. Results: One hundred thirty-eight images from 83 articles were included – 67 invasive PSCC, 44 carcinoma in situ (CIS), and 27 benign. Subspecialist urologist agreement on image categorization was 96%. Ten rounds of algorithm training were performed on 98 randomly assigned images, equating to 980 experiments. A total of 40 images were randomly assigned to the test subset, sequestered from the algorithm, and used for internal accuracy validation. The algorithm demonstrated an overall triage accuracy of 87.5%. Conclusions: We present the first study in English literature to identify the role of artificial intelligence in accurately categorizing penile lesions. Penile lesions beneath non-retractile foreskin were identified as a limitation for image-based PSCC detection. Further image incorporation and validation with clinical images from electronic medical records are underway. An opportunity exists to refine artificial intelligence as an education, triage, and referral optimization tool via a smartphone application. In the future, artificial intelligence may help to lower barriers to accessing sub-specialist penile cancer care globally. SOURCE OF Funding: Self funded
