Mp61-02 Snap Diagnosis: Pilot Study of AI-Powered Smartphone Application for Penile Cancer Detection From the Comfort of Your Home

You have accessJournal of UrologyPenile & Testicular Cancer II (MP61)1 May 2024MP61-02 SNAP DIAGNOSIS: PILOT STUDY OF AI-POWERED SMARTPHONE APPLICATION FOR PENILE CANCER DETECTION FROM THE COMFORT OF YOUR HOME Jianliang Liu, Jonathan S. O'Brien, Nandakishor Desai, Marimuthu Palaniswami, and Nathan Lawrentschuk Jianliang LiuJianliang Liu , Jonathan S. O'BrienJonathan S. O'Brien , Nandakishor DesaiNandakishor Desai , Marimuthu PalaniswamiMarimuthu Palaniswami , and Nathan LawrentschukNathan Lawrentschuk View All Author Informationhttps://doi.org/10.1097/01.JU.0001009536.58867.87.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: 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. 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 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. Source of Funding: Grant has been awarded by the Epworth Medical Foundation, and the Australian Chinese Medical Association of Victoria for this project. No conflict of interest to disclose © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e1010 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Jianliang Liu More articles by this author Jonathan S. O'Brien More articles by this author Nandakishor Desai More articles by this author Marimuthu Palaniswami More articles by this author Nathan Lawrentschuk More articles by this author Expand All Advertisement PDF downloadLoading ...