Snap Diagnosis: Developing an Artificial Intelligence Algorithm for Penile Cancer Detection from Photographs.

Simple Summary: Penile cancer is aggressive and rapidly progressive. Early detection is crucial for survival. Many men delay seeking help due to a lack of awareness and fear of embarrassment. This study explored the use of artificial intelligence (AI) in detection of penile cancer. AI was trained on 136 penile lesions images from scientific publications. This included 65 penile cancer, 44 precancerous, and 27 benign images. It performed well in distinguishing between benign lesions and penile cancer with high accuracy. However, it faced challenges in accurately identifying precancerous lesions. These findings suggest that AI has potential in early detection of penile cancer, but more research is needed to refine and validate the AI software with real-life data. The goal is to complement and not replace clinicians. The AI algorithm may allow patients to evaluate any concerning penile lesion from the comfort and privacy of their home, potentially encouraging earlier medical consultation. Background/Objective: Penile cancer is aggressive and rapidly progressive. Early recognition is paramount for overall survival. However, many men delay presentation due to a lack of awareness and social stigma. This pilot study aims to develop a convolutional neural network (CNN) model to differentiate penile cancer from precancerous and benign penile lesions. Methods: The CNN was developed using 136 penile lesion images sourced from peer-reviewed open access publications. These images included 65 penile squamous cell carcinoma (SCC), 44 precancerous lesions, and 27 benign lesions. The dataset was partitioned using a stratified split into training (64%), validation (16%), and test (20%) sets. The model was evaluated using ten trials of 10-fold internal cross-validation to ensure robust performance assessment. Results: When distinguishing between benign penile lesions and penile SCC, the CNN achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.94, with a sensitivity of 0.82, specificity of 0.87, positive predictive value of 0.95, and negative predictive value of 0.72. The CNN showed reduced discriminative capability in differentiating precancerous lesions from penile SCC, with an AUROC of 0.74, sensitivity of 0.75, specificity of 0.65, PPV of 0.45, and NPV of 0.88. Conclusion: These findings demonstrate the potential of artificial intelligence in identifying penile SCC. Limitations of this study include the small sample size and reliance on photographs from publications. Further refinement and validation of the CNN using real-life data are needed. [ABSTRACT FROM AUTHOR]