The PENIMA project: artificial intelligence decision-support tool for penile lesions - protocol paper and invitation to participate.

OBJECTIVES

To present and describe the design, data collection framework, and interim cohort characteristics of the PENIMA project, a multicentre initiative establishing a clinically annotated penile lesion image repository to support future artificial intelligence (AI)-driven decision-support development - and to encourage urology and dermatology colleagues working with penile lesions to participate in the project.

PATIENTS AND

METHODS

Prospective participant inclusion commenced in March 2023 across Scandinavian urological and dermatological departments. Patients with penile anatomy undergoing clinically indicated genital examination are invited to participate and provide oral and written consent as defined by local authority regulations. Clinical photographs and structured metadata are collected. Lesion classification is based on a specialist evaluation, clinical follow-up and histopathology when available. Images are pseudonymised and annotated using a secure, containerised Computer Vision Annotation Tool (CVAT) environment. Supervised machine-learning model development using convolutional neural networks is planned with patient-level dataset partitioning and evaluation using standard diagnostic performance metrics. The study was approved by relevant local authorities, and the approval number is 1-10-72-150-22.

RESULTS

As of February 2026, 1125 participants and approximately 7000 images have been included. In all, 40% represented normal anatomical variants. Among lesional cases, 33% are clinically assessed as benign, 13% premalignant, and 8% malignant; 22% of all cases have histopathological verification. Concordance between clinical suspicion and histopathology is highest for penile carcinoma (84.6%, 95% confidence interval 75.0-91.0%).

CONCLUSION

The PENIMA project includes a multicentre, clinically grounded image repository reflecting real-world penile lesion presentations. The feasible and successful infrastructure provides a foundation for future development and validation of image-based clinical decision-support tools.