Medical-AI trial: predicting high/low risk and subtype of basal cell carcinoma from dermoscopy images with a CNN — compared with dermatologists (NCT07677124)

Basal cell carcinoma (BCC) is the most common type of skin cancer; most grow slowly, but if neglected or incompletely removed they can spread deeply in one area. Planning treatment hinges on distinguishing a higher-grade subtype (high-risk) from a lower one (low-risk). Dermatologists use dermoscopy, a tool that magnifies the skin to see patterns below the surface, but the final subtype is confirmed by pathology of a biopsy.

This study examines whether that distinction can be supported from images. Per the registry summary, it uses clinical photographs and dermoscopy images with a CNN (a deep-learning method strong at image recognition) to evaluate, as the primary objective, the performance of classifying BCC as low- or high-risk.

Secondarily, it predicts the histopathological subtype and compares the model with the assessments of dermatology physicians. Cases are histopathologically confirmed BCC from a dermatology archive, with pathology as the reference standard. It is retrospective, with a target of 2,500, and it is recruiting.

If a high-risk subtype can be anticipated from dermoscopy images before biopsy, it could help plan the excision margin and surgical approach (for example, a technique that checks the borders carefully). Comparing against dermatologists positions AI as an aid to clinical judgment rather than a replacement. That said, this is a retrospective diagnostic-performance study; it does not replace pathology or establish clinical use.