Sun-Exposure and Lesion Location Bias in Deep Learning Models for Skin Cancer Detection

Background Deep learning (DL) models have demonstrated high performance in classifying skin lesions from dermoscopic images. However, the influence of photoexposure-related factors, such as level of exposure due to the anatomical site and skin phototype, on classification performance remains understudied. Investigating these factors is essential not only to understand their potential impact on bias in model predictions, but also to explore their potential role as risk indicators for skin cancer. Objective This study aims to assess the impact of skin phototype and anatomical-site–related photoexposure on the performance of DL models for malignancy detection, with a focus on potential sources of bias. Methods DL models are trained on widely used public dermoscopic image datasets. Performance is then evaluated on a recently published dataset of 60 patients from the University Hospital of North Norway, which includes dermoscopic images and clinical data on skin phototype and anatomical site to assess their impact on model performance. Results Preliminary analysis suggests that model performance varies between subgroups, with reduced precision observed in lesions chronically photoexposed. The reactions that cause red and painful skin are associated with better model performance, in addition to being mostly benign lesions. However, this result is skewed by the low incidence of malignant cases. Conclusions The findings highlight that individual sun-related behaviours and skin characteristics can influence the reliability of DL-based skin lesion identification. These results underscore the importance of evaluating model robustness across diverse patient profiles and may guide future efforts to define healthier sun exposure habits for the population.