Development of a deep learning model to classify choroidal melanoma risk factors based on color fundus photographs

Abstract

Choroidal melanoma is the most common malignant primary intraocular tumor and can develop either de novo or from a preexisting choroidal nevus, a benign pigmented lesion. Key risk factors for the transformation of choroidal nevus into melanoma include tumor diameter > 5 mm, tumor thickness > 2 mm, orange pigment, subretinal fluid, and low internal reflectivity on ultrasound. However, the assessment of many of these risk factors requires multimodal imaging equipment and skilled subspecialists, only available at tertiary referral centers. In this study, we developed and validated a deep learning approach to identifying these risk factors based solely on fundus images of choroidal nevi. Results indicate acceptable to excellent predictive performance for detection of all five risk factors. These findings suggest that deep learning models may be valuable tools for identifying high-risk choroidal nevi, particularly in resource-limited settings.