Publications

Uveal melanoma (UM) is the most common intraocular malignancy in adults, with high metastatic risk and poor prognosis. Current screening and triaging methods for melanocytic choroidal tumors face inherent limitations, particularly in regions with limited access to specialized ocular oncologists. This systematic review and meta-analysis evaluated artificial intelligence-driven approaches for differentiating uveal melanoma from nevus based on fundus photographs. Analysis included machine learning models with pooled sensitivity of 85% (95% CI 82–87%), specificity of 86% (82–88%), and a C-index of 0.87 (0.84–0.90), with convolutional neural networks as the main method used. Deep learning models achieved AUC scores of 94-95%, outperforming ophthalmologists using standard risk assessment criteria.