Artificial Intelligence-Driven Differentiation Between Uveal Melanoma and Nevus Based on Fundus Photographs: A Systematic Review and Meta-Analysis.

[BACKGROUND] Distinguishing uveal melanoma (UM) from uveal nevus (UN) is often challenging yet crucial for appropriate management. Machine learning (ML), particularly deep learning (DL), has emerged as a promising solution to this binary classification task. This study aimed to examine the ability of artificial intelligence (AI) models to differentiate UM from UN based on fundus photographs.

[METHODS] We systematically searched four databases through July 6, 2025, for studies developing ML models for distinguishing UM from UN using fundus photographs as input. The risk of bias and applicability concerns were assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. The results of primary studies were pooled using random-effects meta-analysis.

[RESULTS] Our review included seven articles with 6208 participants in total. Six studies used DL and one applied conventional ML. Only two studies conducted external validation. The proposed algorithms demonstrated a strong performance, achieving a pooled area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of 0.915, 85.3%, 83.7%, and 87.7%, respectively. In subgroup analysis, externally validated models retained a promising discriminative ability with a subtotal pooled AUC of 0.873.

[CONCLUSIONS] ML algorithms showcase consistently high performance in differentiating UM from UN based on fundus photographs, supporting their potential role as adjunctive tools in clinical practice. Their objective, reproducible assessments may improve referrals, guide clinical decision-making, and boost diagnostic confidence across health care providers. However, existing evidence is highly heterogeneous and constrained by small dataset sizes and limited external validation. Addressing these gaps through multicenter collaborations and data-sharing initiatives could yield more accurate, robust, and generalizable models.

[TRANSLATIONAL RELEVANCE] This work bridges computational research and ophthalmologic care by demonstrating the potential of AI-based analysis of fundus photographs to assist in differentiating uveal melanocytic tumors.