Artificial Intelligence in Decoding Ocular Enigmas: A Literature Review of Choroidal Nevus and Choroidal Melanoma Assessment - Konstantina-Eleni Karamanli; Eirini Maliagkani; Petros Petrou; Elpiniki Papageorgiou; Ilias Georgalas (Applied Sciences)
This review examines the role of artificial intelligence (AI) in differentiating choroidal nevus (CN) from choroidal melanoma (CM), focusing on diagnosis, classification, segmentation, and prediction of malignant transformation. A literature search was performed in PubMed, Google Scholar, and Science Direct up to December 2024. Eight studies met all the inclusion criteria, evaluating machine learning (ML) and deep learning (DL) applications for CN and CM assessment using various ophthalmic imaging modalities. Performance varied across AI models. U-Net achieved 100% sensitivity and an AUC of 0.88, while DenseNet121 reached an AUC of 0.9781, LASSO logistic regression an AUC of 0.88, RETFound (a self-supervised learning model) had an AUCROC of 0.92, and ResNet50 an accuracy of 92.65% in classification tasks. DeepLabv3 achieved Dice scores of 0.87 (melanoma) and 0.81 (nevi) in lesion-based segmentation, while nnU-Net yielded a Dice score of 0.78 and a recall of 0.77 for pigmented lesion segmentation. SAINT (XGBoost-based) demonstrated a strong predictive performance (AUC: 0.910), confirming its effectiveness in ophthalmic imaging. These results highlight the potential of AI models as effective diagnostic tools in ocular oncology. However, further research is needed to improve model generalizability, enhance dataset diversity, and facilitate clinical integration.