SynthesisClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2026
Deep learning for early diagnosis of uveal melanoma: a systematic review and meta-analysis.
Synthesis in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundUveal melanoma (UM) is a rare cancer with an estimated annual incidence of 6 incidences per million people. About half of UM patients develop distant metastases, mainly to the liver. After metastasis, prognosis is poor, with median survival under 1 year and limited treatment options. Thus, earlier diagnostic methods for UM are a critical unmet need. The aim of this study was to evaluate the accuracy (sensitivity, specificity, and combined F1 score) of deep learning algorithms in the differential diagnosis of individuals with uveal melanoma.
methodsWe searched PubMed, Scopus, and Web of Science for studies comparing UM patients to healthy individuals or those with ocular nevi using AI diagnostic tools. AI performance metrics were extracted, with clinical or expert-based assessment as the reference standard. The study adhered to PRISMA guidelines.
resultsFive studies comprising 6388 patients (2981 UM, 2563 nevi, and 844 healthy) were included. The mean age of UM patients ranged from 58 to 63.2 years; for nevi, from 58 to 66 years. Pooled sensitivity was 89.0% (95% CI 88.6-89.5%) with no heterogeneity (I
conclusionsWhile fundus imaging is widely used in outpatient care, multimodal imaging remains limited to specialized clinics. Developing software to analyze fundus images could improve early, noninvasive UM detection and offer a cost-effective diagnostic tool.
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