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This multicenter study applied interpretable machine learning to baseline OCT-derived biomarkers to accurately predict the clinical course of central serous chorioretinopathy (CSC). Key OCT parameters—parafoveal outer nuclear layer thickness, perifoveal photoreceptor-RPE complex thickness, and subretinal fluid volumes—emerged as the strongest predictors of CSC chronicity. The algorithm demonstrated high discriminatory power (AUC 0.852), potentially enabling early risk stratification and personalized clinical management.
