Utility of artificial intelligence for the diagnosis, prognosis, and management of central serous chorioretinopathy: a narrative review
Fernández-Vigo, José Ignacio Valverde-Megías, Alicia Burgos-Blasco, Bárbara de Moura Ramos, José Joaquim Ly-Yang, Fernando
Fernández-Vigo, José Ignacio
Valverde-Megías, Alicia
Burgos-Blasco, Bárbara
de Moura Ramos, José Joaquim
Ly-Yang, Fernando
Abstract
Central serous chorioretinopathy (CSC) represents a significant cause of visual impairment, particularly in working-age individuals. Despite advances in multimodal imaging and evidence supporting photodynamic therapy (PDT) as the mainstay of chronic CSC, current clinical workflows are still affected by variability in image interpretation, manual quantification, and individualized treatment selection. This narrative review investigates the utility of Artificial Intelligence (AI) in improving the diagnosis, prognosis, and management of CSC. AI-based image analysis, including Optical Coherence Tomography (OCT), Optical Coherence Tomography Angiography (OCTA), and Fundus Fluorescein Angiography (FFA), has demonstrated high diagnostic accuracy across selected datasets and the ability to identify relevant biomarkers, thereby improving efficiency and consistency. Machine learning models demonstrate promising predictive power for subretinal fluid absorption, visual acuity outcomes, and disease recurrence, and identify critical prognostic factors. While emerging, AI-guided treatment strategies hold promise for personalized therapy, particularly for optimizing PDT, laser-based interventions, and follow-up strategies. The integration of AI into clinical decision-making workflows may elevate diagnostic capabilities, especially for non-specialists, and reduce clinical workload. However, the widespread implementation of AI in CSC faces notable challenges, including dataset bias, limited external validation, insufficient representation of differential diagnoses, regulatory complexities, and ethical considerations pertaining to transparency and equitable access. Future directions emphasize integrating multimodal data, fostering global collaborative efforts, and developing robust, generalizable AI models to fully realize AI's potential to enhance patient care and optimize healthcare delivery for CSC.
MIDER Authors
Affiliations
San Carlos Health Research Institute; University of Madrid; Centro Internacional de Oftalmología Avanzada; Sandwell & West Birmingham NHS Trust; et al.
Date
2026-05-28
Type
Article
Collections
Citation
Fernández-Vigo JI, Valverde-Megías A, Burgos-Blasco B, de Moura Ramos JJ, Ly-Yang F. Utility of artificial intelligence for the diagnosis, prognosis, and management of central serous chorioretinopathy: a narrative review. Int J Retina Vitreous. 2026 May 28. doi: 10.1186/s40942-026-00867-6. Epub ahead of print