Deep learning in central serous chorioretinopathy.

Abstract

Less than a decade has passed since deep learning (DL) was first applied in ophthalmology. With tremendous growth in this field since then, DL is expected to transform and enhance the efficiency of traditional ophthalmology practice. Central serous chorioretinopathy (CSC) is a common chorioretinal disorder whose etiopathogenesis remains largely unknown. The diagnosis and management of CSC rely heavily on multimodal imaging data, detailed analysis of which may exceed the capacity of many practices. In this comprehensive review, we examine how DL can address such issues through automated analysis of CSC-related imaging biomarkers, including subretinal fluid, pigment epithelial detachment, subretinal hyperreflective material, hyperreflective foci, retinal pigment epithelium atrophy, ellipsoid zone loss, and choroidal layer, sublayers, vessels, and neovascularization. Their prognostic yield and therapeutic implications are covered as well. We describe how DL enables rapid, noninvasive visualization of choroidal vasculature, a primary source of pathology in CSC, in unprecedented detail. We also review the state-of-the-art DL models designed for automated CSC diagnosis, classification, prognostication, and treatment outcome prediction based on imaging data. We highlight the challenges and gaps in this field, discuss some recommended counter measures, and suggest future research directions.