ABSTRACT
Optical coherence tomography (OCT) has become an established diagnostic technology in the clinical management of age-related macular degeneration (AMD). OCT is being used for primary diagnosis, evaluation of therapeutic efficacy, and long-term monitoring. Computer-based advances in image analysis provide complementary imaging tools such as OCT angiography, further novel automated analysis methods as well as feature detection and prediction of prognosis in disease and therapy by machine learning. In early AMD, pathognomonic features such as drusen, pseudodrusen, and abnormalities of the retinal pigment epithelium (RPE) can be imaged in a qualitative and quantitative way to identify early signs of disease activity and define the risk of progression. In advanced AMD, disease activity can be monitored clearly by qualitative and quantified analyses of fluid pooling, such as intraretinal cystoid fluid, subretinal fluid, and pigment epithelial detachment (PED). Moreover, machine learning methods detect a large spectrum of new biomarkers. Evaluation of treatment efficacy and definition of optimal therapeutic regimens are an important aim in managing neovascular AMD. In atrophic AMD hallmarked by geographic atrophy (GA), advanced spectral domain (SD)-OCT imaging largely replaces conventional fundus autofluorescence (FAF) as it adds insight into the condition of the neurosensory layers and associated alterations at the level of the RPE and choroid. Exploration of imaging features by computerized methods has just begun but has already opened relevant and reliable horizons for the optimal use of OCT imaging for individualized and population-based management of AMD-the leading retinal epidemic of modern times.
