Abstract
In developed countries, age-related macular degeneration (AMD), a retinal disease, is the main cause of vision loss in the elderly. Optical Coherence Tomography (OCT) is currently the gold standard for assessing individuals for initial AMD diagnosis. In this paper, we look at how OCT imaging can be used to diagnose AMD. Our main aim is to examine and compare automated computer-aided diagnostic (CAD) systems for diagnosing and grading of AMD. We provide a brief summary, outlining the main aspects of performance assessment and providing a basis for current research in AMD diagnosis. As a result, the only viable alternative is to prevent AMD and stop both this devastating eye condition and unwanted visual impairment. On the other hand, the grading of AMD is very important in order to detect early AMD and prevent patients from reaching advanced AMD disease. In light of this, we explore the remaining issues with automated systems for AMD detection based on OCT imaging, as well as potential directions for diagnosis and monitoring systems based on OCT imaging and telemedicine applications.
Highlights
Age-related macular degeneration (AMD) results in vision loss in the central retina, i.e., the macula
We provide a very comprehensive review of most studies that investigated the role of Optical Coherence Tomography (OCT) and other modalities in age-related macular degeneration (AMD) diagnosis
We introduce a review of articles for AMD diagnosis and progression that have been developed based on ML
Summary
Age-related macular degeneration (AMD) results in vision loss in the central retina, i.e., the macula This disease appears most commonly, in developed countries, in people aged 50 years or older [1,2]. Several computer aided diagnosis (CAD) techniques have been used to monitor and control the process of detecting the AMD disease at the early stages [3,4,5,6,7]. These CAD systems are needed to relieve physicians’ workload
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