A survey on artificial intelligence in ophthalmology: keratoconus classification
Keywords:Artificial Intelligence, Machine Learning, Deep Learning, Ophthalmology, Keratoconus
The progressive integration of artificial intelligent tools in ophthalmology can potentially change the fundamental activities and the practices of ophthalmologists. Intelligent systems based on machine learning allow the detection and classification of several diseases such as age-related macular degeneration, glaucoma, diabetic retinopathy and keratoconus with high precision. The dependence between ophthalmology and images processing, given that almost of these diseases are identified by the analysis of the eye topographic maps, represents a point of at-traction for researchers to benefit of capacity and performance of deep learning tools in image processing. These deep learning tools allow a better differentiation between a sick eye and a normal eye and offer several advantages in the detection and classification of different diseases based on the analysis of the eye topographic maps. Among the diseases already mentioned, keratoconus, this non-inflammatory disease characterized by a progressive thinning of the cornea is of-ten accompanied by aspens of vision. This disease has been the subject of several research studies which aim to produce intelligent systems to assist ophthalmologists in the diagnosis and treatment of keratoconus. This paper represents a state of art of the application of artificial intelligence in ophthalmology, particularly in the detection and classification of keratoconus.
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Copyright (c) 2021 Mustapha AATILA, Mohamed LACHGAR, Hamid Hrimech, Ali Kartit
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