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In Current drug targets

Retinal diseases affect an increasing number of patients worldwide because of the aging population. Request for diagnostic imaging in ophthalmology is ramping up, while the number of specialists keeps shrinking. Cutting-edge technology embedding artificial intelligence (AI) algorithms is thus advocated to help ophthalmologists perform their clinical tasks as well as a source for novel biomarkers. In particular, optical coherence tomography (OCT) evaluation of the retina can be augmented by algorithms based on machine learning and deep learning to early detect, qualitatively localize and quantitatively measure epi/intra/subretinal abnormalities or pathological features of macular or neural diseases. In this paper, we discuss the use of AI to facilitate efficacy and accuracy of retinal imaging in those diseases increasingly treated by intravitreal vascular endothelial growth factor (VEGF) inhibitors (i.e. anti-VEGF drugs), also including integration and interpretation features in the process. We review recent advances by AI in diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity that envision a potentially key role of highly automated systems in screening, early diagnosis, grading and individualized therapy. We discuss opportunities and critical aspects of automating the evaluation of disease activity, recurrences, timing of retreatment and therapeutically potential novel targets in ophthalmology. Impact of a massive employment of AI to optimize clinical assistance and encourage tailored therapies for distinct patterns of retinal diseases is also discussed.

Sorrentino Francesco Saverio, Jurman Giuseppe, De Nadai Katia, Campa Claudio, Furlanello Cesare, Parmeggiani Francesco


Retinal diseases, anti-VEGF drugs, artificial intelligence, deep learning, machine learning, macular complications, optical coherence tomography, retinal imaging