Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology

L Balyen, Tunde Peto

Research output: Contribution to journalArticle

10 Citations (Scopus)
55 Downloads (Pure)

Abstract

The lifestyle of modern society has changed significantly with the emergence of artificial intelligence (AI), machine learning (ML) and deep learning (DL) technologies recently. There are many innovations concerning novel automated technologies including unmanned plane, autonomous vehicles, face and image recognition, automatic speech recognition, natural language processing, bioinformatics, military settings, drug discovery and toxicology. AI is a multi-dimensional technology with various components such as advanced algorithms, ML and DL. AI, ML, and DL are expected to provide recent automated devices to ophthalmologists for early diagnosing and treating ocular disorders in the near future. In fact, AI, ML, and DL are being used in ophthalmic setting to validate the diagnosis of diseases, read images, corneal topographic mapping, and IOL calculations. Diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are the three most common causes of irreversible blindness on a global scale. Ophthalmic imaging provides a way to diagnose and objectively detect the progression of a number of pathologies including DR, AMD, glaucoma and other ophthalmic disorders. There are two methods of imaging generally used as diagnostic methods in ophthalmic practice, including fundus digital photography and optical coherence tomography (OCT). Particularly, OCT has become the most widely used practical imaging modality in ophthalmology settings across developed world. Changes in population demographics and life style, extension of average lifespan, and the changing pattern of chronic diseases, such as obesity, diabetes mellitus (DM), DR, AMD, and glaucoma create a rising demand for such images. Furthermore, the limitation of availability of retina specialists and trained human graders is a major problem in many countries. Consequently, given the current population growth trends, it is inevitable that analysing of such images is time consuming, costly, and prone to human error. Therefore, the detection and treatment of DR, AMD, glaucoma and other ophthalmic disorders will be inevitable through unmanned automated applications system in the near future. Namely, these challenges may be only resolved through unmanned automated applications of novel analysis methods without the need of ophthalmologists, retina specialists and human graders.
We therefore provide an overview of the potential effects of the current AI, ML and DL methods and their applications on the early detection and treatment of DR, AMD, glaucoma and other ophthalmic diseases.
Original languageEnglish
Pages (from-to)264-272
JournalAsia-Pacific Journal of Ophthalmology
Volume8
Issue number3
DOIs
Publication statusPublished - 01 Jun 2019

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