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Artificial Intelligence in Retinal Image Analysis: Development, Advances, and Challenges Publisher Pubmed



Oganov AC1 ; Seddon I2 ; Jabbehdari S3 ; Uner OE4 ; Fonoudi H5 ; Yazdanpanah G6 ; Outani O7 ; Arevalo JF8
Authors
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Authors Affiliations
  1. 1. Department of Ophthalmology, Renaissance School of Medicine, Stony Brook, NY, United States
  2. 2. College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States
  3. 3. Jones Eye Institute, University of Arkansas for Medical Sciences, Little Rock, AR, United States
  4. 4. Casey Eye Institute, Department of Ophthalmology, Oregon Health and Science University, Portland, OR, United States
  5. 5. Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Iranshahr University of Medical Sciences, Sistan and Baluchestan, Iranshahr, Iran
  6. 6. Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, United States
  7. 7. Faculty of Medicine and Pharmacy of Rabat, Mohammed 5 University, Rabat, Rabat, Morocco
  8. 8. Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States

Source: Survey of Ophthalmology Published:2023


Abstract

Modern advances in diagnostic technologies offer the potential for unprecedented insight into ophthalmic conditions relating to the retina. We discuss the current landscape of artificial intelligence in retina with respect to screening, diagnosis, and monitoring of retinal pathologies such as diabetic retinopathy, diabetic macular edema, central serous chorioretinopathy, and age-related macular degeneration. We review the methods used in these models and evaluate their performance in both research and clinical contexts and discuss potential future directions for investigation, use of multiple imaging modalities in artificial intelligence algorithms, and challenges in the application of artificial intelligence in retinal pathologies. © 2023 Elsevier Inc.
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