Artificial intelligence in retinal image analysis: Development, advances, and challenges

Anthony C Oganov; Ian Seddon; Sayena Jabbehdari; Ogul E Uner; Hossein Fonoudi; Ghasem Yazdanpanah; Oumaima Outani; J Fernando Arevalo

doi:10.1016/j.survophthal.2023.04.001

Artificial intelligence in retinal image analysis: Development, advances, and challenges

Surv Ophthalmol. 2023 Sep-Oct;68(5):905-919. doi: 10.1016/j.survophthal.2023.04.001. Epub 2023 Apr 26.

Authors

Anthony C Oganov¹, Ian Seddon², Sayena Jabbehdari³, Ogul E Uner⁴, Hossein Fonoudi⁵, Ghasem Yazdanpanah⁶, Oumaima Outani⁷, J Fernando Arevalo⁸

Affiliations

¹ Department of Ophthalmology, Renaissance School of Medicine, Stony Brook, NY, USA.
² College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, USA.
³ Jones Eye Institute, University of Arkansas for Medical Sciences, Little Rock, AR, USA. Electronic address: Sjabbehdari@uams.edu.
⁴ Casey Eye Institute, Department of Ophthalmology, Oregon Health and Science University, Portland, OR, USA.
⁵ Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Iranshahr University of Medical Sciences, Iranshahr, Sistan and Baluchestan, Iran.
⁶ Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, IL, USA.
⁷ Faculty of Medicine and Pharmacy of Rabat, Mohammed 5 University, Rabat, Rabat, Morocco.
⁸ Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

PMID: 37116544
DOI: 10.1016/j.survophthal.2023.04.001

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.

Keywords: Artificial Intelligence; FA; Fluorescein angiography; Fundus photography; OCT; Optical coherence tomography; Retina.

Publication types

Review

MeSH terms

Artificial Intelligence
Diabetic Retinopathy* / diagnosis
Fluorescein Angiography / methods
Humans
Macular Edema* / diagnosis
Retina / diagnostic imaging
Retina / pathology
Tomography, Optical Coherence / methods