Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2023 May 1;34(3):261-266.
doi: 10.1097/ICU.0000000000000939. Epub 2022 Dec 29.

Assistive applications of artificial intelligence in ophthalmology

Donald C Hubbard  1 , Parker Cox  2 , Travis K Redd  1
Affiliations
Review

Assistive applications of artificial intelligence in ophthalmology

Donald C Hubbard et al. Curr Opin Ophthalmol. .

Abstract

Purpose of review: Assistive (nonautonomous) artificial intelligence (AI) models designed to support (rather than function independently of) clinicians have received increasing attention in medicine. This review aims to highlight several recent developments in these models over the past year and their ophthalmic implications.

Recent findings: Artificial intelligence models with a diverse range of applications in ophthalmology have been reported in the literature over the past year. Many of these systems have reported high performance in detection, classification, prognostication, and/or monitoring of retinal, glaucomatous, anterior segment, and other ocular pathologies.

Summary: Over the past year, developments in AI have been made that have implications affecting ophthalmic surgical training and refractive outcomes after cataract surgery, therapeutic monitoring of disease, disease classification, and prognostication. Many of these recently developed models have obtained encouraging results and have the potential to serve as powerful clinical decision-making tools pending further external validation and evaluation of their generalizability.

PubMed Disclaimer

Conflict of interest statement

Conflicts of interest

None.

References

    1. Ting DSW, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019. Feb;103(2):167–175. - PMC - PubMed
    1. Tiwari M, Piech C, Baitemirova M, et al. Differentiation of Active Corneal Infections from Healed Scars Using Deep Learning. Ophthalmology. 2022. Feb;129(2):139–146. - PMC - PubMed

    2. This model in this study was able to differentiate between corneal ulcers and scars with high accuracy when tested with images from multiple patient populations. The results of this study suggest that this model may have good generalizability to diverse patient populations, which could help guide decisions regarding when to stop antimicrobial therapy for corneal ulcers

    1. Alquran H, Al-Issa Y, Alsalatie M, et al. Intelligent Diagnosis and Classification of Keratitis. Diagnostics (Basel). 2022. May 28;12(6):1344. - PMC - PubMed
    1. Redd TK, Prajna NV, Srinivasan M, et al. Image-Based Differentiation of Bacterial and Fungal Keratitis Using Deep Convolutional Neural Networks. Ophthalmol Sci. 2022. Jan 29;2(2):100119. - PMC - PubMed

    2. This study showed a deep learning model outperformed cornea specialists in distinguishing determining etiologies of keratitis. Similar models may enable earlier initiation of directed antimicrobial therapy for infectious keratitis, improving visual outcomes in this common and potentially blinding disease.

    1. Liu X, Ali TK, Singh P, et al. Deep Learning to Detect OCT-derived Diabetic Macular Edema from Color Retinal Photographs: A Multicenter Validation Study. Ophthalmol Retina. 2022. May;6(5):398–410. - PubMed

    2. The model created in this study was able to detect diabetic macular edema from fundus photos in a diverse set of patients with reasonable sensitivity and specificity. This information could be used to assist clinical decision making regarding whether or not to pursue anti-VEGF injections as part of treatment.