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
. 2020 Feb;33(1):106-110.
doi: 10.1097/WCO.0000000000000773.

Artificial intelligence for detection of optic disc abnormalities

Dan Milea  1   2   3 , Shweta Singhal  1   2   3   4 , Raymond P Najjar  2   3
Affiliations
Review

Artificial intelligence for detection of optic disc abnormalities

Dan Milea et al. Curr Opin Neurol. 2020 Feb.

Abstract

Purpose of review: The aim of this review is to highlight novel artificial intelligence-based methods for the detection of optic disc abnormalities, with particular focus on neurology and neuro-ophthalmology.

Recent findings: Methods for detection of optic disc abnormalities on retinal fundus images have evolved considerably over the last few years, from classical ophthalmoscopy to artificial intelligence-based identification methods being applied to retinal imaging with the aim of predicting sight and life-threatening complications of underlying brain or optic nerve conditions.

Summary: Artificial intelligence and in particular newly developed deep-learning systems are playing an increasingly important role for the detection and classification of acquired neuro-ophthalmic optic disc abnormalities on ocular fundus images. The implementation of automatic deep-learning methods for detection of abnormal optic discs, coupled with innovative hardware solutions for fundus imaging, could revolutionize the practice of neurologists and other non-ophthalmic healthcare providers.

PubMed Disclaimer

References

    1. Tham YC, Li X, Wong TY, et al. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. Ophthalmology 2014; 121:2081–2090.
    1. Devalla SK, Liang Z, Pham TH, et al. Glaucoma management in the era of artificial intelligence. Br J Ophthalmol 2019; pii: bjophthalmol-2019-315016. doi: 10.1136/bjophthalmol-2019-315016. [Epub ahead of print] Review. - DOI
    1. Ting DS, Peng L, Varadarajan AV, et al. Deep learning in ophthalmology: the technical and clinical considerations. Prog Retin Eye Res 2019; 72:100759.
    1. Ting DS, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 2019; 103:167–175.
    1. Grzybowski A, Brona P, Lim G, et al. Artificial intelligence for diabetic retinopathy screening: a review. Eye Lond Engl 2019; doi: 10.1038/s41433-019-0566-0. [Epub ahead of print] Review. - DOI

Publication types