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
. 2019 Feb;103(2):167-175.
doi: 10.1136/bjophthalmol-2018-313173. Epub 2018 Oct 25.

Artificial intelligence and deep learning in ophthalmology

Daniel Shu Wei Ting  1 , Louis R Pasquale  2 , Lily Peng  3 , John Peter Campbell  4 , Aaron Y Lee  5 , Rajiv Raman  6 , Gavin Siew Wei Tan  7 , Leopold Schmetterer  7   8   9   10 , Pearse A Keane  11 , Tien Yin Wong  7
Affiliations
Review

Artificial intelligence and deep learning in ophthalmology

Daniel Shu Wei Ting et al. Br J Ophthalmol. 2019 Feb.

Abstract

Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI 'black-box' algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward.

Keywords: glaucoma; imaging; public health; retina; telemedicine.

PubMed Disclaimer

Conflict of interest statement

Competing interests: DSWT and TYW are the coinventors of a deep learning system for retinal diseases. LP is a member of Google AI Healthcare. LRP is a non-paid consultant for Visulytix. PAK is a consultant for DeepMind.

Figures

Figure 1
Figure 1
Archetype analysis with 16 visual field (VF) archetypes (ATs) that were derived from an unsupervised computer algorithm described by Elze et al.
Figure 2
Figure 2
Some examples of heat maps showing the abnormal areas in the retina. (A) Severe non-proliferative diabetic retinopathy (NPDR); (B) geographic atrophy in advanced age-related macular degeneration (AMD) on fundus photographs; and (C) diabetic macular oedema on optical coherence tomography.
Figure 3
Figure 3
A representative screenshot from the output of the Moorfields-DeepMind deep learning system for optical coherence tomography segmentation and classification. In this case, the system correctly diagnoses a case of central serous retinopathy with secondary choroidal neovascularisation and recommends urgent referral to an ophthalmologist. Through the creation of an intermediate tissue representation (seen here as two-dimensional thickness maps for each morphological parameter), the system provides ’explainability’ for the ophthalmologist.
See this image and copyright information in PMC

References

    1. World Economic Forum , 2016. The fourth industrial revolution: what it means, how to respond. https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-... (accessed 18 Aug 2018).
    1. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436–44. 10.1038/nature14539 - DOI - PubMed
    1. Zhang X, Zou J, He K, et al. Accelerating very deep convolutional networks for classification and detection. IEEE Trans Pattern Anal Mach Intell 2016;38:1943–55. 10.1109/TPAMI.2015.2502579 - DOI - PubMed
    1. Shin HC, Roth HR, Gao M, et al. Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 2016;35:1285–98. 10.1109/TMI.2016.2528162 - DOI - PMC - PubMed
    1. Tompson J, Jain A, LeCun Y. Joint training of a convolutional network and a graphical model for human pose estimation. Advances in Neural Information Processing Systems 2014;27:1799–807.

Publication types