It was of great interest to read the two editorials, one paper and a review on the application of artificial intelligence (AI) in ophthalmology [1,2,3,4], and in particular diabetic retinopathy screening. John McCarthy (a computer scientist of renown) first used the phrase AI in 1956. He stated “We propose that a 2-month, 10-man study of AI be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.” We are now belatedly seeing the application of technology in this way.
The first applications of AI in ophthalmology involved the use of artificial neural networks [5] (the building blocks of deep learning) to examine visual field defects. It was then applied to identification of fundal features [6] and with some success to diabetic retinopathy [7] with sensitivities and specificities, which have now been iteratively improved upon. Initially computer processing capacities required segmentation of images but, as was anticipated, increased computing powers have allowed an incremental improvement in performance [8, 9] over the decades from its first descriptions in ophthalmology in the 1990s.
There is no doubt that further improvements in computing hardware and software mean that many diagnostic processes will be taken over by AI, in time, not just diabetic screening. Indeed, any data which is digitised is amenable to AI processing and clinical decision making on EPR data may in fact be an easier goal than processing of variable digital images. Even such areas as communication with patients traditionally involving the human interface, but as a result highly variable, may be improved upon eventually with AI.
References
Grzybowski A, Brona P, Lim G, Ruamviboonsuk P. Tan GSW, Abramoff M, et al. Artificial intelligence for diabetic retinopathy screening: a review. Eye (Lond). 2020;34:451–60.
He J, Cao T, Xu F, Wang S, Tao H, Wu T, et al. Artificial intelligence-based screening for diabetic retinopathy at community hospital. Eye (Lond). 2020;34:572–6.
Rajalakshmi R. The impact of artificial intelligence in screening for diabetic retinopathy in India. Eye (Lond). 2020;34:420–1.
Zhao M, Jiang Y. Great expectations and challenges of artificial intelligence in the screening of diabetic retinopathy. Eye (Lond). 2020;34:418–9.
Mutlukan E, Keating D. Visual field interpretation with a personal computer based neural network. Eye (Lond). 1994;8(Pt 3):321–3.
Sinthanayothin C, Boyce JF, Cook HL, Williamson TH. Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Br J Ophthalmol. 1999;83:902–10.
Gardner GG, Keating D, Williamson TH, Elliott AT. Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br J Ophthalmol. 1996;80:940–4.
Sinthanayothin C, Boyce JF, Williamson TH, Cook HL, Mensah E, Lai S, et al. Automated detection of diabetic retinopathy on digital fundus images. Diabet Med. 2002;19:105–12.
Usher D, Dumskyj M, Himaga M, Williamson TH, Nussey S, Boyce J. Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet Med. 2004;21:84–90.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations
Rights and permissions
About this article
Cite this article
Williamson, T.H. Artificial intelligence in diabetic retinopathy. Eye 35, 684 (2021). https://doi.org/10.1038/s41433-020-0855-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41433-020-0855-7