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Review
. 2023 Nov;261(11):3283-3297.
doi: 10.1007/s00417-023-06052-x. Epub 2023 May 9.

Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Malena Daich Varela  1   2 , Sagnik Sen  2 , Thales Antonio Cabral De Guimaraes  1   2 , Nathaniel Kabiri  1 , Nikolas Pontikos  1   2 , Konstantinos Balaskas  3   4 , Michel Michaelides  5   6
Affiliations
Review

Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Malena Daich Varela et al. Graefes Arch Clin Exp Ophthalmol. 2023 Nov.

Abstract

Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.

Keywords: Age-related macular dystrophy; Artificial intelligence; Diabetic retinopathy; Inherited retinal disease; Retina.

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Conflict of interest statement

The authors alone are responsible for the content and writing of this article. MM consults for MeiraGTx Ltd. All other authors certify that they have no affiliations with or involvement in any organisation or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; expert testimony or patent-licensing arrangements) or non-financial interest (such as personal or professional relationships, affiliations, knowledge, or beliefs) in the subject matter or materials discussed in this manuscript.

Figures

Fig. 1
Fig. 1
Diagram of artificial intelligence algorithms, subfields, and mechanisms
Fig. 2
Fig. 2
A Overview of a convolutional neural network (CNN). The process starts with an input layer, typically an image or video, that gets divided into subsamples and/or pixels and is analysed by multiple convolutional layers that filter, mask, or multiply features and feed the results to a dense neural network of multiple nodes ‘artificial neurons’. Each one represents a characteristic to be analysed (e.g., pixels, diagnoses, age, contrast, etc.) and is connected to hidden layers that sum and analyse all inputs, combining the received stimuli and designing a new one, leading to an improved output layer and final diagnosis. B The process of developing a supervised AI model. First, a training set needs to be created, and these images are used to train the model to interpret the different features; after this, a separate, non-annotated dataset (validation set) is presented to the model to try it, whilst still fine-tuning its configuration; and lastly, the algorithm is tested on new data, evaluating its overall performance
See this image and copyright information in PMC

References

    1. Causes of blindness and vision impairment in and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the right to sight: an analysis for the Global Burden of Disease Study. Lancet Glob Health. 2020;9:e144–e160. doi: 10.1016/S2214-109X(20)30489-7. - DOI - PMC - PubMed
    1. Solebo AL, Teoh L, Rahi J. Epidemiology of blindness in children. Arch Dis Child. 2017;102:853–857. doi: 10.1136/archdischild-2016-310532. - DOI - PubMed
    1. (https://www.cycloneinteractive.com/cyclone/assets/File/digital-universe-...)
    1. Stephens ZD, Lee SY, Faghri F, Campbell RH, Zhai C, Efron MJ et al (2015) Big data: astronomical or genomical? PLoS Biol 13(7):e1002195. 10.1371/journal.pbio.1002195 - PMC - PubMed
    1. Ting DSW, Lin H, Ruamviboonsuk P, et al. Artificial intelligence, the internet of things, and virtual clinics: ophthalmology at the digital translation forefront. Lancet Digit Health. 2020;2:e8–e9. doi: 10.1016/S2589-7500(19)30217-1. - DOI - PubMed