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Review
. 2022 Aug 24;2(3):100078.
doi: 10.1016/j.aopr.2022.100078. eCollection 2022 Nov-Dec.

Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives

Kai Jin  1 , Juan Ye  1
Affiliations
Review

Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives

Kai Jin et al. Adv Ophthalmol Pract Res. .

Abstract

Background: The ophthalmology field was among the first to adopt artificial intelligence (AI) in medicine. The availability of digitized ocular images and substantial data have made deep learning (DL) a popular topic.

Main text: At the moment, AI in ophthalmology is mostly used to improve disease diagnosis and assist decision-making aiming at ophthalmic diseases like diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), cataract and other anterior segment diseases. However, most of the AI systems developed to date are still in the experimental stages, with only a few having achieved clinical applications. There are a number of reasons for this phenomenon, including security, privacy, poor pervasiveness, trust and explainability concerns.

Conclusions: This review summarizes AI applications in ophthalmology, highlighting significant clinical considerations for adopting AI techniques and discussing the potential challenges and future directions.

Keywords: Age-related macular degeneration; Artificial intelligence; Deep learning; Diabetic retinopathy; Glaucoma; Ophthalmology.

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References

    1. Vasey B., Nagendran M., Campbell B., et al. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med. May 2022;28(5):924–933. doi: 10.1038/s41591-022-01772-9. - DOI - PubMed
    1. Ting D.S.W., Pasquale L.R., Peng L., et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. Feb 2019;103(2):167–175. doi: 10.1136/bjophthalmol-2018-313173. - DOI - PMC - PubMed
    1. Gulshan V., Peng L., Coram M., et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. Dec 13 2016;316(22):2402–2410. doi: 10.1001/jama.2016.17216. - DOI - PubMed
    1. Li Z., He Y., Keel S., et al. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology. Aug 2018;125(8):1199–1206. doi: 10.1016/j.ophtha.2018.01.023. - DOI - PubMed
    1. Grassmann F., Mengelkamp J., Brandl C., et al. A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography. Ophthalmology. Sep 2018;125(9):1410–1420. doi: 10.1016/j.ophtha.2018.02.037. - DOI - PubMed

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