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Review
. 2022 Dec 29;13(1):100.
doi: 10.3390/diagnostics13010100.

Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions

Nicoleta Anton  1 , Bogdan Doroftei  1 , Silvia Curteanu  2 , Lisa Catãlin  2 , Ovidiu-Dumitru Ilie  3 , Filip Târcoveanu  1 , Camelia Margareta Bogdănici  1
Affiliations
Review

Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions

Nicoleta Anton et al. Diagnostics (Basel). .

Abstract

Background: Having several applications in medicine, and in ophthalmology in particular, artificial intelligence (AI) tools have been used to detect visual function deficits, thus playing a key role in diagnosing eye diseases and in predicting the evolution of these common and disabling diseases. AI tools, i.e., artificial neural networks (ANNs), are progressively involved in detecting and customized control of ophthalmic diseases. The studies that refer to the efficiency of AI in medicine and especially in ophthalmology were analyzed in this review.

Materials and methods: We conducted a comprehensive review in order to collect all accounts published between 2015 and 2022 that refer to these applications of AI in medicine and especially in ophthalmology. Neural networks have a major role in establishing the demand to initiate preliminary anti-glaucoma therapy to stop the advance of the disease.

Results: Different surveys in the literature review show the remarkable benefit of these AI tools in ophthalmology in evaluating the visual field, optic nerve, and retinal nerve fiber layer, thus ensuring a higher precision in detecting advances in glaucoma and retinal shifts in diabetes. We thus identified 1762 applications of artificial intelligence in ophthalmology: review articles and research articles (301 pub med, 144 scopus, 445 web of science, 872 science direct). Of these, we analyzed 70 articles and review papers (diabetic retinopathy (N = 24), glaucoma (N = 24), DMLV (N = 15), other pathologies (N = 7)) after applying the inclusion and exclusion criteria.

Conclusion: In medicine, AI tools are used in surgery, radiology, gynecology, oncology, etc., in making a diagnosis, predicting the evolution of a disease, and assessing the prognosis in patients with oncological pathologies. In ophthalmology, AI potentially increases the patient's access to screening/clinical diagnosis and decreases healthcare costs, mainly when there is a high risk of disease or communities face financial shortages. AI/DL (deep learning) algorithms using both OCT and FO images will change image analysis techniques and methodologies. Optimizing these (combined) technologies will accelerate progress in this area.

Keywords: applications of artificial intelligence in ophthalmology; artificial intelligence; artificial intelligence in medicine; glaucoma and artificial intelligence; neural networks in ophthalmology.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Neural networks—biological and artificial.
Figure 2
Figure 2
A flowchart of the current survey design, strategy, results, and studies that complied with the eligibility criteria.
Figure 3
Figure 3
The theoretical chart of the classification algorithm used in predicting glaucoma.
See this image and copyright information in PMC

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