ARTIFICIAL INTELLIGENCE IN EYE DISEASE: AN UPDATE SYSTEMATIC REVIEW
DOI:
https://doi.org/10.61841/pt4dqc90Keywords:
Artificial intelligence, eye disease, ophthalmology, roleAbstract
Background: In recent years, AI has significantly revolutionized the healthcare industry, with deep learning applications being used to identify various illnesses, evaluate cancerous lesions, and determine stroke onset. AI-based systems also have been applied in ophthalmology to address leading eye diseases.
The aim: This study aims to determine the role of artificial intelligence (AI) in eye disease.
Methods: By comparing itself to the standards set by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020, this study was able to show that it met all of the requirements. So, the experts were able to make sure that the study was as up-to-date as it was possible to be. For this search approach, publications that came out between 2014 and 2024 were taken into account. Several different online reference sources, like Pubmed and ScienceDirect, were used to do this. It was decided not to take into account review pieces, works that had already been published, or works that were only half done.
Results: In the PubMed database, the results of our search brought up 157 articles, whereas the results of our search on ScienceDirect brought up 256 articles. The results of the search conducted by title screening yielded a total of 34 articles for PubMed and 28 articles for ScienceDirect. We compiled a total of 16 papers, 10 of which came from PubMed and 6 of which came from ScienceDirect. We excluded 4 review articles, 2 non-full text articles, 3 articles having insufficient outcomes, and 1 article having ineligible subjects. In the end, we included six research that met the criteria.
Conclusion: Our systematic study suggests that AI has a role in the diagnosis or screening of eye disease. AI can be a valuable tool for diabetic retinopathy (DR) screening, glaucoma screening, myopia screening, and diagnosis of dry eye syndrome.
References
Haddad MF, Bakkar MM, Abdo N. Public awareness of common eye diseases in Jordan. BMC Ophthalmol. 2017 Oct 2;17(1).
Rong G, Mendez A, Bou Assi E, Zhao B, Sawan M. Artificial Intelligence in Healthcare: Review and Prediction Case Studies. Vol. 6, Engineering. Elsevier Ltd; 2020. p. 291–301.
Mursch-Edlmayr AS, Ng WS, Diniz-Filho A, Sousa DC, Arnould L, Schlenker MB, et al. Artificial intelligence algorithms to diagnose glaucoma and detect glaucoma progression: Translation to clinical practice. Transl Vis Sci Technol. 2020;9(2):1–21.
Devalla SK, Liang Z, Pham TH, Boote C, Strouthidis NG, Thiery AH, et al. Glaucoma management in the era of artificial intelligence. Vol. 104, British Journal of Ophthalmology. BMJ Publishing Group; 2020. p. 301–11.
Mayro EL, Wang M, Elze T, Pasquale LR. The impact of artificial intelligence in the diagnosis and management of glaucoma. Vol. 34, Eye (Basingstoke). Springer Nature; 2020.
Anton N, Doroftei B, Curteanu S, Catãlin L, Ilie OD, Târcoveanu F, et al. Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions. Vol. 13, Diagnostics. MDPI; 2023.
Hu W, Joseph S, Li R, Woods E, Sun J, Shen M, et al. Population impact and cost-effectiveness of artificial intelligence-based diabetic retinopathy screening in people living with diabetes in Australia: a cost effectiveness analysis. EClinicalMedicine. 2024 Jan 1;67.
Jing D, Liu Y, Chou Y, Jiang X, Ren X, Yang L, et al. Change patterns in the corneal sub-basal nerve and corneal aberrations in patients with dry eye disease: An artificial intelligence analysis. Exp Eye Res. 2022 Feb 1;215.
Lemij HG, Vente C de, Sánchez CI, Vermeer KA. Characteristics of a Large, Labeled Data Set for the Training of Artificial Intelligence for Glaucoma Screening with Fundus Photographs. Ophthalmology Science. 2023 Sep 1;3(3).
Pei X, Yao X, Yang Y, Zhang H, Xia M, Huang R, et al. Efficacy of artificial intelligence-based screening for diabetic retinopathy in type 2 diabetes mellitus patients. Diabetes Res Clin Pract. 2022 Feb 1;184.
Shimizu E, Ishikawa T, Tanji M, Agata N, Nakayama S, Nakahara Y, et al. Artificial intelligence to estimate the tear film breakup time and diagnose dry eye disease. Sci Rep. 2023 Dec 1;13(1).
Tan TE, Schmetterer L, Cheng C y, Wong MBBS CW, M Cheung FRCOphth GC, Saw S m, et al. Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study [Internet]. Vol. 3, Articles Lancet Digit Health. 2021. Available from: www.thelancet.com/
Vought R. Articial Intelligence Analysis of Diabetic Retinopathy in Retinal Screening Events. 2023; Available from: https://doi.org/10.21203/rs.3.rs-2562995/v1
Lin KY, Hsih WH, Lin YB, Wen CY, Chang TJ. Update in the epidemiology, risk factors, screening, and treatment of diabetic retinopathy. Journal of Diabetes Investigation. Blackwell Publishing; 2020.
Bellemo V, Lim G, Rim TH, Tan GSW, Cheung CY, Sadda SV, et al. Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application. Vol. 19, Current Diabetes Reports. Current Medicine Group LLC 1; 2019.
Allison K, Patel D, Alabi O. Epidemiology of Glaucoma: The Past, Present, and Predictions for the Future. Cureus. 2020 Nov 25;
Huang X, Islam MR, Akter S, Ahmed F, Kazami E, Serhan HA, et al. Artificial intelligence in glaucoma: opportunities, challenges, and future directions. Vol. 22, BioMedical Engineering Online. BioMed Central Ltd; 2023.
silva2013.
Zhang C, Zhao J, Zhu Z, Li Y, Li K, Wang Y, et al. Applications of Artificial Intelligence in Myopia: Current and Future Directions. Vol. 9, Frontiers in Medicine. Frontiers Media S.A.; 2022.
Kuo YK, Lin IC, Chien LN, Lin TY, How YT, Chen KH, et al. Dry eye disease: A review of epidemiology in Taiwan, and its clinical treatment and merits. Vol. 8, Journal of Clinical Medicine. MDPI; 2019.
Craig JP, Nichols KK, Akpek EK, Caffery B, Dua HS, Joo CK, et al. TFOS DEWS II Definition and Classification Report. Vol. 15, Ocular Surface. Elsevier Inc.; 2017. p. 276–83.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Journal of Advanced Research in Medical and Health Science (ISSN 2208-2425)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Licensing
Ninety Nine Publication publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
