BRIEF COMMUNICATION
Is Artificial Intelligence an accurate tool for improving access to ophthalmological services in rural areas? A narrative review
 
More details
Hide details
1
Faculty of Medicine, Collegium Medicum, Cardinal Stefan Wyszynski University, Warsaw, Poland
 
2
Faculty of Medicine, Jagiellonian University Medical College, Kraków, Poland
 
3
Faculty of Medicine, Lazarski University, Warsaw, Poland
 
 
Corresponding author
Olga Adamska   

Faculty of Medicine, Collegium Medicum,, Cardinal Stefan Wyszynski University, Warsaw, Poland, Woycickiego 1/3, 01-938, Warszawa, Poland
 
 
 
KEYWORDS
TOPICS
ABSTRACT
Introduction:
The integration of artificial intelligence (AI) in ophthalmology, specifically through the use of Optical Coherence Tomography (OCT) images, has marked a significant advancement in the detection and management of ocular diseases. The article compares the detection of eye conditions by health professionals using Optical Coherence Tomography (OCT) with AI abilities.

Review Methods:
Online databases were searched for articles discussing the effectiveness of AI in OCT analyses and assessment of the accuracy and agreement of AI algorithms with human experts. Key words included ‘OCT’, ‘AI’, ‘comparison’ and ‘effectiveness’’.

Results:
AI algorithms have demonstrated the capability to automatically segment retinal layers, detect and quantify pathological changes, and predict disease progression. The application of AI helps address the challenge of artifacts in OCT images, enhancing the accuracy of tissue structure segmentation and improving diagnostic precision.

Conclusions:
This article explores the comparative effectiveness of AI and human experts in diagnosing ocular conditions using OCT, highlighting AI’s potential to complement human expertise and improve patient outcomes. Despite the promising results, variability in AI performance across different studies underscores the need for more robust and standardized AI models, along with high-quality, diverse datasets to ensure consistent and generalizable results.

REFERENCES (16)
1.
About Common Eye Disorders and Diseases. (b. d.). Vision and Eye Health. [Internet]. [access 2024 Aug 25]. Available from: https://www.cdc.gov/vision-hea....
 
2.
Swenor BK, Ehrlich JR. Ageing and vision loss: looking to the future. Lancet Glob Health. 2021;9(4):e385-e386. doi:10.1016/S2214-109X(21)00031-0.
 
3.
Monaco W, Monaco W, Perez Martinez JP. Prevention of Vision Loss and Blindness Among Nursing Home Residents. J Am Med Dir Assoc. 2021;22(3):B23. doi:10.1016/j.jamda.2021.01.054.
 
4.
Bastawrous A, Suni AV. Thirty Year Projected Magnitude (to 2050) of Near and Distance Vision Impairment and the Economic Impact if Existing Solutions are Implemented Globally. Ophthalmic Epidemiol. 2020;27(2):115–120. doi:10.1080/09286586.2019.170053.
 
5.
Kamińska A, Pinkas J, Wrześniewska-Wal I, Ostrowski J, Jankowski M. Awareness of Common Eye Diseases and Their Risk Factors-A Nationwide Cross-Sectional Survey among Adults in Poland. Int J Environ Res Public Health. 2023;20(4):3594. Published 2023 Feb 17. doi:10.3390/ijerph20043594.
 
6.
Burton MJ, Ramke J, Marques AP, et al. The Lancet Global Health Commission on Global Eye Health: vision beyond 2020. Lancet Glob Health. 2021;9(4):e489-e551. doi:10.1016/S2214-109X(20)30488-5.
 
7.
Monika M, Durajczyk M. Evaluation of the Prevalence of Refractive Defects and Ocular Function in a Group of 1518 Children Aged 8 Years in Northwestern Poland—A Retrospective Study. J Clin Med. 2023;12(8):2880. https://doi.org/10.3390/jcm120....
 
8.
Kamiński M, Jankowski M, Adamska O, Pinkas J, Kamińska A. An analysis of health policy programmes on eye health implemented by Local Government Units (LGUs) in Poland, 2015–2023. Ann Agric Environ Med. 2024. doi:10.26444/aaem/188504.
 
9.
Flaxman SR, Bourne RRA, Resnikoff S, et al. Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis. Lancet Glob Health. 2017;5(12):e1221-e1234. doi:10.1016/S2214-109X(17)30393-5.
 
10.
Ambrosio R. Artificial intelligence for the early detection of ectatic corneal diseases. Ophthalmology Times. [Internet]. [access 2024 Aug 25]. Available from: https://www.ophthalmologytimes....
 
11.
Dahrouj M, Miller JB. Artificial Intelligence (AI) and Retinal Optical Coherence Tomography (OCT). Semin Ophthalmol. 2021 May 19;36(4):341–345. doi:10.1080/08820538.2021.1901123. Epub 2021 Mar 18. PMID: 33734928.
 
12.
Restrepo D, Quion JM, Do Carmo Novaes F, Azevedo Costa ID, Vasquez C, Bautista AN, Quiminiano E, Lim PA, Mwavu R, Celi LA, Nakayama LF. Ophthalmology Optical Coherence Tomography Databases for Artificial Intelligence Algorithm: A Review. Semin Ophthalmol. 2024 Apr;39(3):193–200. doi:10.1080/08820538.2024.2308248. Epub 2024 Feb 9. PMID: 38334303.
 
13.
Midena E, Toto L, Frizziero L, Covello G, Torresin T, Midena G, Danieli L, Pilotto E, Figus M, Mariotti C, Lupidi M. Validation of an Automated Artificial Intelligence Algorithm for the Quantification of Major OCT Parameters in Diabetic Macular Edema. J Clin Med. 2023 Mar 9;12(6):2134. doi:10.3390/jcm12062134. PMID: 36983137; PMCID: PMC10057946.
 
14.
Bai J, Wan Z, Li P, Chen L, Wang J, Fan Y, Chen X, Peng Q, Gao P. Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening. Front Cell Dev Biol. 2022 Nov 3;10:1053483. doi:10.3389/fcell.2022.1053483. PMID: 36407116; PMCID: PMC9670537.
 
15.
Mohammadpour M, Heidari Z, Hashemi H, Yaseri M, Fotouhi A. Comparison of Artificial Intelligence-Based Machine Learning Classifiers for Early Detection of Keratoconus. Eur J Ophthalmol. 2022 May;32(3):1352–1360. doi:10.1177/11206721211073442. Epub 2022 Jan 21. PMID: 35060771.
 
16.
Lin H, Li R, Liu Z, Chen J, Yang Y, Chen H, et al. Diagnostic Efficacy and Therapeutic Decision-making Capacity of an Artificial Intelligence Platform for Childhood Cataracts in Eye Clinics: A Multicentre Randomized Controlled Trial. E Clinical Medicine. 2019 Mar 17;9:52–59. doi:10.1016/j.eclinm.2019.03.001. PMID: 31143882; PMCID: PMC6510889.
 
eISSN:1898-2263
ISSN:1232-1966
Journals System - logo
Scroll to top