Artificial intelligence in ophthalmology. Do we need risk calculators for glaucoma development and progression?-Reference-Cited by-同舟云学术

Artificial intelligence in ophthalmology. Do we need risk calculators for glaucoma development and progression?

Published:2023-05-12 Issue:2 Volume:22 Page:29-37
ISSN:2311-6862
Container-title:National Journal glaucoma
language:
Short-container-title:Glaucoma

Author:

Dorofeev D. A.¹^ORCID,Korelina V. E.²^ORCID,Vitkov A. A.³^ORCID,Kirilik E. V.¹^ORCID,Kuroyedov A. V.⁴,Lukyanova K. O.¹^ORCID,Markelova A. A.⁵^ORCID,Pozdeeva O. G.⁶^ORCID,Khohlova A. S.⁷^ORCID

Affiliation:

1. Chelyabinsk Public Clinical Hospital No. 2, Polyclinic No. 1

2. OOO AMK

3. 3Krasnov Research Institute of Eye Diseases

4. Mandryka Military Clinical Hospital,

5. South Ural State Medical University

6. South Ural State Medical University; Chelyabinsk Public Clinical Hospital No. 2, Polyclinic No. 1

7. Pacific State Medical University

Abstract

Artificial intelligence (AI) is rapidly entering modern medical practice. Many routine clinical tasks, from imaging and automated diagnostics to robotic surgery, cannot be imagined without the use of AI. Neural networks show impressive results when analyzing a large amount of data obtained from standard automated perimetry, optical coherence tomography (OCT) and fundus photography. Currently, both in Russia and abroad mathematical algorithms are being developed that allow detection of glaucoma based on certain signs. This article analyzes the advantages and disadvantages of employing artificial intelligence in ophthalmological practice, discusses the need for careful selection of the criteria and their influence on the accuracy of calculators, considers the specifics of using mathematical analysis in suspected glaucoma, as well as in an already established diagnosis. The article also provides clinical examples of the use of glaucoma risk calculator in the routine practice of an ophthalmologist.

Publisher

Research Institute of Eye Diseases

Reference67 articles.

1. Kucur ŞS, Holló G, Sznitman R. A deep learning approach to automatic detection of early glaucoma from visual fields. PLoS One 2018; 13(11):e0206081. https://doi.org/10.1371/JOURNAL.PONE.0206081

2. Asaoka R, Iwase A, Hirasawa K, Murata H, Araie M. Identifying “preperimetric” glaucoma in standard automated perimetry visual fields. Invest Ophthalmol Vis Sci 2014; 55(12):7814-7820. https://doi.org/10.1167/iovs.14-15120

3. Asaoka R, Murata H, Iwase A, Araie M. Detecting Preperimetric Glaucoma with Standard Automated Perimetry Using a Deep Learning Classifier. Ophthalmology 2016; 123(9):1974-1980. https://doi.org/10.1016/j.ophtha.2016.05.029

4. Mariottoni EB, Jammal AA, Urata CN, Berchuck SI, Thompson AC, Estrela T, Medeiros FA. Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach. Sci Rep 2020; 10(1):402. https://doi.org/10.1038/s41598-019-57196-y.

5. Maetschke S, Antony B, Ishikawa H, Wollstein G, Schuman J, Garnavi R. A feature agnostic approach for glaucoma detection in OCT volumes. PLoS One 2019;14(7):e0219126. https://doi.org/10.1371/JOURNAL.PONE.0219126