Exploring the possibilities of an artificial intelligence program in the diagnosis of macular diseases

Cover Image

Cite item

Full Text

Abstract

BACKGROUND: Macular diseases are a large group of pathological conditions that cause vision loss and visual impairment. Early diagnosis of such changes plays an important role in treatment selection and is one of the crucial factors in predicting outcomes.

AIM: To examine the potential of an artificial intelligence program in the diagnosis of macular diseases using structural optical coherence tomography scans.

MATERIALS AND METHODS: The study included patients examined and treated at the Federal Research and Clinical Center of Specialized Medical Care and Medical Technologies and Moscow Regional Research and Clinical Institute. In total, 200 eyes with macular diseases were examined, as well as eyes without macular pathologies. A comparative clinical analysis of structural optical coherence tomography scans obtained using an RTVue XR 110-2 tomograph was conducted. The Retina.AI software was used to analyze optical coherence tomography scans.

RESULTS: In the analysis of optical coherence tomography scans using Retina.AI, various pathological structures of the macula were identified, and a probable pathology was then determined. The results were compared with the diagnoses made by ophthalmologists. The sensitivity, specificity, and accuracy of the method were 95.16%, 97.76%, and 97.38%, respectively.

CONCLUSION: Retina.AI allows ophthalmologists to automatically analyze optical coherence tomography scans and identify various pathological conditions of the fundus.

About the authors

Margarita R. Khabazova

Federal Research and Clinical Center of Specialized Medical Care and Medical Technologies

Email: rita.khabazova@mail.ru
ORCID iD: 0000-0002-7770-575X
SPIN-code: 2736-9089
Russian Federation, Moscow

Elena N. Ponomareva

Federal Research and Clinical Center of Specialized Medical Care and Medical Technologies

Email: ponomareva.en@fnkc-fmba.ru
ORCID iD: 0009-0001-0828-9844
SPIN-code: 7868-4425
Russian Federation, Moscow

Igor A. Loskutov

Moscow Regional Research and Clinical Institute

Email: loskoutigor@mail.ru
ORCID iD: 0000-0003-0057-3338
SPIN-code: 5845-6058

MD, Dr. Sci. (Medicine)

Russian Federation, Moscow

Evgenia А. Katalevskaya

Digital Vision Solutions LLC

Email: ekatalevskaya@mail.ru
ORCID iD: 0000-0002-5710-9205
SPIN-code: 7849-8890

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

Alexander Yu. Sizov

Digital Vision Solutions LLC; Nizhny Novgorod State Technical University n.a. R.E. Alekseev

Email: sizov_ost_vk@mail.ru
ORCID iD: 0000-0003-3338-4015
SPIN-code: 4468-1730
Russian Federation, Moscow; Nizhny Novgorod

Georgiy М. Gabaraev

Federal Research and Clinical Center of Specialized Medical Care and Medical Technologies

Author for correspondence.
Email: geor_gabaraev1@mail.ru
ORCID iD: 0000-0002-0759-3107
SPIN-code: 1802-3224
Russian Federation, Moscow

References

  1. Report of the 2030 targets on effective coverage of eye care [Internet]. Geneva: World Health Organization. c2024. [cited 2023 Jan 1]. Available from: https://www.who.int/publications/i/item/9789240058002
  2. GBD 2019 Blindness and Vision Impairment Collaborators. Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study. Lancet Glob Health. 2021;9(2):144–160. doi: 10.1016/S2214-109X(20)30489-7
  3. Samanta A, Aziz AA, Jhingan M, et al. Emerging Therapies in Neovascular Age-Related Macular Degeneration in 2020. Asia Pac J Ophthalmol (Phila). 2020;9(3):250–259. doi: 10.1097/APO.0000000000000291
  4. Stahl A. The Diagnosis and Treatment of Age-Related Macular Degeneration. Dtsch Arztebl Int. 2020;117:513–520. doi: 10.3238/arztebl.2020.0513
  5. Teo ZL, Tham YC, Yu M, et al. Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: Systematic Review and Meta-analysis. Ophthalmology. 2021;128(11):1580–1591. doi: 10.1016/j.ophtha.2021.04.027
  6. Schaal S, Kaplan HJ, editors. Cystoid Macular Edema. Switzerland: Springer International Publishing; 2017. doi: 10.1007/978-3-319-39766-5
  7. Bikbov MM, Fayzrakhmanov RR, Zaynullin RM, et al. Macular oedema as manifestation of diabetic retinopathy. Diabetes mellitus. 2017;20(4):263–269. EDN: ZMZAON doi: 10.14341/DM8328
  8. Chernykh DV, Chernykh VV, Trunov AN. Cytokines and growth factors in the pathogenesis of proliferative diabetic retinopathy. Moscow: Oftal’mologiya; 2017. EDN: ZNDEWH
  9. Gupta A, Tripathy K. Central Serous Chorioretinopathy [Internet]. [Updated 2022 Aug 22]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing, 2022. Available from: https://www.statpearls.com/point-of-care/96027
  10. Semeraro F, Morescalchi F, Russo A, et al. Central Serous Chorioretinopathy: Pathogenesis and Management. Clinical ophthalmology. 2019;13:2341–2352. doi: 10.2147/OPTH.S220845
  11. Oh KT, Lazzaro DR, editors. Macular Hole. [Internet]. Medscape, 2020. [cited 2020 Jan 02]. Available from: https://emedicine.medscape.com/article/1224320-overview#a6
  12. Darian-Smith E, Howie AR, Allen PL, et al. Tasmanian macular hole study: whole population-based incidence of full thickness macular hole. Clinical & Experimental Ophthalmology. 2016;44(9):812–816. doi: 10.1111/ceo.12801
  13. Fung AT, Galvin J, Tran T. Epiretinal membrane: A review. Clinical & Experimental Ophthalmology. 2021;49:289–308. doi: 10.1111/ceo.13914
  14. Oh KT, Lazzaro DR, editors. Epiretinal Membrane [Internet]. Medscape, 2020. [cited 2020 Jan 02]. Available from: https://emedicine.medscape.com/article/1223882-overview#a4
  15. World Health Organization. Regional Office for Europe. Screening for diabetic retinopathy: a short guide. Increase effectiveness, maximize benefits and minimize harm [Internet]. Copenhagen; 2021. [cited 2020 Jan 02]. Available from: https://www.who.int/europe/publications/i/item/9789289055321
  16. Qassimi AN, Kozak I, Karam AM, et al. Management of Diabetic Macular Edema: Guidelines from the Emirates Society of Ophthalmology. Ophthalmology and therapy. 2022;11:1937–1950. doi: 10.1007/s40123-022-00547-2
  17. Katalevskaya EA, Katalevskiy DYu, Tyurikov MI, Velieva IA, Bol’shunov AV. Future of artificial intelligence for the diagnosis and treatment of retinal diseases. Russian journal of clinical ophthalmology. 2022;22(1):36–43. EDN: AEBQGU doi: 10.32364/2311-7729-2022-22-1-36-43
  18. Schmidt-Erfurth U, Reiter GS, Riedl S, et al. AI-based monitoring of retinal fluid in disease activity and under therapy. Prog Retin Eye Res. 2022;86. doi: 10.1016/j.preteyeres.2021.100972
  19. Altris.ai [Internet]. United States: Altris Inc. [cited 2022 Jan 01]. Available from: https://www.altris.ai
  20. Malyugin BE, Sakhnov SN, Axenova LE, et al. A deep machine learning model development for the biomarkers of the anatomical and functional anti-VEGF therapy outcome detection on retinal OCT images. Fyodorov Journal of Ophthalmic Surgery. 2022;(S4):77–84. EDN: OWQLRM doi: 10.25276/0235-4160-2022-4S-77-84

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Fig. 1. An example of the optical coherence tomography scan analysis of a patient with diabetic macular edema by the artificial intelligence algorithm: a — structural optical coherence tomography scan; b — optical coherence tomography scan after segmentation of the pathological features (subretinal fluid — green mask, intraretinal cysts — blue masks); c — scan analysis report (the reporting table of the differential diagnostic search, probable pathology is highlighted in red — macular edema).

Download (243KB)
Indexing metadata
3. Fig. 2. An example of the optical coherence tomography scan analysis of a patient with exudative age-related macular degeneration by the artificial intelligence algorithm: a — structural optical coherence tomography scan; b — optical coherence tomography scan after segmentation of the pathological features (subretinal fluid — green mask, intraretinal cysts — blue masks, retinal pigment epithelium detachment — orange mask, subretinal hyperreflective material — yellow mask); c — scan analysis report (the reporting table of the differential diagnostic search, probable pathology is highlighted in red — macular neovascularization).

Download (284KB)
Indexing metadata
4. Fig. 3. An example of the optical coherence tomography scan analysis of a patient with macular hole, epiretinal membrane by the artificial intelligence algorithm: a — structural optical coherence tomography scan; b — optical coherence tomography scan after segmentation of the pathological features (macular hole — violet mask, intraretinal cysts — blue masks, epiretinal membrane — red masks); c — scan analysis report (the reporting table of the differential diagnostic search, probable pathology is highlighted in red — macular hole, epiretinal membrane).

Download (259KB)
Indexing metadata

Copyright (c) 2024 Eco-Vector

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies