AI-based Support for Optical Coherence Tomography in Age-related Macular Degeneration

Overview

Journal Int J Retina Vitreous

Publisher Biomed Central

Date 2024 Apr 8

PMID 38589936

Authors

Virginia Mares

Marcio B Nehemy

Hrvoje Bogunovic

Sophie Frank

Gregor S Reiter

Ursula Schmidt-Erfurth

Affiliations

Soon will be listed here.

Abstract

Artificial intelligence (AI) has emerged as a transformative technology across various fields, and its applications in the medical domain, particularly in ophthalmology, has gained significant attention. The vast amount of high-resolution image data, such as optical coherence tomography (OCT) images, has been a driving force behind AI growth in this field. Age-related macular degeneration (AMD) is one of the leading causes for blindness in the world, affecting approximately 196 million people worldwide in 2020. Multimodal imaging has been for a long time the gold standard for diagnosing patients with AMD, however, currently treatment and follow-up in routine disease management are mainly driven by OCT imaging. AI-based algorithms have by their precision, reproducibility and speed, the potential to reliably quantify biomarkers, predict disease progression and assist treatment decisions in clinical routine as well as academic studies. This review paper aims to provide a summary of the current state of AI in AMD, focusing on its applications, challenges, and prospects.

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References

Ly A, Nivison-Smith L, Assaad N, Kalloniatis M . Fundus Autofluorescence in Age-related Macular Degeneration. Optom Vis Sci. 2016; 94(2):246-259. PMC: 5287441. DOI: 10.1097/OPX.0000000000000997. View

Das S, Kuht H, De Silva I, Deol S, Osman L, Burns J . Feasibility and clinical utility of handheld fundus cameras for retinal imaging. Eye (Lond). 2022; 37(2):274-279. PMC: 9873676. DOI: 10.1038/s41433-021-01926-y. View

Finn A, Pistilli M, Tai V, Daniel E, Ying G, Maguire M . Localized Optical Coherence Tomography Precursors of Macular Atrophy and Fibrotic Scar in the Comparison of Age-Related Macular Degeneration Treatments Trials. Am J Ophthalmol. 2020; 223:338-347. PMC: 7979472. DOI: 10.1016/j.ajo.2020.11.002. View

Yim J, Chopra R, Spitz T, Winkens J, Obika A, Kelly C . Predicting conversion to wet age-related macular degeneration using deep learning. Nat Med. 2020; 26(6):892-899. DOI: 10.1038/s41591-020-0867-7. View

Li M, Huisingh C, Messinger J, Dolz-Marco R, Ferrara D, Freund K . HISTOLOGY OF GEOGRAPHIC ATROPHY SECONDARY TO AGE-RELATED MACULAR DEGENERATION: A Multilayer Approach. Retina. 2018; 38(10):1937-1953. PMC: 6166696. DOI: 10.1097/IAE.0000000000002182. View

Thiele S, Nadal J, Pfau M, Sassmannshausen M, Fleckenstein M, Holz F . Prognostic value of intermediate age-related macular degeneration phenotypes for geographic atrophy progression. Br J Ophthalmol. 2020; 105(2):239-245. PMC: 7848046. DOI: 10.1136/bjophthalmol-2020-316004. View

Reiter G, Told R, Schranz M, Baumann L, Mylonas G, Sacu S . Subretinal Drusenoid Deposits and Photoreceptor Loss Detecting Global and Local Progression of Geographic Atrophy by SD-OCT Imaging. Invest Ophthalmol Vis Sci. 2020; 61(6):11. PMC: 7415285. DOI: 10.1167/iovs.61.6.11. View

Fleckenstein M, Keenan T, Guymer R, Chakravarthy U, Schmitz-Valckenberg S, Klaver C . Age-related macular degeneration. Nat Rev Dis Primers. 2021; 7(1):31. DOI: 10.1038/s41572-021-00265-2. View

Mares V, Schmidt-Erfurth U, Leingang O, Fuchs P, Nehemy M, Bogunovic H . Approved AI-based fluid monitoring to identify morphological and functional treatment outcomes in neovascular age-related macular degeneration in real-world routine. Br J Ophthalmol. 2023; 108(7):971-977. DOI: 10.1136/bjo-2022-323014. View

10.

Ting D, Yim-Lui Cheung C, Lim G, Wei Tan G, Quang N, Gan A . Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA. 2017; 318(22):2211-2223. PMC: 5820739. DOI: 10.1001/jama.2017.18152. View

11.

Schmidt-Erfurth U, Waldstein S . A paradigm shift in imaging biomarkers in neovascular age-related macular degeneration. Prog Retin Eye Res. 2015; 50:1-24. DOI: 10.1016/j.preteyeres.2015.07.007. View

12.

Sleiman K, Veerappan M, Winter K, McCall M, Yiu G, Farsiu S . Optical Coherence Tomography Predictors of Risk for Progression to Non-Neovascular Atrophic Age-Related Macular Degeneration. Ophthalmology. 2017; 124(12):1764-1777. PMC: 5768932. DOI: 10.1016/j.ophtha.2017.06.032. View

13.

Zhou Y, Chia M, Wagner S, Ayhan M, Williamson D, Struyven R . A foundation model for generalizable disease detection from retinal images. Nature. 2023; 622(7981):156-163. PMC: 10550819. DOI: 10.1038/s41586-023-06555-x. View

14.

Romond K, Alam M, Kravets S, De Sisternes L, Leng T, Lim J . Imaging and artificial intelligence for progression of age-related macular degeneration. Exp Biol Med (Maywood). 2021; 246(20):2159-2169. PMC: 8718252. DOI: 10.1177/15353702211031547. View

15.

Schmidt-Erfurth U, Reiter G, Riedl S, Seebock P, Vogl W, Blodi B . AI-based monitoring of retinal fluid in disease activity and under therapy. Prog Retin Eye Res. 2021; 86:100972. DOI: 10.1016/j.preteyeres.2021.100972. View

16.

Martin D, Maguire M, Fine S, Ying G, Jaffe G, Grunwald J . Ranibizumab and bevacizumab for treatment of neovascular age-related macular degeneration: two-year results. Ophthalmology. 2012; 119(7):1388-98. PMC: 3389193. DOI: 10.1016/j.ophtha.2012.03.053. View

17.

Bjerager J, Schneider M, Potapenko I, van Dijk E, Faber C, Grauslund J . Diagnostic Accuracy of the Amsler Grid Test for Detecting Neovascular Age-Related Macular Degeneration: A Systematic Review and Meta-analysis. JAMA Ophthalmol. 2023; 141(4):315-323. DOI: 10.1001/jamaophthalmol.2022.6396. View

18.

LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

19.

Nanegrungsunk O, Gu S, Bressler S, Du W, Amer F, Moini H . Correlation of Change in Central Subfield Thickness and Change in Visual Acuity in Neovascular AMD: Post Hoc Analysis of VIEW 1 and 2. Am J Ophthalmol. 2021; 238:97-102. DOI: 10.1016/j.ajo.2021.11.020. View

20.

Muntean G, Marginean A, Groza A, Damian I, Roman S, Hapca M . The Predictive Capabilities of Artificial Intelligence-Based OCT Analysis for Age-Related Macular Degeneration Progression-A Systematic Review. Diagnostics (Basel). 2023; 13(14). PMC: 10378064. DOI: 10.3390/diagnostics13142464. View