Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Aug 1;42(8):1417-1424.
doi: 10.1097/IAE.0000000000003535.

A SYSTEMATIC REVIEW OF DEEP LEARNING APPLICATIONS FOR OPTICAL COHERENCE TOMOGRAPHY IN AGE-RELATED MACULAR DEGENERATION

Samantha K Paul  1 , Ian Pan  2 , Warren M Sobol  1
Affiliations

A SYSTEMATIC REVIEW OF DEEP LEARNING APPLICATIONS FOR OPTICAL COHERENCE TOMOGRAPHY IN AGE-RELATED MACULAR DEGENERATION

Samantha K Paul et al. Retina. .

Abstract

Purpose: To survey the current literature regarding applications of deep learning to optical coherence tomography in age-related macular degeneration (AMD).

Methods: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses systematic review was conducted from January 1, 2000, to May 9, 2021, using PubMed and EMBASE databases. Original research investigations that applied deep learning to optical coherence tomography in patients with AMD or features of AMD (choroidal neovascularization, geographic atrophy, and drusen) were included. Summary statements, data set characteristics, and performance metrics were extracted from included articles for analysis.

Results: We identified 95 articles for this review. The majority of articles fell into one of six categories: 1) classification of AMD or AMD biomarkers (n = 40); 2) segmentation of AMD biomarkers (n = 20); 3) segmentation of retinal layers or the choroid in patients with AMD (n = 7); 4) assessing treatment response and disease progression (n = 13); 5) predicting visual function (n = 6); and 6) determining the need for referral to a retina specialist (n = 3).

Conclusion: Deep learning models generally achieved high performance, at times comparable with that of specialists. However, external validation and experimental parameters enabling reproducibility were often limited. Prospective studies that demonstrate generalizability and clinical utility of these models are needed.

PubMed Disclaimer

References

    1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019;25:44–56.
    1. Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw 2015;61:85–117.
    1. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2012;25:1097–1105.
    1. Vaswani A, Shazeer N, Parmar N, et al. Attention is All You Need. 31st Conference on Neural Information Processing Systems; December 4–9, 2017; Long Beach, CA.
    1. Liu Y, Jain A, Eng C, et al. A deep learning system for differential diagnosis of skin diseases. Nat Med 2020;26:900–908.

Publication types