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

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.