Abstract

PurposeThe purpose of this study was to summarize and evaluate artificial intelligence (AI) algorithms used in geographic atrophy (GA) diagnostic processes (e.g. isolating lesions or disease progression).MethodsThe search strategy and selection of publications were both conducted in accordance with the Preferred of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and Web of Science were used to extract literary data. The algorithms were summarized by objective, performance, and scope of coverage of GA diagnosis (e.g. lesion automation and GA progression).ResultsTwenty-seven studies were identified for this review. A total of 18 publications focused on lesion segmentation only, 2 were designed to detect and classify GA, 2 were designed to predict future overall GA progression, 3 focused on prediction of future spatial GA progression, and 2 focused on prediction of visual function in GA. GA-related algorithms reported sensitivities from 0.47 to 0.98, specificities from 0.73 to 0.99, accuracies from 0.42 to 0.995, and Dice coefficients from 0.66 to 0.89.ConclusionsCurrent GA-AI publications have a predominant focus on lesion segmentation and a minor focus on classification and progression analysis. AI could be applied to other facets of GA diagnoses, such as understanding the role of hyperfluorescent areas in GA. Using AI for GA has several advantages, including improved diagnostic accuracy and faster processing speeds.Translational RelevanceAI can be used to quantify GA lesions and therefore allows one to impute visual function and quality-of-life. However, there is a need for the development of reliable and objective models and software to predict the rate of GA progression and to quantify improvements due to interventions.

Highlights

  • Age-related macular degeneration (AMD) is the most common cause of irreversible vision loss and legal blindness, accounting for 8.7% of blindness globally for individuals aged 50 years and older.[1]

  • Localized presence of exudative type 1 choroidal neovascularization (CNV) was associated with markedly reduced odds for the localized future progression of retinal pigment epithelium (RPE) atrophy (OR = 0.46; 95% CI = 0.41–0.51; P < 0.001)

  • The review reported here revealed that the primary focus in the literature on AI in geographic atrophy (GA) was on the segmentation of GA lesions (i.e. 18 of 27 publications)

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Summary

Introduction

Age-related macular degeneration (AMD) is the most common cause of irreversible vision loss and legal blindness (visual acuity [VA] < 6/60 in the better eye), accounting for 8.7% of blindness globally for individuals aged 50 years and older.[1] One of the two late stages of the disease is referred to as geographic atrophy (GA).

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