Artificial Intelligence in Assessing Progression of Age-Related Macular Degeneration

AI Generated Summary*

A narrative review surveys machine and deep learning tools for tracking and forecasting age-related macular degeneration progression at all disease stages, from literature searched December 2023 to March 2024. Examples span OCT quantification of drusen, outer retinal layers and hyperreflective foci; a fellow-eye model with AUCs of 0.68 for neovascular conversion and 0.80 for atrophy; atrophy segmentation on autofluorescence and infrared images with dice coefficients from about 0.89 upward, mostly lacking external validation; and fluid quantification validated across 11,127 eyes. Post-hoc trial analyses tie intraretinal fluid to poorer acuity, while genetic data added little to one prognostic model. Noted constraints include dataset diversity, interpretability, image quality, privacy and cost, with validated algorithms framed as easing rising monitoring demands.

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