The British journal of ophthalmology · 2025
Mapping the impact: AI-driven quantification of geographic atrophy on OCT scans and its association with visual sensitivity loss.
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Merle David A, Guymer Robyn H, Chia Mark A, Chopra Reena, Williamson Dominic J, Struyven Robbert R, Jannaud Maxime, Hadoux Xavier, van Wijngaarden Peter, Keane Pearse A, Wu Zhichao
Аннотация
BACKGROUND/AIMS: To examine the association between artificial intelligence (AI)-driven segmentation of geographic atrophy (GA) on optical coherence tomography (OCT) and visual sensitivity loss quantified by defect-mapping microperimetry, a testing strategy optimised to quantify the spatial extent of deep visual sensitivity losses.
METHODS: 50 individuals with GA secondary to age-related macular degeneration underwent defect-mapping microperimetry testing within the central 8° radius region in one eye. GA on OCT was automatically segmented with an AI-based multiclass classification and segmentation model, and GA on fundus autofluorescence (FAF) images was manually annotated. Their extent in the topographically corresponding region sampled on microperimetry was derived, and structure-function relationships were examined based on Spearman correlation coefficients (ρ). The distance of each test location from the OCT-defined and FAF-defined GA margin was also derived and used in prediction models of non-response on defect-mapping microperimetry.
RESULTS: There was a strong correlation between the proportion of locations missed on defect-mapping microperimetry and the corresponding percentage of the central 8° radius region with GA on OCT (ρ=0.85) and FAF (ρ=0.89). Prediction models for non-response at individual test locations using GA derived from OCT and FAF imaging had a sensitivity of 59% and 62% (p=0.310), respectively, at 95% specificity.
CONCLUSIONS: AI-driven, automated quantification of GA on OCT showed a strong correlation with the global extent of visual sensitivity loss, comparable with those based on manual annotations on FAF imaging. These findings affirm the expected functional relevance of OCT-derived GA measurements and their clinical utility for monitoring disease progression in those with GA.
METHODS: 50 individuals with GA secondary to age-related macular degeneration underwent defect-mapping microperimetry testing within the central 8° radius region in one eye. GA on OCT was automatically segmented with an AI-based multiclass classification and segmentation model, and GA on fundus autofluorescence (FAF) images was manually annotated. Their extent in the topographically corresponding region sampled on microperimetry was derived, and structure-function relationships were examined based on Spearman correlation coefficients (ρ). The distance of each test location from the OCT-defined and FAF-defined GA margin was also derived and used in prediction models of non-response on defect-mapping microperimetry.
RESULTS: There was a strong correlation between the proportion of locations missed on defect-mapping microperimetry and the corresponding percentage of the central 8° radius region with GA on OCT (ρ=0.85) and FAF (ρ=0.89). Prediction models for non-response at individual test locations using GA derived from OCT and FAF imaging had a sensitivity of 59% and 62% (p=0.310), respectively, at 95% specificity.
CONCLUSIONS: AI-driven, automated quantification of GA on OCT showed a strong correlation with the global extent of visual sensitivity loss, comparable with those based on manual annotations on FAF imaging. These findings affirm the expected functional relevance of OCT-derived GA measurements and their clinical utility for monitoring disease progression in those with GA.