{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T15:13:38Z","timestamp":1790954018666,"version":"4.1.0"},"reference-count":35,"publisher":"BMJ","issue":"10","funder":[{"name":"UK Research & Innovation Future Leaders Fellowship","award":["MR\/T019050\/1"],"award-info":[{"award-number":["MR\/T019050\/1"]}]},{"DOI":"10.13039\/100032419","name":"EURETINA","doi-asserted-by":"crossref","award":["Retinal Medicine Clinical Research Grant"],"award-info":[{"award-number":["Retinal Medicine Clinical Research Grant"]}],"id":[{"id":"10.13039\/100032419","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100019531","name":"Apellis Pharmaceuticals","doi-asserted-by":"publisher","award":["Investigator-initiated trial grant"],"award-info":[{"award-number":["Investigator-initiated trial grant"]}],"id":[{"id":"10.13039\/100019531","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006312","name":"BrightFocus Foundation","doi-asserted-by":"publisher","award":["M2019073"],"award-info":[{"award-number":["M2019073"]}],"id":[{"id":"10.13039\/100006312","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Health & Medical Research Council of Australia","award":["GNT1194667"],"award-info":[{"award-number":["GNT1194667"]}]},{"name":"National Health & Medical Research Council of Australia","award":["GNT2008382"],"award-info":[{"award-number":["GNT2008382"]}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Br J Ophthalmol"],"accepted":{"date-parts":[[2025,5,18]]},"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:sec>\n                    <jats:title>Background\/aims<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>50 individuals with GA secondary to age-related macular degeneration underwent defect-mapping microperimetry testing within the central 8\u00b0 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 (\u03c1). 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>There was a strong correlation between the proportion of locations missed on defect-mapping microperimetry and the corresponding percentage of the central 8\u00b0 radius region with GA on OCT (\u03c1=0.85) and FAF (\u03c1=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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bjo-2024-326603","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T13:20:19Z","timestamp":1749475219000},"page":"1187-1193","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":6,"title":["Mapping the impact: AI-driven quantification of geographic atrophy on OCT scans and its association with visual sensitivity loss"],"prefix":"10.1136","volume":"109","author":[{"given":"David A","family":"Merle","sequence":"first","affiliation":[{"name":"NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust, London, UK"},{"name":"Institute of Ophthalmology, University College London, London, UK"},{"name":"Centre for Ophthalmology, University Hospital 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