{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T17:15:28Z","timestamp":1787505328899,"version":"build-2736575974"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eye"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Monitoring neovascular age-related macular degeneration (nAMD) is a significant contributor to ophthalmology demands in the NHS, with clinical capacity struggling to meet the demand. This task depends upon interpreting retinal optical coherence tomography (OCT) imaging, where artificial intelligence (AI) could rebalance clinical demand and capacity. However, evidence of safety and effectiveness in nAMD monitoring is lacking.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using a published non-inferiority design protocol, 521 pairs of ipsilateral retinal OCTs from consecutive visits for nAMD treatment were collected from two NHS ophthalmology services. Real-world binary assessments of nAMD disease activity or stability were compared to an independent ophthalmic reading centre reference standard. An AI system capable of retinal OCT segmentation analysed the OCTs, applying thresholds for intraretinal and subretinal fluid to generate binary assessments. The relative negative predictive value (rNPV) of AI versus real-world assessments was calculated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Real-world assessments of nAMD activity showed a NPV of 81.6% (57.3\u201381.6%) and a positive predictive value (PPV) of 41.5% (17.8\u201362.3%). Optimised thresholds for intraretinal fluid increase (&gt;1,000,000\u2009\u00b5m\u00b3) and subretinal fluid increase (&gt;2,000,000\u2009\u00b5m\u00b3) for the AI system assessments produced an NPV of 95.3% (85.5\u201397.9%) and PPV of 57.8% (29.4\u201376.0%). The rNPV of 1.17 (1.11\u20131.23) met predefined criteria for clinical and statistical superiority and accompanied an rPPV of 1.39 (1.10\u20131.76).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study suggests that the same thresholds for interpreting OCT-based AI analysis could reduce undertreatment and overtreatment in nAMD monitoring at different centres. Interventional research is needed to test the potential of supportive or autonomous AI assessments of nAMD disease activity to improve the quality and efficiency of services.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1038\/s41433-025-04025-4","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T18:38:54Z","timestamp":1758307134000},"page":"2973-2979","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Dual site external validation of artificial intelligence-enabled treatment monitoring for neovascular age-related macular degeneration in England"],"prefix":"10.1038","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8044-7790","authenticated-orcid":false,"given":"Henry David Jeffry","family":"Hogg","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S. 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MDT \u2013 No conflicts of interest MP is an officer of a company offering consultancy services to manufacturers of Software as a Medical Device. SJT has acted on advisory boards for Bayer and Roche, received travel and speaker fees for Bayer, Roche, Heidelberg engineering, Alimera Sciences LTD and research support from Bayer, Roche, Boehringer Ingelheim, Janssen Pharmaceuticals and Alexion Pharmaceuticals. PJP has acted as a consultant for Bayer, Boehringer Ingelheim, Genentech and Roche. He has received travel support from Bayer and Roche. KB has received speaker fees from Novartis, Bayer, Roche, and Boehringer-Ingelheim; equity in READ AI; meeting or travel fees from Novartis and Bayer; compensation for being on an advisory board from Novartis and Roche; consulting fees from Roche and Boehringer-Ingelheim; research support from Apellis, Novartis, and Bayer. GM \u2013 No conflicts of interest. PAK has acted as a consultant for Retina Consultants of America, Topcon, Roche, Boehringer-Ingelheim, and Bitfount and is an equity owner in Big Picture Medical. He has received speaker fees from Zeiss, Novartis, Gyroscope, Boehringer-Ingelheim, Apellis, Roche, AbbVie, Topcon, and Hakim Group. He has received travel support from Bayer, Topcon, and Roche. He has attended advisory boards for Topcon, Bayer, Boehringer-Ingelheim, RetinAI, and Novartis.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Ethical approval was granted for this work by the Greater Manchester South Research Ethics Committee, reference 21\/NW\/0138.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics"}}]}}