ArticleEye (London, England)2025
Dual site external validation of artificial intelligence-enabled treatment monitoring for neovascular age-related macular degeneration in England.
Article in Eye (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Calibrated AI for nAMD monitoring with transport validation and actionable thresholds.Eye (London, England) · 2026Article
- Towards precision medicine in Tourette syndrome: a perspective on AI-driven predictive modelling and personalised care.Frontiers in computational neuroscience · 2026Review
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Authors and funding
9 authors.
Funding
Abstract
backgroundMonitoring 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.
methodsUsing 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.
resultsReal-world assessments of nAMD activity showed a NPV of 81.6% (57.3-81.6%) and a positive predictive value (PPV) of 41.5% (17.8-62.3%). Optimised thresholds for intraretinal fluid increase (>1,000,000 µm³) and subretinal fluid increase (>2,000,000 µm³) for the AI system assessments produced an NPV of 95.3% (85.5-97.9%) and PPV of 57.8% (29.4-76.0%). The rNPV of 1.17 (1.11-1.23) met predefined criteria for clinical and statistical superiority and accompanied an rPPV of 1.39 (1.10-1.76).
conclusionsThis 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.
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