Hogg Henry David Jeffry, Talks S James, Engelmann Justin, Teare Marion Dawn, Pogose Michael, Patel Praveen J, Balaskas K, Maniatopoulos G, Keane P A
University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
Institute for Inflammation and Ageing, University of Birmingham, Birmingham, UK.
Eye (Lond). 2025 Sep 19. doi: 10.1038/s41433-025-04025-4.
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.
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.
Real-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).
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.
监测新生血管性年龄相关性黄斑变性（nAMD）是英国国民医疗服务体系（NHS）眼科需求的一个重要因素，临床能力难以满足需求。这项任务依赖于对视网膜光学相干断层扫描（OCT）成像的解读，而人工智能（AI）可以重新平衡临床需求和能力。然而，缺乏nAMD监测安全性和有效性的证据。
采用已发表的非劣效性设计方案，从两个NHS眼科服务机构收集了521对连续就诊接受nAMD治疗的同侧视网膜OCT图像。将nAMD疾病活动或稳定的真实二元评估结果与独立眼科阅读中心的参考标准进行比较。一个能够进行视网膜OCT分割的人工智能系统对OCT图像进行分析，应用视网膜内和视网膜下液的阈值来生成二元评估。计算人工智能评估相对于真实评估的相对阴性预测值（rNPV）。
nAMD活动的真实评估显示阴性预测值为81.6%（57.3 - 81.6%），阳性预测值（PPV）为41.5%（17.8 - 62.3%）。人工智能系统评估中，视网膜内液增加（>1,000,000 µm³）和视网膜下液增加（>2,000,000 µm³）的优化阈值产生的阴性预测值为95.3%（85.5 - 97.9%），阳性预测值为57.8%（29.4 - 76.0%）。1.17（1.11 - 1.23）的rNPV符合临床和统计学优越性的预定义标准，同时rPPV为1.39（1.10 - 1.76）。
本研究表明，基于OCT的人工智能分析的相同解读阈值可以减少不同中心nAMD监测中的治疗不足和过度治疗。需要进行干预性研究来测试支持性或自主性人工智能对nAMD疾病活动评估的潜力，以提高服务质量和效率。
