By 2040, age-related macular degeneration (AMD) will affect ~288 million people worldwide. Identifying individuals at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Although deep learning has shown promise in diagnosing/screening AMD using color fundus photographs, it remains difficult to predict individuals’ risks of late AMD accurately. For both tasks, these initial deep learning attempts have remained largely unvalidated in independent cohorts. Here, we demonstrate how deep learning and survival analysis can predict the probability of progression to late AMD using 3298 participants (over 80,000 images) from the Age-Related Eye Disease Studies AREDS and AREDS2, the largest longitudinal clinical trials in AMD. When validated against an independent test data set of 601 participants, our model achieved high prognostic accuracy (5-year C-statistic 86.4 (95% confidence interval 86.2–86.6)) that substantially exceeded that of retinal specialists using two existing clinical standards (81.3 (81.1–81.5) and 82.0 (81.8–82.3), respectively). Interestingly, our approach offers additional strengths over the existing clinical standards in AMD prognosis (e.g., risk ascertainment above 50%) and is likely to be highly generalizable, given the breadth of training data from 82 US retinal specialty clinics. Indeed, during external validation through training on AREDS and testing on AREDS2 as an independent cohort, our model retained substantially higher prognostic accuracy than existing clinical standards. These results highlight the potential of deep learning systems to enhance clinical decision-making in AMD patients.

到 2040 年，年龄相关性黄斑变性 (AMD) 将影响全球约 2.88 亿人。识别进展为AMD晚期（威胁视力的阶段）的高风险个体对于临床行动（包括医疗干预和及时监测）至关重要。尽管深度学习在使用彩色眼底照片诊断/筛查 AMD 方面显示出良好的前景，但准确预测个人晚期 AMD 的风险仍然很困难。对于这两项任务，这些最初的深度学习尝试在独立队列中基本上仍未得到验证。在这里，我们使用来自年龄相关眼病研究 AREDS 和 AREDS2（AMD 最大的纵向临床试验）的 3298 名参与者（超过 80,000 张图像），展示了深度学习和生存分析如何预测进展为晚期 AMD 的概率。当针对 601 名参与者的独立测试数据集进行验证时，我们的模型实现了很高的预后准确性（5 年C统计量 86.4（95% 置信区间 86.2–86.6）），大大超过了使用两个现有临床标准的视网膜专家的预测准确性（81.3） (81.1–81.5) 和 82.0 (81.8–82.3)。有趣的是，我们的方法比 AMD 预后的现有临床标准（例如，风险确定超过 50%）具有更多优势，并且考虑到来自 82 个美国视网膜专科诊所的广泛培训数据，可能具有高度通用性。事实上，在通过 AREDS 培训和 AREDS2 作为独立队列进行测试的外部验证过程中，我们的模型保留了比现有临床标准高得多的预后准确性。这些结果凸显了深度学习系统在增强 AMD 患者临床决策方面的潜力。