The global burden of diabetic retinopathy (DR) continues to worsen and DR remains a leading cause of vision loss worldwide. Here, we describe an algorithm to predict DR progression by means of deep learning (DL), using as input color fundus photographs (CFPs) acquired at a single visit from a patient with DR. The proposed DL models were designed to predict future DR progression, defined as 2-step worsening on the Early Treatment Diabetic Retinopathy Diabetic Retinopathy Severity Scale, and were trained against DR severity scores assessed after 6, 12, and 24 months from the baseline visit by masked, well-trained, human reading center graders. The performance of one of these models (prediction at month 12) resulted in an area under the curve equal to 0.79. Interestingly, our results highlight the importance of the predictive signal located in the peripheral retinal fields, not routinely collected for DR assessments, and the importance of microvascular abnormalities. Our findings show the feasibility of predicting future DR progression by leveraging CFPs of a patient acquired at a single visit. Upon further development on larger and more diverse datasets, such an algorithm could enable early diagnosis and referral to a retina specialist for more frequent monitoring and even consideration of early intervention. Moreover, it could also improve patient recruitment for clinical trials targeting DR.

糖尿病性视网膜病（DR）的全球负担继续加重，并且DR仍然是全世界视力丧失的主要原因。在这里，我们描述了一种算法，该算法通过使用深度学习（DL）预测DR的进展，并使用从DR患者单次就诊时获得的彩色眼底照片（CFP）作为输入。拟议的DL模型旨在预测未来的DR进展，在早期治疗糖尿病性视网膜病变糖尿病视网膜病变严重程度量表上定义为2步恶化，并针对基线访视后第6、12和24个月评估的DR严重程度评分进行了训练蒙面的，训练有素的人类阅读中心评分员。这些模型之一的性能（在第12个月的预测）导致曲线下的面积等于0.79。有趣的是，我们的研究结果突出了位于视网膜周边区域的预测信号的重要性，而该预测信号并非常规用于DR评估，而是微血管异常的重要性。我们的发现表明，通过利用单次就诊患者的CFP来预测未来DR进展的可行性。在对更大和更多样化的数据集进行进一步开发后，这种算法可以实现早期诊断并转介给视网膜专家进行更频繁的监测，甚至考虑早期干预。此外，它还可以改善针对DR的临床试验的患者招募。我们的发现表明，通过利用单次就诊患者的CFP来预测未来DR进展的可行性。在对更大和更多样化的数据集进行进一步开发后，这种算法可以实现早期诊断并转介给视网膜专家进行更频繁的监测，甚至考虑早期干预。此外，它还可以改善针对DR的临床试验的患者招募。我们的发现表明，通过利用单次就诊患者的CFP来预测未来DR进展的可行性。在对更大和更多样化的数据集进行进一步开发后，这种算法可以实现早期诊断并转介给视网膜专家进行更频繁的监测，甚至考虑早期干预。此外，它还可以改善针对DR的临床试验的患者招募。