Retinal fundus photographs can be used to detect a range of retinal conditions. Here we show that deep-learning models trained instead on external photographs of the eyes can be used to detect diabetic retinopathy (DR), diabetic macular oedema and poor blood glucose control. We developed the models using eye photographs from 145,832 patients with diabetes from 301 DR screening sites and evaluated the models on four tasks and four validation datasets with a total of 48,644 patients from 198 additional screening sites. For all four tasks, the predictive performance of the deep-learning models was significantly higher than the performance of logistic regression models using self-reported demographic and medical history data, and the predictions generalized to patients with dilated pupils, to patients from a different DR screening programme and to a general eye care programme that included diabetics and non-diabetics. We also explored the use of the deep-learning models for the detection of elevated lipid levels. The utility of external eye photographs for the diagnosis and management of diseases should be further validated with images from different cameras and patient populations.

视网膜眼底照片可用于检测一系列视网膜状况。在这里，我们展示了在眼睛外部照片上训练的深度学习模型可用于检测糖尿病性视网膜病变 (DR)、糖尿病性黄斑水肿和血糖控制不佳。我们使用来自 301 个 DR 筛查点的 145,832 名糖尿病患者的眼睛照片开发了模型，并根据来自 198 个额外筛查点的 48,644 名患者的四项任务和四个验证数据集评估了模型。对于所有四项任务，深度学习模型的预测性能明显高于使用自我报告的人口统计和病史数据的逻辑回归模型的性能，并且预测推广到瞳孔散大的患者，到来自不同 DR 筛查计划的患者和包括糖尿病患者和非糖尿病患者的一般眼部护理计划。我们还探索了深度学习模型在检测血脂水平升高方面的应用。应使用来自不同相机和患者群体的图像进一步验证外眼照片在疾病诊断和管理中的效用。