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期刊来源：Nat Med

原文链接：https://doi.org/10.1038/s41591-023-02702-z

摘要内容如下：

糖尿病视网膜病变（DR）是世界范围内可预防失明的主要原因。DR进展的风险在不同的个体中是高度可变的，这使得预测风险和个性化筛查间隔变得困难。我们开发并验证了一个深度学习系统（DeepDR Plus），仅根据眼底图像即可预测5年内DR进展的时间。首先，我们使用了来自179，327名糖尿病患者的717，308张眼底图像来预训练系统。随后，我们使用多种族数据集对该系统进行了训练和验证，该数据集包括来自29，868名糖尿病患者的118，868张图像。对于预测DR进展的时间，该系统在长达5年的所有时间内获得了0.754-0.846的一致性指数和0.153-0.241的综合Brier评分。此外，我们在真实世界的糖尿病参与者队列中验证了该系统。与临床工作流程的整合可潜在地将平均筛查间隔从12个月延长至31.97个月，建议在1-5年进行筛查的参与者百分比分别为30.62%、20.00%、19.63%、11.85%和17.89%，而进展为威胁视力的DR的延迟检测为0.18%。总之，DeepDR Plus系统可以预测5年内DR进展的个体化风险和时间，潜在地允许个性化的筛查间隔。

英文原文如下：

Abstracts

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

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