College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China.
Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, 600 Yishan Road, Shanghai, 200233, China.
Phys Eng Sci Med. 2023 Jun;46(2):813-825. doi: 10.1007/s13246-023-01254-3. Epub 2023 Apr 11.
Continuous glucose monitoring (CGM) data analysis will provide a new perspective to analyze factors related to diabetic retinopathy (DR). However, the problem of visualizing CGM data and automatically predicting the incidence of DR from CGM is still controversial. Here, we explored the feasibility of using CGM profiles to predict DR in type 2 diabetes (T2D) by deep learning approach. This study fused deep learning with a regularized nomogram to construct a novel deep learning nomogram from CGM profiles to identify patients at high risk of DR. Specifically, a deep learning network was employed to mine the nonlinear relationship between CGM profiles and DR. Moreover, a novel nomogram combining CGM deep factors with basic information was established to score the patients' DR risk. This dataset consists of 788 patients belonging to two cohorts: 494 in the training cohort and 294 in the testing cohort. The area under the curve (AUC) values of our deep learning nomogram were 0.82 and 0.80 in the training cohort and testing cohort, respectively. By incorporating basic clinical factors, the deep learning nomogram achieved an AUC of 0.86 in the training cohort and 0.85 in the testing cohort. The calibration plot and decision curve showed that the deep learning nomogram had the potential for clinical application. This analysis method of CGM profiles can be extended to other diabetic complications by further investigation.
连续血糖监测（CGM）数据分析将为分析与糖尿病视网膜病变（DR）相关的因素提供新的视角。然而，可视化 CGM 数据并从 CGM 自动预测 DR 发生率的问题仍存在争议。在这里，我们通过深度学习方法探讨了使用 CGM 谱图预测 2 型糖尿病（T2D）中 DR 的可行性。本研究将深度学习与正则化列线图相结合，从 CGM 谱图中构建了一种新的深度学习列线图，以识别 DR 高危患者。具体来说，使用深度学习网络挖掘 CGM 谱图与 DR 之间的非线性关系。此外，建立了一种新的列线图，将 CGM 深度因素与基本信息相结合，对患者的 DR 风险进行评分。该数据集包含来自两个队列的 788 名患者：训练队列中的 494 名和测试队列中的 294 名。我们的深度学习列线图在训练队列和测试队列中的 AUC 值分别为 0.82 和 0.80。通过纳入基本临床因素，深度学习列线图在训练队列和测试队列中的 AUC 值分别达到 0.86 和 0.85。校准图和决策曲线表明，深度学习列线图具有临床应用的潜力。通过进一步研究，这种 CGM 谱图的分析方法可以扩展到其他糖尿病并发症。
