Retinal diseases and systemic diseases, such as diabetic retinopathy (DR) and Alzheimer’s disease, may manifest themselves in the retina, changing the retinal oxygen saturation (${\mathrm{\text{SO}}}_2$) level or the retinal vascular structures. Recent studies explored the correlation of diseases with either retina vascular structures or ${\mathrm{\text{SO}}}_2$ level, but not both due to the lack of proper instrument or methodology. In this study, we applied a dual-modal fundus camera and developed a deep learning-based analysis method to simultaneously acquire and quantify the ${\mathrm{\text{SO}}}_2$ and vascular structures. Deep learning was used to automatically locate the optic discs and segment arterioles and venules of the blood vessels. We then sought to apply machine learning methods, such as random forest (RF) and support vector machine (SVM), to fuse the ${\mathrm{\text{SO}}}_2$ level and retinal vessel parameters as different features to discriminate against the disease from the healthy controls. We showed that the fusion of the functional (oxygen saturation) and structural (vascular parameters) features offers better performance to classify diseased and healthy subjects. For example, we gained a 13.8% and 2.0% increase in the accuracy with fusion using the RF and SVM to classify the nonproliferative DR and the healthy controls.

视网膜疾病和全身性疾病，如糖尿病视网膜病变 (DR) 和阿尔茨海默病，可能会在视网膜中表现出来，从而改变视网膜氧饱和度。${\mathrm{\text{所以}}}_2$) 水平或视网膜血管结构。最近的研究探讨了疾病与视网膜血管结构或${\mathrm{\text{所以}}}_2$水平，但由于缺乏适当的工具或方法而不能两者兼而有之。在这项研究中，我们应用了双模态眼底相机并开发了一种基于深度学习的分析方法来同时获取和量化${\mathrm{\text{所以}}}_2$和血管结构。深度学习用于自动定位视盘并分割血管的小动脉和小静脉。然后，我们试图应用机器学习方法，例如随机森林 (RF) 和支持向量机 (SVM)，来融合${\mathrm{\text{所以}}}_2$水平和视网膜血管参数作为不同的特征来区分疾病与健康对照。我们表明，功能（氧饱和度）和结构（血管参数）特征的融合提供了更好的分类患病和健康受试者的性能。例如，我们使用 RF 和 SVM 对非增殖性 DR 和健康对照进行分类的融合精度提高了 13.8% 和 2.0%。