Abstract: 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 (SO2) level or the retinal vascular structures. Recent studies explored the correlation of diseases with either retina vascular structures or SO2 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 SO2 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 SO2 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.

Dou P, Zhang Y, Zheng RUI, et al. Retinal Imaging and Analysis Using Machine Learning with Information Fusion of the Functional and Structural Features Based on a Dual-Modal Fundus Camera. Journal of Mechanics in Medicine and Biology. 2021;21(06)doi:10.1142/s0219519421500305