Jun Long

In recent years, researchers have found that the diagnosis of cardiovascular disease (CVD) is closely related to retinal fundus images and specific clinical indicators. However, current diagnostic methods are still limited to analyzing single-modal data, lacking analysis of multi-modal diagnostic information, and unable to comprehensively predict the progression of CVD. To this end, we design a non-invasive multimodal CVD detection method based on comprehensive view analysis. This method uses retinal fundus images and non-invasive clinical indicators as analysis data. The method is divided into two branches; one branch is used to extract retinal fundus features, and the other branch is used to extract clinical indicator features. In the retinal fundus feature extraction branch, we propose a method for comprehensive view analysis, which assists in learning retinal fundus images by incorporating vascular segmentation maps. Additionally, we introduce a multi-order belief interaction feature fusion method, through the basic belief assignment and Dempster-Shafer theory, the extracted features of the two modalities are fused in two stages, thereby achieving complementarity between multimodal information. The model in this paper was developed and verified using 1870 UK Biobank data. The results show that our method achieves an area under the curve (AUC) of 0.873 (95% CI, 0.855–0.892) and a PR score of 0.896 (95% CI, 0.882–0.91), which were higher than those of the method using single modality data and traditional fusion methods.