Abstract
Cardiovascular diseases (CVDs) remain the foremost cause of mortality globally, emphasizing the imperative for early detection to improve patient outcomes and mitigate healthcare burdens. Carotid intima-media thickness (CIMT) serves as a well-established predictive marker for atherosclerosis and cardiovascular risk assessment. Fundus imaging offers a non-invasive modality to investigate microvascular pathology and systemic vascular health. However, the paucity of high-quality, publicly available datasets linking fundus images with CIMT measurements has hindered the progression of AI-driven predictive models for CVDs. Addressing this gap, we introduce the China-Fundus-CIMT dataset, comprising bilateral high-resolution fundus images, CIMT measurements, and clinical data-including age and gender-from 2,903 patients. Our experiments with multimodal models reveal that integrating clinical information substantially enhances predictive performance, yielding AUC-ROC increases of 3.22% and 7.83% on the validation and test sets, respectively, compared to unimodal models. This dataset constitutes a vital resource for developing and validating AI-based early screening models for CVDs using fundus images and is now accessible to the research community.