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<p>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.</p>

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High-resolution fundus images for ophthalmomics and early cardiovascular disease prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月3日

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  • Na Guo,
  • Wanjin Fu,
  • Heng Li,
  • Haoyun Zhang,
  • Tiantian Li,
  • Wei Zhang,
  • Xing Zhong,
  • Tianrong Pan,
  • Fuchun Sun,
  • Ajuan Gong

摘要

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.