高分辨率的 fundus 图像用于眼科和早期心血管疾病预测
1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
Scientific data
|April 3, 2025
概括
这项研究引入了一个新的数据集,将眼底图像与动脉内膜介质厚度 (CIMT) 测量联系起来. 将临床数据与多式人工智能模型集成,可以显著改善心血管疾病 (CVD) 的预测.
科学领域:
- 眼科和心脏病学的眼科和心脏病.
- 医疗保健中的人工智能
- 生物医学数据科学 生物医学数据科学
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因,需要早期检测方法.
- Carotid intima-media 厚度 (CIMT) 是动脉样硬化和心血管风险的一个关键指标.
- 底部成像为系统性血管健康提供了一个非侵入性的窗口,但链接的数据集很少.
研究的目的:
- 为了解决缺乏公共数据集,连接 fundus 图像和 CIMT 测量.
- 引入中国-Fundus-CIMT数据集用于人工智能驱动的CVD预测.
- 评估将临床数据整合到多式联络预测模型中的影响.
主要方法:
- 汇编了中国-Fundus-CIMT数据集,其中包括2,903名患者,包括 fundus 图像,CIMT 测量,年龄和性别.
- 开发和测试用于心血管疾病风险预测的单模和多模AI模型.
- 使用AUC-ROC指标对预测性能进行比较分析.
主要成果:
- 综合临床数据的多模式模型显示,预测性能的显著改善.
- 与单模模型相比,AUC-ROC在验证组上增加了3.22%,在测试组上增加了7.83%.
- 中国-基金-CIMT数据集对于人工智能模型开发和验证非常有价值.
结论:
- 中国-基金-CIMT数据集是推进基于AI的早期心血管疾病查使用基金图像的关键资源.
- 将临床数据与成像相结合,显著提高了心血管疾病预测模型的准确性.
- 这项工作有助于开发用于心血管风险评估的新型非侵入性方法.
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