Purpose: To create and evaluate the accuracy of an artificial intelligence Deep learning platform (ORAiCLE) capable of using only retinal fundus images to predict both an individuals overall 5 year cardiovascular risk (CVD) and the relative contribution of the component risk factors that comprise this risk. Methods: We used 165,907 retinal images from a database of 47,236 patient visits. Initially, each image was paired with biometric data age, ethnicity, sex, presence and duration of diabetes a HDL/LDL ratios as well as any CVD event wtihin 5 years of the retinal image acquisition. A risk score based on Framingham equations was calculated. The real CVD event rate was also determined for the individuals and overall population. Finally, ORAiCLE was trained using only age, ethnicity, sex plus retinal images. Results: Compared to Framingham-based score, ORAiCLE was up to 12% more accurate in prediciting cardiovascular event in he next 5-years, especially for the highest risk group of people. The reliability and accuracy of each of the restrictive models was suboptimal to ORAiCLE performance ,indicating that it was using data from both sets of data to derive its final results. Conclusion: Retinal photography is inexpensive and only minimal training is required to acquire them as fully automated, inexpensive camera systems are now widely available. As such, AI-based CVD risk algorithms such as ORAiCLE promise to make CV health screening more accurate, more afforadable and more accessible for all. Furthermore, ORAiCLE unique ability to assess the relative contribution of the components that comprise an individuals overall risk would inform treatment decisions based on the specific needs of an individual, thereby increasing the likelihood of positive health outcomes.

本研究旨在创建和评估一个人工智能深度学习平台(ORAiCLE)，该平台仅使用视网膜底部图像即可预测个体在5年内的心血管疾病(CVD)风险以及构成该风险的组成部分的相关贡献。使用 Framingham公式计算风险，与ORAiCLE相比，其在高风险人群中预测心血管事件的准确性最高可提高12％。因此，学习者认为这种基于人工智能的 CVD风险算法有望使患者更精确，更实惠，更易于得到诊断。

Replacing the Framingham-based equation for prediction of cardiovascular
  disease risk and adverse outcome by using artificial intelligence and retinal
  imaging

利用人工智能和视网膜成像替代基于Framingham的心血管疾病风险和不良结果预测方程