  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.
