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

• URL: http://arxiv.org/abs/2207.14685v2
• Date: Mon, 1 Aug 2022 00:44:03 GMT
• Title: Replacing the Framingham-based equation for prediction of cardiovascular disease risk and adverse outcome by using artificial intelligence and retinal imaging
• Authors: Ehsan Vaghefi, David Squirrell, Songyang An, Song Yang, John Marshall
• Abstract summary: We used 165,907 retinal images from a database of 47,236 patient visits. Risk score based on Framingham equations was calculated. The real CVD event rate was also determined for the individuals and overall population. 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.
• Score: 2.3972862241374444
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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