Development and validation of a deep-learning model to predict 10-year ASCVD risk from retinal images using the UK Biobank and EyePACS 10K datasets

Background Atherosclerotic Cardiovascular Disease (ASCVD) is a leading cause of death globally, and early detection of high-risk individuals is essential for initiating timely interventions. The authors aimed to develop and validate a deep learning (DL) model to predict an individual’s elevated 10-year ASCVD risk score based on retinal images and limited demographic data. Methods The study used 89,894 retinal fundus images from 44,176 UK Biobank participants (96% non-Hispanic White, 5% diabetic) to train and test the DL model. The DL model was developed using retinal images plus age, race/ethnicity, and sex at birth to predict an individual’s 10-year ASCVD risk score using the Pooled Cohort Equation (PCE) as the ground truth. This model was then tested on the US EyePACS 10K dataset (5.8% Non-Hispanic white 99.9% diabetic), composed of 18,900 images from 8,969 diabetic individuals. Elevated ASCVD risk was defined as a PCE score of ≥7.5%. Results In the UK Biobank internal validation dataset, the DL model achieved area under the receiver operating characteristic curve (AUROC) of 0.89, sensitivity 84%, and specificity 90%, for detecting individuals with elevated ASCVD risk scores. In the EyePACS 10K and with the addition of a regression-derived diabetes modifier, it achieved sensitivity 94%, specificity 72%, mean error −0.2%, and mean absolute error 3.1%. Conclusion This study demonstrates that DL models using retinal images can provide an additional approach to estimating ASCVD risk, and the value of applying DL models to different external datasets and opportunities about ASCVD risk assessment in patients living with diabetes. ### Competing Interest Statement The study is funded by Toku Eyes, and some of the writers are paid advisors of Toku ### Funding Statement Toku Eyes has funded this study ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: UK Biobank data was obtained after the approval of its data guardian and according to its internal IRB processes EyePACS was obtained after the approval of its data guardian and according to its internal IRB processes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Databases used in this study are obtainable from their original data guardian, UK Biobank and EyePACS.