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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 individuals 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 individuals 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 8969 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 an area under the receiver operating characteristic curve 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, as well as the value of applying DL models to different external datasets and opportunities about ASCVD risk assessment in patients living with diabetes.

Recent advancements in artificial intelligence (AI) have prompted researchers to expand into the field of oculomics; the association between the retina and systemic health. Unlike conventional AI models trained on well-recognized retinal features, the retinal phenotypes that most oculomics models use are more subtle. Consequently, applying conventional tools, such as saliency maps, to understand how oculomics models arrive at their inference is problematic and open to bias. We hypothesized that neuron activation patterns (NAPs) could be an alternative way to interpret oculomics models, but currently, most existing implementations focus on failure diagnosis. In this study, we designed a novel NAP framework to interpret an oculomics model. We then applied our framework to an AI model predicting systolic blood pressure from fundus images in the United Kingdom Biobank dataset. We found that the NAP generated from our framework was correlated to the clinically relevant endpoint of cardiovascular risk. Our NAP was also able to discern two biologically distinct groups among participants who were assigned the same predicted systolic blood pressure. These results demonstrate the feasibility of our proposed NAP framework for gaining deeper insights into the functioning of oculomics models. Further work is required to validate these results on external datasets. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the 3-year outcomes of VEGF inhibitors in the treatment of cystoid macular edema due to branch retinal vein occlusion (BRVO) in an international multicenter cohort of eyes., Design: Multicenter, international, BRVO database study., Subjects: Seven hundred forty-seven patients (760 eyes) undergoing intravitreal therapy for BRVO for 3 years in a multicenter international setting., Methods: Demographics, visual acuity (VA) in logarithm of the minimum angle of resolution letters, central subfield thickness (CST), treatments, number of injections, and visits data was collected using a validated web-based tool., Main Outcome Measures: Visual acuity gain at 3 years in logarithm of the minimum angle of resolution letters. Secondary outcome measures included anatomical results, treatment pattern, and percentage of completers. A subgroup analysis by study drug was conducted for clinical outcomes., Results: Mean adjusted VA change was +11 letters (95% confidence interval 9-13), mean adjusted change in CST was -176 μm (-193, -159). Median number of injections/visits was 16 of 24 at 3 years of follow-up. Most eyes received VEGF inhibitors exclusively (89%, n = 677) and as a monotherapy in 71% (n = 538). Few eyes were switched to steroids (11%, n = 83). Suspensions in treatment >180 days occurred in 26% of study eyes. Aflibercept showed greater CST reductions (-147 vs. -128 vs. -114 μm; P < 0.001) and significantly lower switching rates (14% vs. 38% vs. 33%; P < 0.001) compared with ranibizumab and bevacizumab, respectively., Conclusions: This international study of 3-year BRVO outcomes after starting treatment with VEGF inhibitors found adequate visual and anatomical results in routine clinical care. Visual outcomes were similar among the different initiating VEGF inhibitors, although eyes starting with aflibercept had better anatomical outcomes and a lower switching rate., Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (Copyright © 2024. Published by Elsevier Inc.)