Medical Student Université Laval Laval, Quebec, Canada
Background: The rising burden of cardiovascular disease (CVD) has spurred the development of innovative, non-invasive screening methods. Advances in artificial intelligence (AI) and deep learning (DL) applied to retinal imaging offer a promising avenue, as changes in the retinal vasculature can mirror systemic vascular health.
METHODS AND RESULTS: A systematic literature search was conducted on MEDLINE and Embase databases up to February 2024 in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline using relevant search terms such as; “cardiovascular disease,” “artificial intelligence,” “deep learning,” “retinal imaging,” “colour fundus photography,” etc. Inclusion criteria were the development of a deep learning (DL) model applied to any ophthalmic imaging modality for predicting CVD outcome or for establishing CVD risk scores.
Of 9880 studies which were screened, 13 studies were included. All studies included general population databases, while 7 (54%) studies used databases that included patients with pre-existing CVD risk factors. All studies used retinal fundus images as input for the DL models, and most models (92%) analysed characteristics of the retinal vasculature (e.g., vessel calibre, venular dilatation, arteriolar narrowing, microaneurysms) for their prediction. Overall, 18 different CVD risk factors were predicted through DL models, with age (n=13; 100%), sex (n=11; 85%) and smoking status (n=9; 69%) being the most common. Five (38%) studies analysed binary CVD outcomes including incident myocardial infarction, stroke, and coronary atherosclerotic disease; Four (31%) studies compared CVD risk prediction to traditional CVD risk scores (e.g., WHO CVD risk chart, Framingham risk score, European Systematic Coronary Risk Evaluation, etc.). These studies were able to accurately stratify cumulative CVD events into low, moderate, and high-risk groups via fundus imaging, demonstrating stratification comparable to established CVD risk scores and cardiac imaging modalities. In total, 8 (62%) studies performed an external validation: the area under the receiver operating characteristic curve ranged between 68.2% and 85.9%. Accuracy, specificity and sensitivity were measured in 4 (31%) studies. Respectively, they ranged between 58.3%-82.0%, 40.4-66.0% and 81.0-89.1%. Only one AI model (Reti-CVD) was made publicly available for clinical use.
Conclusion: In conclusion, recent studies using DL applied to fundus imaging to predict CVD risk mostly examine retinal vasculature to make predictions and can stratify CVD risk comparably to other clinical risk scores. Though many report promising performance, the majority have not been used in real clinical settings. Additional research is required to enable their clinical implementation in a primary care context, or in an ophthalmological setting.
