Pivotal trial of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from CMERC-HI.

Abstract

The potential of using retinal images as a biomarker of cardiovascular disease (CVD) risk has gained significant attention, but regulatory approval of such artificial intelligence (AI) algorithms is lacking. In this regulated pivotal trial, we validated the efficacy of Reti-CVD, an AI-Software as a Medical Device (AI-SaMD), that utilizes retinal images to stratify CVD risk. In this retrospective study, we used data from the Cardiovascular and Metabolic Diseases Etiology Research Center-High Risk (CMERC-HI) Cohort. Cox proportional hazard model was used to estimate hazard ratio (HR) trend across the 3-tier CVD risk groups (low-, moderate-, and high-risk) according to Reti-CVD in prediction of CVD events. The cardiac computed tomography-measured coronary artery calcium (CAC), carotid intima-media thickness (CIMT), and brachial-ankle pulse wave velocity (baPWV) were compared to Reti-CVD. A total of 1106 participants were included, with 33 (3.0%) participants experiencing CVD events over 5years; the Reti-CVD-defined risk groups (low, moderate, and high) were significantly associated with increased CVD risk (HR trend, 2.02; 95% CI, 1.26-3.24). When all variables of Reti-CVD, CAC, CIMT, baPWV, and other traditional risk factors were incorporated into one Cox model, the Reti-CVD risk groups were only significantly associated with increased CVD risk (HR = 2.40 [0.82-7.03] in moderate risk and HR = 3.56 [1.34-9.51] in high risk using low-risk as a reference). This regulated pivotal study validated an AI-SaMD, retinal image-based, personalized CVD risk scoring system (Reti-CVD). These results led the Korean regulatory body to authorize Reti-CVD.