The distribution of artificial intelligence–derived retinal cardiovascular risk scores and conventional risk factors in two Korean health screening cohorts: a descriptive study

ABSTRACT

• Background Although retinal imaging–based artificial intelligence (AI) tools have recently been introduced for cardiovascular disease (CVD) risk assessment, little is known about the distribution of these AI-derived scores across the full age spectrum or their associations with traditional cardiometabolic risk factors at different ages.

• Methods We analyzed data from 138,745 participants who underwent routine health examinations at two health screening centers in Seoul, Korea. The AI-based retinal CVD risk score (Dr.Noon CVD), as well as anthropometric, hemodynamic, and metabolic indices and cardiometabolic disease status, were compared across ages 16 to 96 years. In a subgroup of 13,182 individuals who underwent coronary artery calcium scoring (CACS) by cardiac computed tomography, we evaluated the performance of the Dr.Noon CVD score in detecting CACS using receiver operating characteristic curve analysis.

• Results Mean Dr.Noon CVD scores rose steadily with age from 14.2±2.9 (<30 years) to 46.3±6.5 (≥70 years), closely mirroring the increase in traditional cardiovascular risk factors with age. Additional analysis using CACS demonstrated that the Dr.Noon CVD score achieved an area under the curve of 0.80 (95% confidence interval, 0.80–0.81) for detecting any coronary calcification, defined as CACS >0, and an area under the curve of 0.82 (95% confidence interval, 0.81–0.83) for identifying significant calcification burden, defined as CACS >100.

• Conclusions Dr.Noon CVD scores were consistently correlated with age, conventional risk factors, and CACS, suggesting a potential role in broad-based cardiovascular risk stratification and in guiding personalized prevention strategies.