Abstract Background Cardiovascular disease (CVD) risk is assessed through standard risk assessment tools such as the Pooled Cohort Equation (PCE), QRISK3, and modified Framingham Risk Score (FRS). Previously, we developed a novel biomarker based on retinal photographs and a deep-learning algorithm (Reti-CVD). Aims This study aims to evaluate Reti-CVD’s ability to identify individuals with intermediate- and high-risk for CVD. Methods We defined the intermediate- and high-risk groups according to PCE, QRISK3, and modified FRS. Reti-CVD’s prediction was compared to the number of individuals identified as intermediate- and high-risk according to standard CVD risk assessment tools and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the results. Results In the UK Biobank, among 48,260 participants, 20,643 (42.8%) and 7192 (14.9%) were classified into the intermediate- and high-risk groups according to PCE, and QRISK3, respectively. In the Singapore Epidemiology of Eye Diseases (SEED) study, among 6810 participants, 3799 (55.8%) were classified as intermediate- and high-risk group according to modified FRS. Reti-CVD identified PCE-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.7%, 87.6%, 86.5%, and 84.0%, respectively. Reti-CVD identified QRISK3-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.6%, 85.5%, 49.9%, and 96.6%, respectively. Reti-CVD identified intermediate- and high-risk groups according to the modified FRS with a sensitivity, specificity, PPV, and NPV of 73.4%, 82.0%, 75.7%, and 80.1%, respectively. Conclusion The retinal photograph biomarker (Reti-CVD) was able to identify individuals with intermediate and high-risk for CVD, in accordance with existing risk assessment tools.