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Multicenter External Validation of an AI-Based Funduscopic Carotid Atherosclerosis Score and Assessment of Its
Changho Han1, Jooyoung Chang2,3, Jaewon Kim4
1Medical Big Data Research Center, Seoul National University Medical Research Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
Background:
Screening for atherosclerosis is essential for early intervention, but conventional screening methods are often invasive and resource-intensive. As a result, there is growing interest in leveraging AI with noninvasive tools such as retinal fundus imaging to enable opportunistic cardiovascular risk assessment. The deep-learning funduscopic atherosclerosis score (DL-FAS) is an AI-derived biomarker, generated by a deep learning model, that was developed in a previous study to reflect the likelihood of carotid artery atherosclerosis from retinal fundus images.
Objective:
This study aimed to externally validate DL-FAS in a multicenter health checkup population and investigate its association with coronary artery calcification to gain deeper insights into its ability to reflect the broader systemic atherosclerotic burden.
Methods:
We used data from 108,982 participants in a Korean health checkup population who underwent retinal fundus imaging and at least one of either carotid artery sonography or coronary artery calcium scoring across 5 health-promotion centers operated by the Korea Association of Health Promotion between 2018 and 2021. Carotid atherosclerosis was defined by increased intima-media thickness (≥ 0.9 mm), atheroma, or stenosis. A coronary artery calcium score >0 indicated the presence of coronary artery calcification. DL-FAS (range 0-1) was generated for each fundus image, and the average score from both eyes was used as the final DL-FAS when available. The discriminative performance of DL-FAS for carotid atherosclerosis was assessed using the area under the receiver operating characteristic curve. We performed multivariable logistic regression, adjusted for the Pooled Cohort Equations (PCE) 10-year cardiovascular risk score, to quantify the associations between DL-FAS and both outcomes.
Results:
The area under the receiver operating characteristic curve for detecting carotid atherosclerosis was 0.700 (95% CI 0.697-0.703), confirming the generalizability of DL-FAS across multiple centers. In multivariable logistic regression adjusted for the PCE score, a 10% absolute increase in DL-FAS was associated with both carotid atherosclerosis (odds ratio 1.29, 95% CI 1.28-1.31) and coronary artery calcification (odds ratio 1.29, 95% CI 1.24-1.33). These associations remained significant among participants aged <60 years, as well as within the PCE-defined low- and moderate-risk subgroups, highlighting the potential utility of DL-FAS in populations that may benefit most from early detection and intervention.
Conclusions:
DL-FAS was externally validated in a large, multicenter health checkup dataset and was significantly associated with both carotid atherosclerosis and coronary artery calcification. These findings highlight its potential as a noninvasive biomarker associated with systemic atherosclerotic burden and cardiovascular risk stratification, particularly in the context of opportunistic screening.
