Artificial intelligence-assisted longitudinal assessment of coronary artery calcification in the Korean lung cancer screening CT program

This study investigated the progression of coronary artery calcification (CAC) and its association with adverse cardiovascular events (ACEs) in participants of the Korean Lung Cancer Screening (LCS) program using low-dose chest CT (LDCT). A total of 193 male participants were followed for an average of 4 years. Greater baseline CAC severity predicted a faster annual CAC score increase. Among baseline CAC-negative individuals, 20.9% developed incident CAC, and 44.1% of baseline-positive participants showed CAC progression. AI-based software (AVIEW CAC by Coreline Soft) using the Agatston method was used for CAC scoring. Patients with CAC growth showed a significantly higher rate of ACEs. This study suggests that AI-assisted longitudinal CAC tracking via LDCT can provide meaningful prognostic value and may guide individualized cardiovascular risk assessment and follow-up strategies in clinical practice.