The CAC_auto system, which is a deep learning-based fully automatic calcium scoring system, was validated in this study using previously published cardiac CT cohort data, with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. The AVIEW CAC software was used for the automatic CAC measurement. The results showed that the CAC_auto system provided accurate calcium score measurement and risk category classification, with high sensitivity and low false-positive rates in detecting coronary calcium, as well as high reliability in measuring the Agatston score per vessel and per patient. The main causes of false-positive results were image noise, aortic wall calcification, and pericardial calcification. The study suggests that the CAC_auto system could potentially streamline CAC imaging workflows.