Abstract: Objective: To investigate the feasibility of using non-gated high-pitch chest computed tomography (CT) combined with artificial intelligence (AI) technology to measure the coronary artery calcium score. Methods: In this retrospective analysis, we reviewed the images of 24 patients who underwent both non-gated high-pitch chest CT and coronary artery calcium scoring CT. The scanning parameters were as follows: ① non-gated high-pitch chest CT scan: CarekV and CareDose 4D, ref.kV 110, ref.mAs 80, pitch 3, filter core BR40; ② ECG-gated coronary artery calcification score CT: CarekV, CareDose 4D, ref.kV 120, ref.mAs 50, pitch 0.15, acquisition R-R interval 35% and 75%, filter core QR36. The image window width/window level of the two groups was 345/50, and the slice thickness/interval was 3 mm/1.5 mm. The images of the two scan groups were divided into three subgroups according to the three measurement methods (Syngo.via workstation measurement, AI measurement, and AI + manual correction measurement). The image calcification score (Agatston Score, AS) was measured, risk classification was performed, and the time taken for the workstation measurement and the AI + manual correction measurement was recorded. The volume dose index (CTDI_vol) and dose-length product (DLP) of the two examinations were recorded, and the effective dose (ED) was calculated. SPSS Statistics software (version 27.0) was used for the statistical analysis. Results: The tube voltages of the high-pitch chest CT were 100, 110, and 120 kV, and the coronary artery calcium score was 120 kV. EDs of high-pitch chest CT and coronary artery calcium score CT were 2.1±0.4 and 2.1±0.7 mSv, respectively. The three measurement methods used for high-pitch chest CT showed significant differences in AS (X²=27.163, P < 0.001), but the consistency of AS was high (ICC: 0.988). Regarding AS measurement, high-pitch chest CT combined with AI (X² = 4.795, P = 0.091, ICC: 0.990), high-pitch chest CT using a workstation (Z = 0.912, P = 0.362, ICC: 0.988), and gated calcium scoring combined with AI (X² = 10.900, P = 0.004, ICC: 0.980) showed no significant differences or high consistency compared to the workstation for coronary artery calcium scoring. The risk classification obtained by the AI was highly consistent, and the weighted kappa coefficients were between 0.818 and 1.000. The time taken for measurements using the workstation and AI + manual correction in both scanning groups showed significant differences (Z = 4.200, 4.049, P < 0.001). Conclusion: Non-gated high-pitch chest CT combined with AI can provide reliable results for artery calcium scores and risk classification with reduced time consumption, demonstrating substantial clinical utility.