Evaluation of an AI-based, automatic coronary artery calcium scoring software.

Affiliation

Sandstedt M(1)(2), Henriksson L(3)(4), Janzon M(5), Nyberg G(3)(4), Engvall J(3)(6), De Geer J(3)(4), Alfredsson J(5), Persson A(3)(4).
Author information:
(1)Center for Medical Image Science and Visualization
(CMIV), Linköping University, Linköping, Sweden. [Email]
(2)Department of Radiology and Department of Medical and Health Sciences, University Hospital of Linköping, Linköping University, SE-581 85, Linköping, Sweden. [Email]
(3)Center for Medical Image Science and Visualization
(CMIV), Linköping University, Linköping, Sweden.
(4)Department of Radiology and Department of Medical and Health Sciences, University Hospital of Linköping, Linköping University, SE-581 85, Linköping, Sweden.
(5)Department of Cardiology and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.
(6)Department of Clinical Physiology and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.

Abstract

OBJECTIVES: To evaluate an artificial intelligence (AI)-based, automatic coronary artery calcium (CAC) scoring software, using a semi-automatic software as a reference. METHODS: This observational study included 315 consecutive, non-contrast-enhanced calcium scoring computed tomography (CSCT) scans. A semi-automatic and an automatic software obtained the Agatston score (AS), the volume score (VS), the mass score (MS), and the number of calcified coronary lesions. Semi-automatic and automatic analysis time were registered, including a manual double-check of the automatic results. Statistical analyses were Spearman's rank correlation coefficient (⍴), intra-class correlation (ICC), Bland Altman plots, weighted kappa analysis (κ), and Wilcoxon signed-rank test. RESULTS: The correlation and agreement for the AS, VS, and MS were ⍴ = 0.935, 0.932, 0.934 (p < 0.001), and ICC = 0.996, 0.996, 0.991, respectively (p < 0.001). The correlation and agreement for the number of calcified lesions were ⍴ = 0.903 and ICC = 0.977 (p < 0.001), respectively. The Bland Altman mean difference and 1.96 SD upper and lower limits of agreements for the AS, VS, and MS were - 8.2 (- 115.1 to 98.2), - 7.4 (- 93.9 to 79.1), and - 3.8 (- 33.6 to 25.9), respectively. Agreement in risk category assignment was 89.5% and κ = 0.919 (p < 0.001). The median time for the semi-automatic and automatic method was 59 s (IQR 35-100) and 36 s (IQR 29-49), respectively (p < 0.001). CONCLUSIONS: There was an excellent correlation and agreement between the automatic software and the semi-automatic software for three CAC scores and the number of calcified lesions. Risk category classification was accurate but showing an overestimation bias tendency. Also, the automatic method was less time-demanding. KEY POINTS: • Coronary artery calcium (CAC) scoring is an excellent candidate for artificial intelligence (AI) development in a clinical setting. • An AI-based, automatic software obtained CAC scores with excellent correlation and agreement compared with a conventional method but was less time-consuming.