Performance of artificial intelligence-based coronary artery calcium scoring in non-gated chest CT

2021 ◽  
pp. 110034
Author(s):  
Jie Xu ◽  
Jia Liu ◽  
Ning Guo ◽  
Linli Chen ◽  
Weixiang Song ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Coronary Artery ◽  
Coronary Artery Calcium ◽  
Chest Ct ◽  
Calcium Scoring ◽  
Coronary Artery Calcium Scoring

scholarly journals 2016 SCCT/STR guidelines for coronary artery calcium scoring of noncontrast noncardiac chest CT scans

2017 ◽  
Vol 32 (5) ◽  
pp. W54-W66 ◽  
Author(s):  
Harvey S. Hecht ◽  
Paul Cronin ◽  
Michael J. Blaha ◽  
Matthew J. Budoff ◽  
Ella A. Kazerooni ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Coronary Artery Calcium ◽  
Chest Ct ◽  
Ct Scans ◽  
Calcium Scoring ◽  
Coronary Artery Calcium Scoring

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

2019 ◽  
Vol 30 (3) ◽  
pp. 1671-1678 ◽  
Author(s):  
Mårten Sandstedt ◽  
Lilian Henriksson ◽  
Magnus Janzon ◽  
Gusten Nyberg ◽  
Jan Engvall ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Coronary Artery ◽  
Coronary Artery Calcium ◽  
Rank Test ◽  
Agatston Score ◽  
Risk Category ◽  
Automatic Method ◽  
Calcium Scoring ◽  
Excellent Correlation ◽  
Automatic Software

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.

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