扬抑
钙化积分
医学
卡帕
右冠状动脉
动脉
人工智能
算法
心脏病学
内科学
冠状动脉钙
冠状动脉疾病
冠状动脉造影
计算机科学
数学
心肌梗塞
几何学
作者
David Winkel,Venkata Suryanarayana,Ajmol Ali,Johannes Görich,Sebastian J. Buss,Axel Mendoza,Chris Schwemmer,Puneet Sharma,U. Joseph Schoepf,Saikiran Rapaka
出处
期刊:European Journal of Echocardiography
[Oxford University Press]
日期:2021-07-29
卷期号:23 (6): 846-854
被引量:19
标识
DOI:10.1093/ehjci/jeab119
摘要
Abstract Aims To present and validate a fully automated, deep learning (DL)-based branch-wise coronary artery calcium (CAC) scoring algorithm on a multi-centre dataset. Methods and results We retrospectively included 1171 patients referred for a CAC computed tomography examination. Total CAC scores for each case were manually evaluated by a human reader. Next, each dataset was fully automatically evaluated by the DL-based software solution with output of the total CAC score and sub-scores per coronary artery (CA) branch [right coronary artery (RCA), left main (LM), left anterior descending (LAD), and circumflex (CX)]. Three readers independently manually scored the CAC for all CA branches for 300 cases from a single centre and formed the consensus using a majority vote rule, serving as the reference standard. Established CAC cut-offs for the total Agatston score were used for risk group assignments. The performance of the algorithm was evaluated using metrics for risk class assignment based on total Agatston score, and unweighted Cohen’s Kappa for branch label assignment. The DL-based software solution yielded a class accuracy of 93% (1085/1171) with a sensitivity, specificity, and accuracy of detecting non-zero coronary calcium being 97%, 93%, and 95%. The overall accuracy of the algorithm for branch label classification was 94% (LM: 89%, LAD: 91%, CX: 93%, RCA: 100%) with a Cohen's kappa of k = 0.91. Conclusion Our results demonstrate that fully automated total and vessel-specific CAC scoring is feasible using a DL-based algorithm. There was a high agreement with the manually assessed total CAC from a multi-centre dataset and the vessel-specific scoring demonstrated consistent and reproducible results.
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