Fully automatic coronary calcium scoring in non-ECG-gated low-dose chest CT: comparison with ECG-gated cardiac CT

医学 组内相关 可靠性(半导体) 置信区间 卡帕 神经组阅片室 冠状动脉钙 科恩卡帕 放射科 计算机断层摄影术 核医学 心脏病学 内科学 机器学习 计算机科学 神经学 功率(物理) 哲学 物理 心理测量学 精神科 临床心理学 量子力学 语言学
作者
Young Joo Suh,Cherry Kim,June‐Goo Lee,Hongmin Oh,Hee Jun Kang,Young‐Hak Kim,Dong Hyun Yang
出处
期刊:European Radiology [Springer Nature]
卷期号:33 (2): 1254-1265 被引量:13
标识
DOI:10.1007/s00330-022-09117-3
摘要

To validate an artificial intelligence (AI)–based fully automatic coronary artery calcium (CAC) scoring system on non-electrocardiogram (ECG)–gated low-dose chest computed tomography (LDCT) using multi-institutional datasets with manual CAC scoring as the reference standard. This retrospective study included 452 subjects from three academic institutions, who underwent both ECG-gated calcium scoring computed tomography (CSCT) and LDCT scans. For all CSCT and LDCT scans, automatic CAC scoring (CAC_auto) was performed using AI-based software, and manual CAC scoring (CAC_man) was set as the reference standard. The reliability and agreement of CAC_auto was evaluated and compared with that of CAC_man using intraclass correlation coefficients (ICCs) and Bland-Altman plots. The reliability between CAC_auto and CAC_man for CAC severity categories was analyzed using weighted kappa (κ) statistics. CAC_auto on CSCT and LDCT yielded a high ICC (0.998, 95% confidence interval (CI) 0.998–0.999 and 0.989, 95% CI 0.987–0.991, respectively) and a mean difference with 95% limits of agreement of 1.3 ± 37.1 and 0.8 ± 75.7, respectively. CAC_auto achieved excellent reliability for CAC severity (κ = 0.918–0.972) on CSCT and good to excellent but heterogenous reliability among datasets (κ = 0.748–0.924) on LDCT. The application of an AI-based automatic CAC scoring software to LDCT shows good to excellent reliability in CAC score and CAC severity categorization in multi-institutional datasets; however, the reliability varies among institutions. • AI-based automatic CAC scoring on LDCT shows excellent reliability with manual CAC scoring in multi-institutional datasets. • The reliability for CAC score–based severity categorization varies among datasets. • Automatic scoring for LDCT shows a higher false-positive rate than automatic scoring for CSCT, and most common causes of a false-positive are image noise and artifacts for both CSCT and LDCT.
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