Accuracy Evaluation of Coronary Artery Calcification Score by Non Gated Chest CT Scan Based on Artificial Intelligence Technology
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摘要: 目的：采用人工智能方法，探讨非门控胸部低剂量CT平扫对冠状动脉钙化积分（CACS）评价的准确性。方法：回顾性分析行冠状动脉CTA扫描的100例患者，所有患者均行心电门控钙化积分CT（ECGgated-CT）扫描和常规非门控胸部CT平扫检查，在Siemens后处理工作站采用Agatston钙化积分软件记录心电门控CT钙化积分，采用数坤科技胸部CT人工智能钙化积分软件记录非门控胸部CT平扫的钙化积分。两种扫描方法分别获得Agatston评分，其中包括冠状动脉总评分（TOTAL），左主干（LM）评分，前降支（LAD）评分，回旋支（CX）评分，右冠状动脉（RCA）评分，采用配对t检验比较两组钙化积分的统计学差异，采用Pearson相关系数分析两组钙化积分的相关性，采用组内相关性系数（ICC）对两组钙化积分的危险度分层进行一致性检验，P<0.05为有统计学显著差异。结果：采用配对t检验比较发现两组方法测量TOTAL，LM、CX、RCA的Agatston积分无统计学显著差异（P<0.05），LAD的钙化积分两组间有统计学显著差异（P<0.05），Pearson分析发现两组间TOTAL，LM、CX、RCA、LAD的Agatston积分均有显著相关性。ICC分析发现两组所获的Agatston钙化积分在危险度分层方面具有较好的一致性，组内相关系数为0.938，P<0.001。结论：基于人工智能技术的非门控胸部CT平扫对冠状动脉钙化积分的评价具有较高的准确性，在危险度分层方面与传统门控检查一致性很好，可用于冠心病风险的筛查评估。Abstract: Objective: To evaluate the accuracy of coronary artery calcification score(CACS) by non-gated low-dose chest CT plain scan using artificial intelligence. Methods: A total of 100 cases of patients with coronary CTA scanning were retrospectively analyzed. All patients were selected for ECG gated CT scan and routine non-gated chest CT plain scan. The calcification score of non-gated chest CT plain scan was recorded by Agatston calcification integral software in Siemens post-processing workstation, and the calcification score of non-gated chest CT plain scan was recorded by artificial intelligence analysis software. Two scanning methods were used to obtain Agatston score, including total coronary artery(TOTAL) score, left main artery(LM) score, left Left anterior descending artery(LAD) score, circumflexcoronaryartery(CX) score, and right coronary artery(RCA) score. Paired T-test was used to compare the statistical differences between the two groups. Pearson correlation coefficient was used to analyze the correlation of the two groups' calcification scores, and intra-group correlation coefficient(ICC) was used to test the consistency of risk stratification of the two groups' calcification scores. P<0.05 was considered statistically significant. Results: there was no statistically significant difference in the Agatston score of LM, CX and RCA between the two groups(P<0.05), while there was a statistically significant difference in the calcification score of LAD between the two groups(P<0.05). Pearson analysis found the significant correlation between the two groups in the Agatston score of TOTAL, LM, CX, RCA and LAD. ICC analysis found that the Agatston calcification scores of the two groups showed good consistency in risk stratification, with an intra-group correlation coefficient of 0.938, P<0.001. Conclusion: The artificial intelligence-based non-gated chest CT plain scan has a high accuracy in the evaluation of coronary artery calcification score, which is in good agreement with the traditional gated examination in terms of risk stratification, and can be used for the screening and assessment of coronary heart disease risk.
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期刊类型引用(8)
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