■冠状动脉钙积分(CACS)已被证明是心血管事件的独立预测因子。传统的冠状动脉钙质评分算法已经针对心电图(ECG)门控图像进行了优化,这些都是通过特定的设置和定时获取的。因此,如果基于人工智能的冠状动脉钙积分(AI-CACS)可以从胸部低剂量计算机断层扫描(LDCT)检查计算,它在提前评估冠状动脉疾病(CAD)的风险方面可能是有价值的,它有可能减少患者心血管事件的发生。这项研究旨在评估AI-CACS算法在三种不同切片厚度(1、3和5mm)的非门控胸部扫描中的性能。
■共有135例同时接受胸部LDCT和ECG门控非对比增强心脏CT的患者被前瞻性纳入本研究。Agatston评分是使用AI-CACS软件从在1、3和5mm的切片厚度下重建的胸部CT图像中自动得出的。然后使用常规的半自动方法作为参考,将这些评分与从ECG门控心脏CT数据获得的评分进行比较。分析AI-CACS与心电图门控冠状动脉钙化积分(ECG-CACS)的相关性,和Bland-Altman地块被用来评估协议。风险分层基于计算的CACS,并确定了一致率。
■总共112名患者被纳入最终分析。三种不同厚度(1、3、5mm)的AI-CACS与ECG-CACS的相关性分别为0.973、0.941、0.834(均P<0.01)。分别。Bland-Altman图显示了三种厚度分别为-6.5、15.4和53.1的AI-CACS的平均差异。三个AI-CACS组的风险类别一致性分别为0.868、0.772和0.412(均P<0.01)。分别。虽然一致性率为91%,84.8%,62.5%,分别。
■基于AI的算法成功地从胸部的LDCT扫描中计算出CACS,证明了它在风险分类中的效用。此外,从切片厚度为1mm的图像获得的CACS比从切片厚度为3和5mm的图像获得的CACS更准确.
UNASSIGNED: The coronary artery calcium score (CACS) has been shown to be an independent predictor of cardiovascular events. The traditional coronary artery calcium scoring algorithm has been optimized for electrocardiogram (ECG)-gated images, which are acquired with specific settings and timing. Therefore, if the artificial intelligence-based coronary artery calcium score (AI-CACS) could be calculated from a chest low-dose computed tomography (LDCT) examination, it could be valuable in assessing the risk of coronary artery disease (CAD) in advance, and it could potentially reduce the occurrence of cardiovascular events in patients. This study aimed to assess the performance of an AI-CACS algorithm in non-gated chest scans with three different slice thicknesses (1, 3, and 5 mm).
UNASSIGNED: A total of 135 patients who underwent both LDCT of the chest and ECG-gated non-contrast enhanced cardiac CT were prospectively included in this study. The Agatston scores were automatically derived from chest CT images reconstructed at slice thicknesses of 1, 3, and 5 mm using the AI-CACS software. These scores were then compared to those obtained from the ECG-gated cardiac CT data using a conventional semi-automatic method that served as the reference. The correlations between the AI-CACS and electrocardiogram-gated coronary artery calcium score (ECG-CACS) were analyzed, and Bland-Altman plots were used to assess agreement. Risk stratification was based on the calculated CACS, and the concordance rate was determined.
UNASSIGNED: A total of 112 patients were included in the final analysis. The correlations between the AI-CACS at three different thicknesses (1, 3, and 5 mm) and the ECG-CACS were 0.973, 0.941, and 0.834 (all P<0.01), respectively. The Bland-Altman plots showed mean differences in the AI-CACS for the three thicknesses of -6.5, 15.4, and 53.1, respectively. The risk category agreement for the three AI-CACS groups was 0.868, 0.772, and 0.412 (all P<0.01), respectively. While the concordance rates were 91%, 84.8%, and 62.5%, respectively.
UNASSIGNED: The AI-based algorithm successfully calculated the CACS from LDCT scans of the chest, demonstrating its utility in risk categorization. Furthermore, the CACS derived from images with a slice thickness of 1 mm was more accurate than those obtained from images with slice thicknesses of 3 and 5 mm.