人工智能在标准心脏ct和不同重建核的胸部ct冠状动脉钙化评分中的作用。

IF 2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Thoracic Imaging Pub Date : 2024-03-01 Epub Date: 2023-11-20 DOI:10.1097/RTI.0000000000000765
Yenpo Lin, Gigin Lin, Meng-Ting Peng, Chi-Tai Kuo, Yung-Liang Wan, Wen-Jin Cherng
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引用次数: 0

摘要

目的:评价人工智能(AI)获得的冠状动脉钙化评分(CS)与不同重建核数的心电图门控标准心脏计算机断层扫描(CCT)和非栅格胸部计算机断层扫描(ChCT)的相关性。患者和方法:76例患者同时接受标准CCT和ChCT。我们比较了四组CS: CSCCT,采用传统方法获得标准CCT, 25 cm视场,3 mm切片厚度,核滤波器卷积12 (FC12);CSAICCT,由AI从标准的CCT;CSChCTsoft,由人工智能从非门控CCT, 40厘米的视野,3毫米的切片厚度,和一个软核FC02;CSChCTsharp和CSChCTsharp,由人工智能从CSChCTsoft的CCT图像中获得相同的参数,只是使用了锐利内核FC56。统计分析包括Spearman等级相关系数(ρ)、类内相关系数(ICC)、Bland-Altman图和加权kappa分析(κ)。结果:CSAICCT与CSCCT结果一致(ρ = 0.994, ICC为1.00,P < 0.001),与Agatston评分的心血管(CV)危险类别吻合极好(κ = 1.000)。CSChCTsoft与CSChCTsharp的相关性较好(ρ = 0.912、0.963,ICC = 0.929、0.948,P < 0.001),有低估倾向(Bland-Altman平均差值和95%上下限分别为329.1[-798.9 ~ 1457]和335.3[-651.9 ~ 1322])。CSChCTsoft和CSChCTsharp的CV风险类别一致性中等(κ分别= 0.556和0.537)。结论:CSCCT和CSAICCT之间有很好的相关性,CV风险类别之间有很好的一致性。CSCCT与ChCT获得的CS之间也有良好的相关性,尽管在CV风险评估方面存在低估和中等准确性的倾向。
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The Role of Artificial Intelligence in Coronary Calcium Scoring in Standard Cardiac Computed Tomography and Chest Computed Tomography With Different Reconstruction Kernels.

Purpose: To assess the correlation of coronary calcium score (CS) obtained by artificial intelligence (AI) with those obtained by electrocardiography gated standard cardiac computed tomography (CCT) and nongated chest computed tomography (ChCT) with different reconstruction kernels.

Patients and methods: Seventy-six patients received standard CCT and ChCT simultaneously. We compared CS obtained in 4 groups: CS CCT , by the traditional method from standard CCT, 25 cm field of view, 3 mm slice thickness, and kernel filter convolution 12 (FC12); CS AICCT , by AI from the standard CCT; CS ChCTsoft , by AI from the non-gated CCT, 40 cm field of view, 3 mm slice thickness, and a soft kernel FC02; and CS ChCTsharp , by AI from CCT image with same parameters for CS ChCTsoft except for using a sharp kernel FC56. Statistical analyses included Spearman rank correlation coefficient (ρ), intraclass correlation (ICC), Bland-Altman plots, and weighted kappa analysis (κ).

Results: The CS AICCT was consistent with CS CCT (ρ = 0.994 and ICC of 1.00, P < 0.001) with excellent agreement with respect to cardiovascular (CV) risk categories of the Agatston score (κ = 1.000). The correlation between CS ChCTsoft and CS ChCTsharp was good (ρ = 0.912, 0.963 and ICC = 0.929, 0.948, respectively, P < 0.001) with a tendency of underestimation (Bland-Altman mean difference and 95% upper and lower limits of agreements were 329.1 [-798.9 to 1457] and 335.3 [-651.9 to 1322], respectively). The CV risk category agreement between CS ChCTsoft and CS ChCTsharp was moderate (κ = 0.556 and 0.537, respectively).

Conclusions: There was an excellent correlation between CS CCT and CS AICCT , with excellent agreement between CV risk categories. There was also a good correlation between CS CCT and CS obtained by ChCT albeit with a tendency for underestimation and moderate accuracy in terms of CV risk assessment.

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来源期刊
Journal of Thoracic Imaging
Journal of Thoracic Imaging 医学-核医学
CiteScore
7.10
自引率
9.10%
发文量
87
审稿时长
6-12 weeks
期刊介绍: Journal of Thoracic Imaging (JTI) provides authoritative information on all aspects of the use of imaging techniques in the diagnosis of cardiac and pulmonary diseases. Original articles and analytical reviews published in this timely journal provide the very latest thinking of leading experts concerning the use of chest radiography, computed tomography, magnetic resonance imaging, positron emission tomography, ultrasound, and all other promising imaging techniques in cardiopulmonary radiology. Official Journal of the Society of Thoracic Radiology: Japanese Society of Thoracic Radiology Korean Society of Thoracic Radiology European Society of Thoracic Imaging.
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