利用机器学习算法比较心脏计算机断层扫描血管造影和心脏磁共振成像的左心室质量和室壁厚度。

European heart journal. Imaging methods and practice Pub Date : 2024-07-11 eCollection Date: 2024-07-01 DOI:10.1093/ehjimp/qyae069
Finn Y van Driest, Rob J van der Geest, Sharif K Omara, Alexander Broersen, Jouke Dijkstra, J Wouter Jukema, Arthur J H A Scholte
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引用次数: 0

摘要

目的:心脏磁共振成像(MRI)是评估左心室质量和室壁厚度的金标准。近年来,心脏计算机断层扫描(CCTA)作为一种成像方式得到了广泛应用。尽管如此,以往专门针对 CCTA 作为定量评估左心室的替代方法的潜力的研究仍然有限。本研究旨在利用机器学习算法将 CCTA 得出的左心室质量和室壁厚度与心脏核磁共振成像进行比较:确定了 57 名同时接受 CCTA 和心脏核磁共振成像的参与者。使用内部开发的机器学习模型自动绘制的左心室轮廓计算左心室质量和室壁厚度。计算皮尔逊相关系数并绘制布兰-阿尔特曼图,以评估 CCTA 和心脏核磁共振每个区域的左心室质量和室壁厚度之间的一致性。使用皮尔逊相关系数检验观察者之间的相关性。CCTA 和心脏磁共振成像的平均左心室质量和室壁厚度分别为 127 克、128 克、7 毫米和 8 毫米。Bland-Altman图显示,左心室质量和左心室平均壁厚的平均差和相应的95%一致性限分别为-1.26(25.06;-27.58)和-0.57(1.78;-2.92)。基底区、中间区和心尖区每个区域室壁厚度的平均差和相应的 95% 一致性限分别为-0.75 (1.34; -2.83)、-0.58 (2.14; -3.30)和-0.29 (3.21; -3.79)。观察者之间的相关性非常好:结论:使用机器学习算法在 CCTA 上对左心室质量和室壁厚度进行定量评估似乎是可行的,并且与心脏核磁共振成像显示出良好的一致性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of left ventricular mass and wall thickness between cardiac computed tomography angiography and cardiac magnetic resonance imaging using machine learning algorithms.

Aims: Cardiac magnetic resonance imaging (MRI) is the gold standard in the assessment of left ventricle (LV) mass and wall thickness. In recent years, cardiac computed tomography angiography (CCTA) has gained widespread usage as an imaging modality. Despite this, limited previous investigations have specifically addressed the potential of CCTA as an alternative modality for quantitative LV assessment. The aim of this study was to compare CCTA derived LV mass and wall thickness with cardiac MRI utilizing machine learning algorithms.

Methods and results: Fifty-seven participants who underwent both CCTA and cardiac MRI were identified. LV mass and wall thickness was calculated using LV contours which were automatically placed using in-house developed machine learning models. Pearson's correlation coefficients were calculated along with Bland-Altman plots to assess the agreement between the LV mass and wall thickness per region on CCTA and cardiac MRI. Inter-observer correlations were tested using Pearson's correlation coefficient. Average LV mass and wall thickness for CCTA and cardiac MRI were 127 g, 128 g, 7, and 8 mm, respectively. Bland-Altman plots demonstrated mean differences and corresponding 95% limits of agreement of -1.26 (25.06; -27.58) and -0.57 (1.78; -2.92), for LV mass and average LV wall thickness, respectively. Mean differences and corresponding 95% limits of agreement for wall thickness per region were -0.75 (1.34; -2.83), -0.58 (2.14; -3.30), and -0.29 (3.21; -3.79) for the basal, mid, and apical regions, respectively. Inter-observer correlations were excellent.

Conclusion: Quantitative assessment of LV mass and wall thickness on CCTA using machine learning algorithms seems feasible and shows good agreement with cardiac MRI.

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