Han Donghee, Shanbhag Aakash, Miller Robert Jh, Kwok Nicholas, Waechter Parker, Builoff Valerie, Newby David E, Dey Damini, Berman Daniel S, Slomka Piotr
Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, California, USA.
Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.
JACC Adv. 2024 Sep 12;3(10):101249. doi: 10.1016/j.jacadv.2024.101249. eCollection 2024 Oct.
Noncontrast computed tomography (CT) scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (CMR).
The purpose of the study was to assess the feasibility of LV mass estimation from standard, ECG-gated, noncontrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and CMR.
We enrolled consecutive patients who underwent coronary CTA, which included noncontrast CT calcium scanning and contrast CTA, and CMR. The median interval between coronary CTA and CMR was 22 days (interquartile range: 3-76). We utilized a no new UNet AI model that automatically segmented noncontrast CT structures. AI measurement of LV mass was compared to contrast CTA and CMR.
A total of 316 patients (age: 57.1 ± 16.7 years, 56% male) were included. The AI segmentation took on average 22 seconds per case. An excellent correlation was observed between AI and contrast CTA LV mass measures (r = 0.84, < 0.001), with no significant differences (136.5 ± 55.3 g vs 139.6 ± 56.9 g, = 0.133). Bland-Altman analysis showed minimal bias of 2.9. When compared to CMR, measured LV mass was higher with AI (136.5 ± 55.3 g vs 127.1 ± 53.1 g, < 0.001). There was an excellent correlation between AI and CMR (r = 0.85, < 0.001), with a small bias (-9.4). There were no statistical differences between the correlations of LV mass between contrast CTA and CMR or AI and CMR.
The AI-based automated estimation of LV mass from noncontrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and CMR.
非增强计算机断层扫描（CT）未用于评估左心室心肌质量（LV质量），LV质量通常通过增强CT或心血管磁共振成像（CMR）进行评估。
本研究的目的是评估使用人工智能（AI）方法从标准的、心电图门控的非增强CT估计LV质量的可行性，并将其与冠状动脉CT血管造影（CTA）和CMR进行比较。
我们纳入了连续接受冠状动脉CTA的患者，其中包括非增强CT钙扫描和增强CTA以及CMR。冠状动脉CTA和CMR之间的中位间隔为22天（四分位间距：3 - 76天）。我们使用了一种新型的UNet AI模型，该模型可自动分割非增强CT结构。将AI测量的LV质量与增强CTA和CMR进行比较。
共纳入316例患者（年龄：57.1±16.7岁，56%为男性）。AI分割平均每个病例耗时22秒。观察到AI与增强CTA测量的LV质量之间具有良好的相关性（r = 0.84，P < 0.001），无显著差异（136.5±55.3克 vs 139.6±56.9克，P = 0.133）。Bland - Altman分析显示偏差极小，为2.9。与CMR相比，AI测量的LV质量更高（136.5±55.3克 vs 127.1±53.1克，P < 0.001）。AI与CMR之间具有良好的相关性（r = 0.85，P < 0.001），偏差较小（-9.4）。增强CTA与CMR或AI与CMR之间LV质量的相关性无统计学差异。
与增强CTA和CMR相比，基于AI的非增强CT自动估计LV质量显示出良好的相关性和极小的偏差。
