Department of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Cardiac Sciences, University of Calgary, Calgary Alberta, Canada.
Department 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 Cardiovasc Imaging. 2024 Jul;17(7):780-791. doi: 10.1016/j.jcmg.2024.01.006. Epub 2024 Mar 6.
Computed tomography attenuation correction (CTAC) improves perfusion quantification of hybrid myocardial perfusion imaging by correcting for attenuation artifacts. Artificial intelligence (AI) can automatically measure coronary artery calcium (CAC) from CTAC to improve risk prediction but could potentially derive additional anatomic features.
The authors evaluated AI-based derivation of cardiac anatomy from CTAC and assessed its added prognostic utility.
The authors considered consecutive patients without known coronary artery disease who underwent single-photon emission computed tomography/computed tomography (CT) myocardial perfusion imaging at 3 separate centers. Previously validated AI models were used to segment CAC and cardiac structures (left atrium, left ventricle, right atrium, right ventricular volume, and left ventricular [LV] mass) from CTAC. They evaluated associations with major adverse cardiovascular events (MACEs), which included death, myocardial infarction, unstable angina, or revascularization.
In total, 7,613 patients were included with a median age of 64 years. During a median follow-up of 2.4 years (IQR: 1.3-3.4 years), MACEs occurred in 1,045 (13.7%) patients. Fully automated AI processing took an average of 6.2 ± 0.2 seconds for CAC and 15.8 ± 3.2 seconds for cardiac volumes and LV mass. Patients in the highest quartile of LV mass and left atrium, LV, right atrium, and right ventricular volume were at significantly increased risk of MACEs compared to patients in the lowest quartile, with HR ranging from 1.46 to 3.31. The addition of all CT-based volumes and CT-based LV mass improved the continuous net reclassification index by 23.1%.
AI can automatically derive LV mass and cardiac chamber volumes from CT attenuation imaging, significantly improving cardiovascular risk assessment for hybrid perfusion imaging.
计算机断层扫描衰减校正（CTAC）通过校正衰减伪影来提高混合心肌灌注成像的灌注定量。人工智能（AI）可以从 CTAC 自动测量冠状动脉钙（CAC），以改善风险预测，但可能会衍生出其他解剖特征。
作者评估了基于 AI 的 CTAC 心脏解剖结构自动推导，并评估了其附加的预后效用。
作者纳入了在 3 个不同中心接受单光子发射计算机断层扫描/计算机断层扫描（CT）心肌灌注成像的连续无已知冠状动脉疾病的患者。使用先前验证的 AI 模型从 CTAC 中分割 CAC 和心脏结构（左心房、左心室、右心房、右心室容积和左心室[LV]质量）。他们评估了与主要不良心血管事件（MACE）的关联，MACE 包括死亡、心肌梗死、不稳定型心绞痛或血运重建。
共纳入 7613 例患者，中位年龄为 64 岁。在中位随访 2.4 年（IQR：1.3-3.4 年）期间，1045 例（13.7%）患者发生 MACE。完全自动化的 AI 处理过程，CAC 平均需要 6.2 ± 0.2 秒，心脏容积和 LV 质量平均需要 15.8 ± 3.2 秒。与最低四分位数的患者相比，LV 质量和左心房、LV、右心房和右心室容积最高四分位数的患者发生 MACE 的风险显著增加，HR 范围为 1.46 至 3.31。所有基于 CT 的容积和基于 CT 的 LV 质量的增加使连续净重新分类指数提高了 23.1%。
AI 可以从 CT 衰减成像自动推导 LV 质量和心脏腔室容积，显著提高混合灌注成像的心血管风险评估。
