Ability of artificial intelligence to diagnose coronary artery stenosis using hybrid images of coronary computed tomography angiography and myocardial perfusion SPECT.
Ability of artificial intelligence to diagnose coronary artery stenosis using hybrid images of coronary computed tomography angiography and myocardial perfusion SPECT.
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DOI：
10.1186/s41824-019-0052-8
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发表时间：
2019-03-18
影响因子：
1.7
通讯作者：
Kinuya S
中科院分区：
文献类型：
--
作者：
Yoneyama H;Nakajima K;Taki J;Wakabayashi H;Matsuo S;Konishi T;Okuda K;Shibutani T;Onoguchi M;Kinuya S
仅使用心肌灌注单光子发射计算机断层扫描（SPECT）检测缺血患者的罪魁祸首冠状动脉可能具有挑战性。本研究旨在利用人工神经网络（ANN）对冠状动脉计算机断层血管造影（CCTA）和心肌灌注SPECT的混合图像进行分析，以提高对罪魁祸首区域的检测。本研究招募了59例稳定型冠状动脉疾病（CAD）患者，他们在心肌灌注SPECT后60天内通过冠状动脉造影进行评估。两名核医学医生对心肌灌注SPECT和混合图像进行了四级置信度分析，然后在极坐标图上绘制区域，以确定罪魁祸首冠状动脉。金标准是由另外两名核心脏病学专家根据冠状动脉造影结果和临床信息一致确定的。对经验丰富的核心脏病专家和人工神经网络检测罪魁祸首冠状动脉的能力进行了比较。分析受试者工作特征（ROC）曲线，确定ROC曲线下面积（AUC）。使用混合图像，观察者A在右（RCA）、左前降（LAD）和左旋（LCX）冠状动脉中检测到CAD的准确率分别为83.6%、89.3%和94.4%，观察者B分别为72.9%、84.2%和89.3%。每条冠状动脉的ANN准确率分别为79.1%、89.8%和89.3%。人工神经网络的诊断准确性与经验丰富的核医学医生相当。使用RCA区域的混合图像，AUC显著提高（观察者A：从0.715到0.835,p = 0.0031；观察者B：从0.771到0.843,p = 0.042）。由于LCX和RCA的灌注区域因人而异，因此不使用混合图像检测下壁灌注缺陷的罪魁祸首冠状动脉是有问题的。CCTA与心肌灌注SPECT的混合成像对检测罪魁祸首冠状动脉是有用的。使用人工智能的诊断与核医学医生的诊断相当。
Detecting culprit coronary arteries in patients with ischemia using only myocardial perfusion single-photon emission computed tomography (SPECT) can be challenging. This study aimed to improve the detection of culprit regions using an artificial neural network (ANN) to analyze hybrid images of coronary computed tomography angiography (CCTA) and myocardial perfusion SPECT. This study enrolled 59 patients with stable coronary artery disease (CAD) who had been assessed by coronary angiography within 60 days of myocardial perfusion SPECT. Two nuclear medicine physicians interpreted the myocardial perfusion SPECT and hybrid images with four grades of confidence, then drew regions on polar maps to identify culprit coronary arteries. The gold standard was determined by the consensus of two other nuclear cardiology specialist based on coronary angiography findings and clinical information. The ability to detect culprit coronary arteries was compared among experienced nuclear cardiologists and the ANN. Receiver operating characteristics (ROC) curves were analyzed and areas under the ROC curves (AUC) were determined. Using hybrid images, observer A detected CAD in the right (RCA), left anterior descending (LAD), and left circumflex (LCX) coronary arteries with 83.6%, 89.3%, and 94.4% accuracy, respectively and observer B did so with 72.9%, 84.2%, and 89.3%, respectively. The ANN was 79.1%, 89.8%, and 89.3% accurate for each coronary artery. Diagnostic accuracy was comparable between the ANN and experienced nuclear medicine physicians. The AUC was significantly improved using hybrid images in the RCA region (observer A: from 0.715 to 0.835, p = 0.0031; observer B: from 0.771 to 0.843, p = 0.042). To detect culprit coronary arteries in perfusion defects of the inferior wall without using hybrid images was problematic because the perfused areas of the LCX and RCA varied among individuals. Hybrid images of CCTA and myocardial perfusion SPECT are useful for detecting culprit coronary arteries. Diagnoses using artificial intelligence are comparable to that by nuclear medicine physicians.
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影响因子：
37.8
作者：
Hachamovitch, R;Hayes, SW;Berman, DS
通讯作者：
Berman, DS
影响因子：
2.6
作者：
Kiriyama, Tomonari;Fukushima, Yoshimitsu;Kumita, Shin-ichiro
通讯作者：
Kumita, Shin-ichiro
影响因子：
2.6
作者：
Nakajima, Kenichi;Matsuo, Shinro;Okuda, Koichi
通讯作者：
Okuda, Koichi
影响因子：
2.4
作者：
Nakajima, Kenichi;Okuda, Koichi;Kawano, Masaya;Matsuo, Shinro;Slomka, Piotr;Germano, Guido;Kinuya, Seigo
通讯作者：
Kinuya, Seigo
DOI：
10.1007/s12350-014-0027-x
发表时间：
2015-10
期刊：
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
影响因子：
--
作者：
Arsanjani R;Dey D;Khachatryan T;Shalev A;Hayes SW;Fish M;Nakanishi R;Germano G;Berman DS;Slomka P
通讯作者：
Slomka P