The epicardial adipose tissue volume (EATV) was quantitatively measured by deep learning-based computed tomography (CT) images, and its correlation with coronary heart disease (CHD) was investigated in this study. 150 patients who underwent coronary artery CT examination in hospital were taken as research objects. Besides, patients from the observation group (group A) suffered from vascular stenosis, while patients from the control group (group B) had no vascular stenosis. The deep convolutional neural network model was applied to construct deep learning algorithm, and deep learning-based CT images were adopted to quantitatively measure EATV. The results showed that the sensitivity, specificity, accuracy, and area under the curve (AUC) of the deep learning algorithm were 0.8512, 0.9899, 0.9623, and 0.9813, respectively. By comparison, the correlation results of the traditional George algorithm, Aslani algorithm, and Lahiri algorithm were all lower than those of the deep learning algorithm, and the difference was statistically substantial (). The epicardial adipose tissue volume of the observation group (114.23 ± 55.46) was higher markedly than the volume of the control group (92.65 ± 43.28), with a statistically huge difference (). The r values of EATV with plaque properties and the number of stenosed coronary vessels were 0.232 and 0.268 in turn, both showing significant positive correlation. In conclusion, the sensitivity and other index values of deep learning algorithm were improved greatly compared with traditional algorithm. CT images based on deep learning algorithm achieved good blood vessel segmentation effects. In addition, EATV was closely related to the development of CHD.

中文翻译：

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基于深度学习的计算机断层扫描图像用于定量测量心外膜脂肪组织体积与冠心病之间的相关性

本研究通过基于深度学习的计算机断层扫描 (CT) 图像定量测量心外膜脂肪组织体积 (EATV)，并研究其与冠心病 (CHD) 的相关性。以在医院接受冠状动脉CT检查的150例患者为研究对象。此外，观察组（A组）患者出现血管狭窄，而对照组（B组）患者未出现血管狭窄。应用深度卷积神经网络模型构建深度学习算法，并采用基于深度学习的CT图像对EATV进行定量测量。结果表明，深度学习算法的灵敏度、特异性、准确度和曲线下面积（AUC）分别为0.8512、0.9899、0.9623和0.9813。通过对比，）。观察组心外膜脂肪组织体积（114.23±55.46）明显高于对照组（92.65±43.28），差异有统计学意义（）。具有斑块特性的EATV与狭窄冠状血管数量的r值依次为0.232和0.268，均呈显着正相关。综上所述，深度学习算法的灵敏度等指标值较传统算法有较大提升。基于深度学习算法的CT图像取得了良好的血管分割效果。此外，EATV与CHD的发展密切相关。