Objective. This study was to explore the diagnostic effect of the coronary angiography (CAG) based on the fully convolutional neural network (FCNN) algorithm for patients with coronary heart disease (CHD) and suspected (not diagnosed) myocardial ischemia. Methods. In this study, 150 patients with undiagnosed CHD with myocardial ischemia in hospital were selected as the research objects. They were divided into an observation group and a control group by random number method. The patients in observation group were examined with CAG with the assistance of convolutional neural network (CNN) algorithm, while patients in the control group received conventional CAG. Results. The Dice coefficient of the segmentation effect evaluation index was 0.89, which showed that the image processing effect of the algorithm was good. There was no statistical difference in positive rates of single/double-vessel lesions between the two groups (), and the positive rates of multivessel lesions and total lesions in the observation group were higher than those in the control group, showing statistically obvious difference (). The examination sensitivity, specificity, accuracy, and Kappa value of the observation group were −90.9%, −60%, −82.7%, and −0.72, which were all higher in contrast to those of the control group. The proportion of positive myocardial ischemia and coronary artery stenosis (CAS) (82%) was higher than other cases (18%), and the comparison was statistically significant (). Conclusion. CAG based on the deep learning algorithm showed a good detection effect and can better display the coronary lesions and reflect the good development prospects of deep learning technology in medical imaging.

中文翻译：

---

基于深度学习的冠状动脉造影诊断心肌缺血

目标。本研究旨在探讨基于全卷积神经网络（FCNN）算法的冠状动脉造影（CAG）对冠心病（CHD）和疑似（未确诊）心肌缺血患者的诊断效果。方法。本研究选取住院未确诊冠心病合并心肌缺血患者150例作为研究对象。采用随机数法分为观察组和对照组。观察组患者在卷积神经网络（CNN）算法的辅助下进行CAG检查，而对照组患者接受常规CAG。结果. 分割效果评价指标的Dice系数为0.89，说明该算法的图像处理效果良好。两组单/双血管病变阳性率无统计学差异（)，且观察组多支病变阳性率和总病变阳性率均高于对照组，差异有统计学意义(）。观察组检查灵敏度、特异度、准确度、Kappa值分别为-90.9%、-60%、-82.7%、-0.72，均高于对照组。心肌缺血和冠状动脉狭窄（CAS）阳性比例（82%）高于其他病例（18%），比较有统计学意义（）。 结论。基于深度学习算法的CAG表现出良好的检测效果，能够更好地显示冠状动脉病变，体现了深度学习技术在医学影像方面的良好发展前景。