Deep Learning Based Coronary Angiography in Diagnosis of Myocardial Ischemia

Abstract

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 (P>0.05), 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 (P<0.05). 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 (P<0.05). 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.