Digital Subtraction Angiography Image Features under the Deep Learning Algorithm in Cardiovascular Interventional...

The objective of this study was to explore the application value of digital subtraction angiography (DSA) images optimized by deep learning algorithms in vascular restenosis patients undergoing cardiovascular intervention and their nursing efficacy. In this study, a network model for removing artifacts was constructed based on a deep algorithm. 60 patients with coronary artery restenosis were selected as the research objects, and they were randomly divided into the CTA group guided by CT angiography (CTA) and digital subtraction angiography (DSA) group, with 30 cases in each group. The antiartifact network model constructed based on the depth algorithm was applied to the images of CTA and DSA for experiments. After cardiovascular intervention and clinical pathway nursing intervention, it was found that the diameter stenosis rate in the DSA group decreased from 65.82 ± 12.9% to 4.7 ± 1.3%, and the area stenosis rate decreased from 88.4 ± 14.3% to 5.4 ± 1.7%. During the follow-up period of 3-24 months, 3 out of 46 lesions in the DSA group showed restenosis, so the restenosis rate was 6.5%, which was significantly lower than the 18.4% in the CTA group (P < 0.05). In the DSA group, there was 1 case of bleeding, 0 case of hematoma, 2 cases of urinary retention, and 0 case of hypotension, so the total incidence of adverse reactions was 10%, which was significantly lower than the 30% of the CTA group (P < 0.05). The high-sensitivity C-reactive protein (hs-CRP) levels of the two groups of patients were 3.58 ± 2.02 mg/L and 4.36 ± 3.11 mg/L before surgery and 3.49 ± 2.18 mg/L and 4.57 ± 3.4 mg/L after the surgery. The postoperative hs-CRP level in the CTA group was slightly lower than that before the surgery and the postoperative hs-CRP level in the DSA group was slightly higher than that before the surgery, but they were not statistically significant (P > 0.05). The hs-CRP level of the DSA group before and after the surgery was slightly higher than that of the CTA group, but there was no significant difference (P > 0.05). In summary, the network model based on the deep learning algorithm can remove the artifacts in DSA images and present high-quality clear images, and convolutional neural network (CNN) algorithms had a strong ability to automatically learn features in the field of medical image processing and were worthy of being widely used and popularized. In addition, the DSA-guided intervention can reduce the rate of vascular stenosis in patients, showing low probability of postoperative restenosis and adverse reactions and a good clinical effect.