Deep Learning-Based Assessment of Adverse Cardiovascular Events in Elderly Patients with Coronary Heart Disease after Percutaneous Coronary Intervention Using Intravascular Ultrasound Images

This study aimed to analyze the risk factors of adverse cardiovascular events (ACVEs) in elderly patients with coronary heart disease (CI ID) after percutaneous coronary intervention (PCI) using the intravascular ultrasound (IVUS) images based on the deep learning of convolutional neural networks (CNNs). This study included 90 patients with coronary heart disease as the research object. All the patients were randomly divided into a control group (group C) and an experimental group (group E), and all were treated with PCI. The patients in group C were diagnosed by angiography, and patients in group E underwent IVUS examination under deep learning. The levels of blood lipids and inflammatory factors between the two groups before and after PCI were compared, and the sensitivity, specificity, and positive predictive value (PPV) were recorded. Compared with angiography diagnosis, ultrasound diagnosis based on deep learning algorithm had higher sensitivity (92.3% vs. 81.4%), specificity (90.1% vs. 88.6%), and PPV (94.8% vs. 75.3%) (P <0.05). Compared with group C, patients in group E had a higher narrowest lesion diameter (2.54 +/- 0.18 mm vs. 2.21 +/- 0.19 mm) and detection rate of eccentric plaques (80.1% vs. 45.3%) (P < 0.05). High-density lipoprotein cholesterol (HDL-C) after PCI in the two groups was significantly higher than that before surgery, while low-density lipoprotein cholesterol (LDL-C), tumor necrosis factor (TNF), and C-reactive protein (CRP) were significantly lower than those before surgery, and the difference was statistically significant (P < 0.05). In short, the ultrasonic detection method based on deep learning algorithm has high sensitivity, specificity, and accuracy for CHD detection; PCI can improve the patient's blood lipid level, relieve the patient's inflammation, and reduce the occurrence of ACVEs in the patient.