This study aimed to analyze the risk factors of adverse cardiovascular events (ACVEs) in elderly patients with coronary heart disease (CHD) 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%) (). 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%) (). 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 (). 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.

中文翻译：

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使用血管内超声图像对经皮冠状动脉介入治疗后老年冠心病患者的不良心血管事件进行基于深度学习的评估

本研究旨在利用基于卷积神经网络深度学习的血管内超声（IVUS）图像分析老年冠心病（CHD）患者经皮冠状动脉介入治疗（PCI）后发生不良心血管事件（ACVEs）的危险因素。 CNN）。本研究以90名冠心病患者为研究对象。所有患者随机分为对照组（C组）和实验组（E组），均行PCI治疗。C组患者通过血管造影确诊，E组患者在深度学习下进行IVUS检查。比较两组PCI前后血脂及炎症因子水平，记录敏感性、特异性及阳性预测值（PPV）。）。与C组相比，E组患者的最窄病灶直径（2.54±0.18 mm vs. 2.21±0.19 mm）和偏心斑块检出率（80.1% vs. 45.3%）更高（）。两组PCI术后高密度脂蛋白胆固醇（HDL-C）均显着高于术前，而低密度脂蛋白胆固醇（LDL-C）、肿瘤坏死因子（TNF）、C反应蛋白（CRP） )明显低于术前，差异有统计学意义(）。总之，基于深度学习算法的超声检测方法对冠心病检测具有较高的灵敏度、特异性和准确性；PCI可以改善患者的血脂水平，缓解患者的炎症，减少患者ACVEs的发生。