Department of Cardiovascular Disease, Cangzhou Central Hospital of Tianjin Medical University, Tianjin 300000, China.
Department of Cardiovascular Disease, First Central Clinical College of Tianjin Medical University, Tianjin 300000, China.
Comput Math Methods Med. 2022 Feb 2;2022:2420586. doi: 10.1155/2022/2420586. eCollection 2022.
This research was aimed at exploring the application value of coronary angiography (CAG) based on a convolutional neural network algorithm in analyzing the distribution characteristics of ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI) culprit lesions in acute myocardial infarction (AMI) patients. . Patients with AMI treated in hospital from June 2019 to December 2020 were selected as subjects. According to the results of an echocardiogram, the patients were divided into the STEMI group (44 cases) and the NSTEMI group (36 cases). All patients received CAG. All images were denoised and edge detected by a convolutional neural network algorithm. Then, the number of diseased vessels, the location of diseased vessels, and the degree of stenosis of diseased vessels in the two groups were compared and analyzed. . The number of patients with complete occlusion (3 cases vs. 12 cases) and collateral circulation (5 cases vs. 20 cases) in the NSTEMI group was significantly higher than that in the STEMI group, and the difference was statistically significant, < 0.05. There was a statistically significant difference in the number of lesions between the distal LAD (1 case vs. 10 cases) and the distal LCX (4 cases vs. 11 cases), < 0.05. There was a statistically significant difference in the number of patients with one lesion branch (1 vs. 18) and three lesion branches (25 vs. 12) between the two groups, < 0.05. The image quality after the convolution neural network algorithm is significantly improved, and the lesion is more prominent. . The convolutional neural network algorithm has good performance in DSA image processing of AMI patients. STEMI and NSTEMI as the starting point of AMI disease analysis to determine the treatment plan have high clinical application value. This work provided reference and basis for the application of the convolutional neural network algorithm and CAG in the analysis of the distribution characteristics of STEMI and NSTEMI culprit lesions in AMI patients.
本研究旨在探讨基于卷积神经网络算法的冠状动脉造影（CAG）在分析急性心肌梗死（AMI）患者罪犯病变中 ST 段抬高型心肌梗死（STEMI）和非 ST 段抬高型心肌梗死（NSTEMI）分布特征中的应用价值。选取 2019 年 6 月至 2020 年 12 月在我院治疗的 AMI 患者为研究对象。根据超声心动图结果将患者分为 STEMI 组（44 例）和 NSTEMI 组（36 例）。所有患者均行 CAG 检查。所有图像均采用卷积神经网络算法进行降噪和边缘检测。然后比较分析两组患者的病变血管数量、病变血管位置及病变血管狭窄程度。结果显示，NSTEMI 组完全闭塞（3 例比 12 例）和侧支循环（5 例比 20 例）患者数量明显多于 STEMI 组，差异有统计学意义（均＜0.05）。LAD 远端病变（1 例比 10 例）和 LCX 远端病变（4 例比 11 例）数量差异有统计学意义（均＜0.05）。两组单支病变分支患者（1 例比 18 例）和三支病变分支患者（25 例比 12 例）数量差异有统计学意义（均＜0.05）。卷积神经网络算法处理后图像质量明显改善，病变更突出。卷积神经网络算法在 AMI 患者 DSA 图像处理中性能良好。以 STEMI 和 NSTEMI 作为 AMI 疾病分析的起点来确定治疗方案具有较高的临床应用价值。本研究为卷积神经网络算法和 CAG 在 AMI 患者罪犯病变中 STEMI 和 NSTEMI 分布特征分析中的应用提供了参考和依据。
