Department of Cardiology, The First Affiliated Hospital of Jiamusi University, Jiamusi, 154007 Heilongjiang, China.
Comput Math Methods Med. 2022 Feb 7;2022:6447472. doi: 10.1155/2022/6447472. eCollection 2022.
This study was aimed at comparing the characteristics of coronary angiography based on intelligent algorithm in patients with acute non-ST-segment elevation myocardial infarction (NSTEMI) of different genders.
Eighty patients were selected to segment the coronary angiogram using the convolutional neural network (CNN) algorithm, the input layer of the CNN was used to receive the image dataset, and three-dimensional data were input during semantic segmentation to achieve automatic segmentation of the target features. Segmentation results were quantitatively assessed by accuracy (Acc), sensitivity (Se), specificity (Sp), and Dice coefficient (Dice). The characteristics of coronary angiography were compared between the two groups.
The CNN algorithm had good segmentation effect, complete vessel extraction, and little noise, and Acc, Se, Sp, and Dice were 90.32%, 93.39%, 91.25%, and 89.75%, respectively. The proportion of diabetes mellitus was higher in female patients with NSTEMI (68.8%) than that in male patients (46.3%); the proportion of the left main coronary artery (LM) and left anterior descending artery (LAD) was lower in the female group (7.5%, 41.3%) than that in the male group (13.8%, 81.3%), and the difference between the two groups was statistically significant ( < 0.05).
The CNN algorithm achieves accurate extraction of vessels from coronary angiographic images, and women with diabetes and hyperlipidemia are more likely to have NSTEMI than men, especially the elderly.
本研究旨在比较不同性别急性非 ST 段抬高型心肌梗死（NSTEMI）患者基于智能算法的冠状动脉造影特征。
选择 80 例患者，采用卷积神经网络（CNN）算法对冠状动脉造影进行分割，CNN 的输入层用于接收图像数据集，在语义分割中输入三维数据，实现目标特征的自动分割。通过准确率（Acc）、敏感度（Se）、特异度（Sp）和 Dice 系数（Dice）对分割结果进行定量评估。比较两组的冠状动脉造影特征。
CNN 算法分割效果良好，血管提取完整，噪声小，Acc、Se、Sp 和 Dice 分别为 90.32%、93.39%、91.25%和 89.75%。女性 NSTEMI 患者糖尿病（68.8%）的比例高于男性（46.3%）；女性组左主干（LM）和左前降支（LAD）的比例（7.5%、41.3%）低于男性组（13.8%、81.3%），两组比较差异有统计学意义（<0.05）。
CNN 算法能准确提取冠状动脉造影图像中的血管，糖尿病和高脂血症女性更易发生 NSTEMI，尤其是老年女性。
