Intelligent Algorithm-Based Coronary Angiography Characteristics of Acute Non-ST-Segment Elevation Myocardial Infarction Patients with Different Genders

OBJECTIVE: 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. METHODS: 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. RESULTS: 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 (P < 0.05). CONCLUSION: 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.