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

Authors: Yulong Cui; Hui Wang; Peng Peng; Feng Zhang; Qing Liu; Guangyang Zhao
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## Abstract

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.


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## Body

## 1. Introduction

Cardiovascular disease is one of the major diseases endangering human health and life nowadays, of which coronary heart disease (CHD) is the most common in cardiovascular diseases [1]. Clinical manifestations of coronary artery disease include occult ischemia, stable angina, unstable angina, myocardial infarction, heart failure, and sudden death. Clinically, CHD is generally divided into two syndromes: the first is chronic ischemic syndrome, including occult CHD, stable angina, and ischemic cardiomyopathy; the second is acute myocardial infarction (AMI), including acute ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI) [2]. The onset of AMI is associated with secondary thrombosis after rupture of unstable plaques [3]. When the thrombus is completely blocked, it is manifested as the clinical symptoms of chest pain, ECG ST-segment elevation, and increased level of myocardial injury markers, namely, STEMI; when the thrombus is incompletely blocked, it is manifested as ECG ST-segment depression, namely, non-ST-segment elevation AMI; if accompanied by increased level of myocardial injury markers, it is NSTEMI, and NSTEMI is mainly caused by platelet thrombosis resulting in acute subtotal occlusion of the coronary artery, causing subendocardial injury. ECG does not show ST-segment elevation and is often associated with multivessel severe stenotic lesions [4, 5]. Compared with STEMI, the proportion of multivessel disease is large, the age of onset is older, the symptoms are atypical, and multivessel disease is combined with more ischemic myocardium, poor prognosis, more complications, high incidence of infarction and angina pectoris, and low long-term survival rate, seriously affecting the quality of life of patients [6]. There are many causes of AMI, such as age, gender, hypertension, diabetes mellitus (DM), smoking, dyslipidemia, obesity, unreasonable diet, lack of physical activity, excessive alcohol consumption, hyperfibrinogenemia (Fbg), hyperhomocysteinemia (HCY), and hyper-C-reactive protein (CRP) [7]. Among them, age, gender, and family history were uncontrollable factors; hypertension, diabetes, hyperlipidemia, and smoking were controllable factors. Numerous studies have shown that many risk factors affect the occurrence and development of CHD, of which controllable factors such as hypertension, diabetes, hyperlipidemia, and smoking are the main risk factors for AMI. After effective intervention of controllable factors, the highlighted uncontrollable factors have been paid attention to by researchers. CAG is the gold standard for the diagnosis of CHD. CT is also used for coronary angiography, but the spatial resolution is not as good as coronary angiography.In recent years, it has been found that the prevalence of AMI in women is lower than that in men, but the prognosis is poor in men, so while the mortality rate in men is decreasing year by year, the mortality rate in women is increasing year by year, and female patients are left behind by male patients in treatment and prognosis [8]. At present, there are a variety of diagnostic methods for coronary vascular diseases, such as ECG, echocardiography, coronary angiography, and intravascular imaging techniques. Among them, coronary angiography is currently the “gold standard” for the diagnosis of coronary artery-related diseases and is also the basis for guiding interventional therapy and further examination [9]. Clinically, physicians diagnose diseases by observing the morphological structure of vessels in coronary angiography images and relying on personal experience to evaluate the vascular conditions, and the evaluation results are greatly affected by human factors. Due to the influence of equipment noise and imaging quality, the phenomenon of blurred vascular image and low contrast between target vessel and background tissue is very common, which increases the difficulty of accurate diagnosis.With the gradual rise of intelligent algorithm in the field of medical imaging, the use of intelligent algorithm to segment medical images not only obtains a more accurate medical lesion segmentation map but also solves the misjudgment caused by subjective experience and fatigue of doctors. Convolutional neural network (CNN) algorithm, as one of the intelligent algorithms, has been used by some scholars to segment the lung field image of chest X-ray, and they found that the CNN algorithm can automatically discover the potential characteristics in the image block. At present, the application of intelligent algorithm combined with coronary angiography is still relatively small, and the data of CNN intelligent algorithm for segmentation of coronary angiography in NSTEMI patients are even less. Therefore, based on the above, the CNN algorithm will be applied to segment the coronary angiograms of NSTEMI patients and also analyze the differences in the coronary angiographic characteristics of NSTEMI patients of different genders, in order to provide a reliable basis for the clinical diagnosis of NSTEMI patients and the intervention of risk factors.


## 2. Materials and Methods

### 2.1. Study Subjects

Eighty male patients with NSTEMI, aged 40~81 years, with a mean age of58.2±3.45 years, admitted to the hospital from January 2019 to October 2021 were selected, and another 80 female patients with NSTEMI, aged 45~79 years, with a mean age of 62.5±3.16 years, were selected during the same period.Inclusion criteria [10] were as follows: (1) the diagnostic results of all patients were clearly in accordance with the Guidelines for the Diagnosis and Treatment of Acute Non-ST-segment Elevation Myocardial Infarction issued by the Chinese Society of Cardiology in 2010; (2) all patients were Killip class I at admission; and (3) patients with good general condition who could undergo further coronary angiography.Exclusion criteria were as follows: (1) patients with other cardiovascular diseases, such as heart failure or heart valve disease; (2) due to mental illness, patients cannot cooperate; and (3) patients have severe liver and kidney dysfunction.This study has been approved by the Ethics Committee of Hospital. All patients and their families understood the study and signed the informed consent form.


### 2.2. Coronary Angiographic Methods

All patients underwent coronary angiography, and digital angiography machine was used. The femoral artery was punctured by the Seldinger method, and the coronary arteries of patients were observed in different directions. Coronary positions include spider position, anteroposterior position, liver position, right shoulder position, anteroposterior head position, and left shoulder position. Right coronary artery position includes left anterior position and anteroposterior position. The contrast agent was Ultravist. The puncture point was the place with the strongest right radial artery pulse. Under digital subtraction (DSA), the catheter was introduced along the balloon, and then left and right coronary angiography was performed for the patients at the opening of left and right coronary arteries, respectively.


### 2.3. Image Segmentation Based on CNN Feature Extraction

Nowadays, CNN is the focus of many experts and scholars in many fields. Similarly, in terms of image processing, the use of CNN can save the preprocessing operation, so it also gets more application opportunities. For the general CNN, its basic organizational structure has two layers, one layer of feature extraction layer, as the name implies; this layer is used to extract local features in the image, and the output of the previous layer of local reception domain is used as the input of this layer of neurons. The features extracted through the feature extraction layer can directly determine the positional relationship with other features on the image. The other layer is the feature mapping layer. In the CNN, several feature maps form the calculation layer, and the neurons on this layer use the same weight value, which is conducive to reducing parameters and improving efficiency. Compared with the traditional neural network structure, the input data of CNN is usually in the form of image. The network structure can be modified according to the input image data with certain spatial characteristics, so as to maximize the utilization of input image data, reduce the setting of other auxiliary parameters, and accelerate the processing time of neural network. The basic structure of the CNN is shown in Figure1.Figure 1
Basic structure of the CNN algorithm.The input layer of the CNN was used to receive the image data set. When performing semantic segmentation of images, the input was three-dimensional data, including two-dimensional pixels on the plane and RGB channels. The data was usually preprocessed at the input layer to speed up the network operation rate. The convolution layer was used to extract different features of the input. A simple convolution process is given in Figure2.Figure 2
Convolution process.In general, the convolution layer of CNN was connected to the pool layer and divided into the maximum pool layer or mean pool layer according to the function, which can reduce the resolution of the image and reduce the calculation pressure of the next convolution layer. CNN, as a network structure algorithm that automatically segments and identifies images, can automatically achieve the purpose of dividing the corresponding feature regions without prior extraction of target features. Its input layer is generally an image mode, and the role of the convolution layer is to extract image features. Its mathematical calculation is as follows:(1)njX=f∑i∈MnjX−1∗aijX+mjX.njX represents the output result; f represents activation function; njX−1 represents the output result of the X−1 layer (the previous level); ∗ represents convolution operation; aijX represents the weight of each convolution kernel; and mjX represents bias. The operation of neural networks is inseparable from the activation function. There are usually three types of activation functions, including sigmoid function, tanh function, and Relu function. The mathematical equations of sigmoid function, tanh function, and Relu activation function are as follows:
(2)sigmoidx=11+e−x,tanhx=ex−e−xex+e−x,Rx=maxo,x=x,x≥0,0,x<0.The effect of the pooling layer is to improve the fault tolerance of the algorithm by reducing the amount of parameter calculation in the network image and reducing the complexity. Its mathematical calculation method is as shown in the following equation:(3)njX=fαjXsubnjX−1+βjX.αjX represents a multiplicative bias; sub represents the downsampling function; and βjX represents additive bias.The pooling layer can remove the unimportant information in the feature map, greatly reduce the number of network parameters, and accelerate the operation speed of the network. Common pooling includes maximum pooling and average pooling. The maximum pooling is used to retain the maximum value within the size range of the pooling unit. As the output feature after pooling, the average pooling is used to retain the average value of each feature within the size range of the pooling unit (Figures3 and 4).Figure 3
Maximum pooling operation.Figure 4
Average pooling operation.


### 2.4. Quantitative Evaluation of Segmented Image Results by the CNN Algorithm

To evaluate whether the CNN algorithm can accurately segment NSTEMI coronary angiography images, four indexes including accuracy (Acc), sensitivity (Se), specificity (Sp), and Dice coefficient (Dice) were used for quantitative evaluation. Acc represents the ratio of correctly segmented regions to total segmented regions; Se represents the ratio of predicted positive results to true positive results; Sp represents the ratio of detected negative results to total true negative results; and Dice represents the closeness of predicted samples to actual samples.(4)Acc=TP+TNTP+FN+TN+FP,PRE=TPTP+FP,SEN=TPTP+FN,Dice=2A∩BA+B.TP represents true positive (segmented true lesion); FP represents false positive (falsely segmented false lesion); FN represents false negative (unsegmented true lesion);A represents the result of lesion segmentation by imaging physician; and B represents the result of lesion segmentation by automatic segmentation algorithm.


### 2.5. Comparison Indicators between the Two Groups

The main contents collected in this study included the following: (1) general data of patients: age, smoking, body mass index (BMI), hypertension, type 2 diabetes, hyperlipidemia, and other risk factor indicators; hypertension:SBP≥140 mmHg and/or DBP≥90 mmHg at rest on two consecutive occasions or a clear history of hypertension; type 2 diabetes: glycosylated hemoglobin≥6.5%, FPG≥7.0 mmol/L, oral glucose tolerance test 2-hour blood glucose≥11.1 mmol/L, previous symptoms of hyperglycemia or hyperglycemic crisis, and random blood glucose≥11.1 mmol/L; as long as one of the above four conditions is present, it can be diagnosed; hyperlipidemia: TC≥5.98 mmol/L, TG≥1.54 mmol/L, and LDL−C≥3.36 mmol/L; as long as one of the above three conditions is present, it can be diagnosed. (2) The coronary angiographic characteristics and the incidence of adverse events during hospitalization were compared between the two groups. Indicators of coronary artery disease are as follows: coronary artery stenosis more than 50% was considered positive, and left main stenosis more than 40% was considered positive. The left main (LM) artery, left anterior descending (LAD) artery, left circumflex (LCX) artery, and right coronary artery (RCA) were defined as the main coronary arteries. According to the number of main coronary arteries involved in the lesion, the patients were divided into a single-vessel disease group, double-vessel disease group, and multivessel disease group (3-vessel or more), and LM was counted as double-vessel disease.


### 2.6. Statistical Analysis

The data were analyzed by SPSS 20.0 statistical software. The measurement data conforming to normal distribution were analyzed by one-way variance method. The measurement data of the two groups were expressed byx¯±s, and the count data were expressed by n (%). The data were input into the Excel table for statistical analysis. The enumeration data were analyzed by χ2 test. The correlation analysis was performed by the Spearman correlation coefficient method. When P<0.05, the difference was statistically significant.


## 2.1. Study Subjects

Eighty male patients with NSTEMI, aged 40~81 years, with a mean age of58.2±3.45 years, admitted to the hospital from January 2019 to October 2021 were selected, and another 80 female patients with NSTEMI, aged 45~79 years, with a mean age of 62.5±3.16 years, were selected during the same period.Inclusion criteria [10] were as follows: (1) the diagnostic results of all patients were clearly in accordance with the Guidelines for the Diagnosis and Treatment of Acute Non-ST-segment Elevation Myocardial Infarction issued by the Chinese Society of Cardiology in 2010; (2) all patients were Killip class I at admission; and (3) patients with good general condition who could undergo further coronary angiography.Exclusion criteria were as follows: (1) patients with other cardiovascular diseases, such as heart failure or heart valve disease; (2) due to mental illness, patients cannot cooperate; and (3) patients have severe liver and kidney dysfunction.This study has been approved by the Ethics Committee of Hospital. All patients and their families understood the study and signed the informed consent form.


## 2.2. Coronary Angiographic Methods

All patients underwent coronary angiography, and digital angiography machine was used. The femoral artery was punctured by the Seldinger method, and the coronary arteries of patients were observed in different directions. Coronary positions include spider position, anteroposterior position, liver position, right shoulder position, anteroposterior head position, and left shoulder position. Right coronary artery position includes left anterior position and anteroposterior position. The contrast agent was Ultravist. The puncture point was the place with the strongest right radial artery pulse. Under digital subtraction (DSA), the catheter was introduced along the balloon, and then left and right coronary angiography was performed for the patients at the opening of left and right coronary arteries, respectively.


## 2.3. Image Segmentation Based on CNN Feature Extraction

Nowadays, CNN is the focus of many experts and scholars in many fields. Similarly, in terms of image processing, the use of CNN can save the preprocessing operation, so it also gets more application opportunities. For the general CNN, its basic organizational structure has two layers, one layer of feature extraction layer, as the name implies; this layer is used to extract local features in the image, and the output of the previous layer of local reception domain is used as the input of this layer of neurons. The features extracted through the feature extraction layer can directly determine the positional relationship with other features on the image. The other layer is the feature mapping layer. In the CNN, several feature maps form the calculation layer, and the neurons on this layer use the same weight value, which is conducive to reducing parameters and improving efficiency. Compared with the traditional neural network structure, the input data of CNN is usually in the form of image. The network structure can be modified according to the input image data with certain spatial characteristics, so as to maximize the utilization of input image data, reduce the setting of other auxiliary parameters, and accelerate the processing time of neural network. The basic structure of the CNN is shown in Figure1.Figure 1
Basic structure of the CNN algorithm.The input layer of the CNN was used to receive the image data set. When performing semantic segmentation of images, the input was three-dimensional data, including two-dimensional pixels on the plane and RGB channels. The data was usually preprocessed at the input layer to speed up the network operation rate. The convolution layer was used to extract different features of the input. A simple convolution process is given in Figure2.Figure 2
Convolution process.In general, the convolution layer of CNN was connected to the pool layer and divided into the maximum pool layer or mean pool layer according to the function, which can reduce the resolution of the image and reduce the calculation pressure of the next convolution layer. CNN, as a network structure algorithm that automatically segments and identifies images, can automatically achieve the purpose of dividing the corresponding feature regions without prior extraction of target features. Its input layer is generally an image mode, and the role of the convolution layer is to extract image features. Its mathematical calculation is as follows:(1)njX=f∑i∈MnjX−1∗aijX+mjX.njX represents the output result; f represents activation function; njX−1 represents the output result of the X−1 layer (the previous level); ∗ represents convolution operation; aijX represents the weight of each convolution kernel; and mjX represents bias. The operation of neural networks is inseparable from the activation function. There are usually three types of activation functions, including sigmoid function, tanh function, and Relu function. The mathematical equations of sigmoid function, tanh function, and Relu activation function are as follows:
(2)sigmoidx=11+e−x,tanhx=ex−e−xex+e−x,Rx=maxo,x=x,x≥0,0,x<0.The effect of the pooling layer is to improve the fault tolerance of the algorithm by reducing the amount of parameter calculation in the network image and reducing the complexity. Its mathematical calculation method is as shown in the following equation:(3)njX=fαjXsubnjX−1+βjX.αjX represents a multiplicative bias; sub represents the downsampling function; and βjX represents additive bias.The pooling layer can remove the unimportant information in the feature map, greatly reduce the number of network parameters, and accelerate the operation speed of the network. Common pooling includes maximum pooling and average pooling. The maximum pooling is used to retain the maximum value within the size range of the pooling unit. As the output feature after pooling, the average pooling is used to retain the average value of each feature within the size range of the pooling unit (Figures3 and 4).Figure 3
Maximum pooling operation.Figure 4
Average pooling operation.


## 2.4. Quantitative Evaluation of Segmented Image Results by the CNN Algorithm

To evaluate whether the CNN algorithm can accurately segment NSTEMI coronary angiography images, four indexes including accuracy (Acc), sensitivity (Se), specificity (Sp), and Dice coefficient (Dice) were used for quantitative evaluation. Acc represents the ratio of correctly segmented regions to total segmented regions; Se represents the ratio of predicted positive results to true positive results; Sp represents the ratio of detected negative results to total true negative results; and Dice represents the closeness of predicted samples to actual samples.(4)Acc=TP+TNTP+FN+TN+FP,PRE=TPTP+FP,SEN=TPTP+FN,Dice=2A∩BA+B.TP represents true positive (segmented true lesion); FP represents false positive (falsely segmented false lesion); FN represents false negative (unsegmented true lesion);A represents the result of lesion segmentation by imaging physician; and B represents the result of lesion segmentation by automatic segmentation algorithm.


## 2.5. Comparison Indicators between the Two Groups

The main contents collected in this study included the following: (1) general data of patients: age, smoking, body mass index (BMI), hypertension, type 2 diabetes, hyperlipidemia, and other risk factor indicators; hypertension:SBP≥140 mmHg and/or DBP≥90 mmHg at rest on two consecutive occasions or a clear history of hypertension; type 2 diabetes: glycosylated hemoglobin≥6.5%, FPG≥7.0 mmol/L, oral glucose tolerance test 2-hour blood glucose≥11.1 mmol/L, previous symptoms of hyperglycemia or hyperglycemic crisis, and random blood glucose≥11.1 mmol/L; as long as one of the above four conditions is present, it can be diagnosed; hyperlipidemia: TC≥5.98 mmol/L, TG≥1.54 mmol/L, and LDL−C≥3.36 mmol/L; as long as one of the above three conditions is present, it can be diagnosed. (2) The coronary angiographic characteristics and the incidence of adverse events during hospitalization were compared between the two groups. Indicators of coronary artery disease are as follows: coronary artery stenosis more than 50% was considered positive, and left main stenosis more than 40% was considered positive. The left main (LM) artery, left anterior descending (LAD) artery, left circumflex (LCX) artery, and right coronary artery (RCA) were defined as the main coronary arteries. According to the number of main coronary arteries involved in the lesion, the patients were divided into a single-vessel disease group, double-vessel disease group, and multivessel disease group (3-vessel or more), and LM was counted as double-vessel disease.


## 2.6. Statistical Analysis

The data were analyzed by SPSS 20.0 statistical software. The measurement data conforming to normal distribution were analyzed by one-way variance method. The measurement data of the two groups were expressed byx¯±s, and the count data were expressed by n (%). The data were input into the Excel table for statistical analysis. The enumeration data were analyzed by χ2 test. The correlation analysis was performed by the Spearman correlation coefficient method. When P<0.05, the difference was statistically significant.


## 3. Results

### 3.1. Segmentation Results of Coronary Angiogram by the CNN Algorithm

The coronary angiograms of NSTEMI patients were segmented by the CNN algorithm, and the segmentation results of the CNN algorithm were compared with matched filtering (MF) algorithm. In order to visually verify the accuracy of the CNN algorithm in segmenting the coronary angiograms of patients of different genders, the coronary angiograms of patients in the two groups were selected for testing, and the segmentation results of the coronary angiograms done by the imaging physician were used as the reference standard (Figure5). Figure 5(a) show an angiogram of a 52-year-old male patient with NSTEMI. Coronary CTA showed a significant proximal stenosis of the right coronary artery, and the patient had a hobby of smoking. Figure 5(b) shows an angiogram of a 55-year-old female patient with NSTEMI. Coronary CTA suggested calcified plaques in the middle of the right coronary artery and severe stenosis of the lumen.Figure 5
Segmentation results of coronary angiography images by different methods. (a, b) Are original coronary angiography images of male NSTEMI patients and female NSTEMI patients, respectively; (c, d) are the results of original image segmentation by imaging doctors; (e, f) are the results of original image segmentation by the CNN algorithm; (g, h) are the results of original image segmentation by the MF algorithm, respectively.
(a)(b)(c)(d)(e)(f)(g)(h)Although the MF algorithm also segments smaller vessels, there are still many noises in the segmented images, which have affected the identification of normal vessels. Compared with the MF algorithm, the CNN algorithm used in this study has better resolution for segmenting images, clearer vessel boundaries, more complete division of coronary vessels, and little noise, indicating that the CNN algorithm used in this study has better segmentation results for coronary angiograms.


### 3.2. Evaluation Results of Coronary Angiography Segmentation

In order to quantitatively evaluate the segmentation results, Acc, Se, Sp, and Dice were used. The CNN algorithm was also compared with the MF algorithm (Figure6). The Acc, Se, Sp, and Dice of the MF algorithm were 85.71%, 88.65%, 87.14%, and 83.43%, respectively, which were significantly lower than those of the CNN algorithm (90.32%, 93.39%, 91.25%, and 89.75%). It showed that the CNN algorithm used in this study has good segmentation effect on coronary angiography.Figure 6
Evaluation index of segmentation results of two algorithms.


### 3.3. Comparison of General Data between the Two Groups

The results of the comparison of the basic data of 110 male NSTEMI patients and 110 female NSTEMI patients are shown in Table1 and Figure 7. Compared with the mean age of male patients (58.2±3.45), the mean age of female NSTEMI patients (62.5±3.16) was significantly higher; compared with the proportion of male diabetic patients, the proportion of female NSTEMI patients with diabetes was significantly higher; compared with the proportion of male smoking patients, the proportion of female NSTEMI smoking patients was significantly lower; compared with the proportion of male hypertensive patients, the proportion of female NSTEMI hypertensive patients was significantly lower; the above indicators were statistically significant (P<0.05). There was no significant difference in BMI and hyperlipidemia ratio between the two groups (P>0.05).Table 1
General data of patients in the two groups.
ClassificationSmokingHypertensionDiabetesHyperlipidemiaFemale (%)8 (10.0)41 (51.3)55 (68.8)25 (31.3)Male (%)66 (82.5)61 (76.3)37 (46.3)22 (27.5)Figure 7
Proportion results of general data of patients in the two groups.∗ indicates statistical significance compared with the male group, P<0.05.


### 3.4. Comparison of Coronary Angiography between the Two Groups

The results of coronary angiography in the two groups of patients are shown in Table2 and Figure 8. The proportion of single-vessel LM and the proportion of LAD in the female group were lower than those in the male group, and the difference between the two groups had statistical significance (P<0.05). There was no statistical significance between female double-vessel disease and multivessel disease and male double-vessel disease and multivessel disease (P>0.05).Table 2
Comparison of coronary artery disease between the two groups.
ClassificationLMLADLCXRCADouble vessel diseaseMultivessel diseaseFemale (%)6 (7.5)34 (42.5)30 (37.5)26 (32.5)32 (40.0)36 (45.0)Male (%)9 (11.3)65 (81.3)45 (56.3)55 (68.8)51 (63.8)42 (52.5)Figure 8
Proportion of coronary artery lesions in the two groups.∗ indicates statistically significant difference from the male group, P<0.05.


## 3.1. Segmentation Results of Coronary Angiogram by the CNN Algorithm

The coronary angiograms of NSTEMI patients were segmented by the CNN algorithm, and the segmentation results of the CNN algorithm were compared with matched filtering (MF) algorithm. In order to visually verify the accuracy of the CNN algorithm in segmenting the coronary angiograms of patients of different genders, the coronary angiograms of patients in the two groups were selected for testing, and the segmentation results of the coronary angiograms done by the imaging physician were used as the reference standard (Figure5). Figure 5(a) show an angiogram of a 52-year-old male patient with NSTEMI. Coronary CTA showed a significant proximal stenosis of the right coronary artery, and the patient had a hobby of smoking. Figure 5(b) shows an angiogram of a 55-year-old female patient with NSTEMI. Coronary CTA suggested calcified plaques in the middle of the right coronary artery and severe stenosis of the lumen.Figure 5
Segmentation results of coronary angiography images by different methods. (a, b) Are original coronary angiography images of male NSTEMI patients and female NSTEMI patients, respectively; (c, d) are the results of original image segmentation by imaging doctors; (e, f) are the results of original image segmentation by the CNN algorithm; (g, h) are the results of original image segmentation by the MF algorithm, respectively.
(a)(b)(c)(d)(e)(f)(g)(h)Although the MF algorithm also segments smaller vessels, there are still many noises in the segmented images, which have affected the identification of normal vessels. Compared with the MF algorithm, the CNN algorithm used in this study has better resolution for segmenting images, clearer vessel boundaries, more complete division of coronary vessels, and little noise, indicating that the CNN algorithm used in this study has better segmentation results for coronary angiograms.


## 3.2. Evaluation Results of Coronary Angiography Segmentation

In order to quantitatively evaluate the segmentation results, Acc, Se, Sp, and Dice were used. The CNN algorithm was also compared with the MF algorithm (Figure6). The Acc, Se, Sp, and Dice of the MF algorithm were 85.71%, 88.65%, 87.14%, and 83.43%, respectively, which were significantly lower than those of the CNN algorithm (90.32%, 93.39%, 91.25%, and 89.75%). It showed that the CNN algorithm used in this study has good segmentation effect on coronary angiography.Figure 6
Evaluation index of segmentation results of two algorithms.


## 3.3. Comparison of General Data between the Two Groups

The results of the comparison of the basic data of 110 male NSTEMI patients and 110 female NSTEMI patients are shown in Table1 and Figure 7. Compared with the mean age of male patients (58.2±3.45), the mean age of female NSTEMI patients (62.5±3.16) was significantly higher; compared with the proportion of male diabetic patients, the proportion of female NSTEMI patients with diabetes was significantly higher; compared with the proportion of male smoking patients, the proportion of female NSTEMI smoking patients was significantly lower; compared with the proportion of male hypertensive patients, the proportion of female NSTEMI hypertensive patients was significantly lower; the above indicators were statistically significant (P<0.05). There was no significant difference in BMI and hyperlipidemia ratio between the two groups (P>0.05).Table 1
General data of patients in the two groups.
ClassificationSmokingHypertensionDiabetesHyperlipidemiaFemale (%)8 (10.0)41 (51.3)55 (68.8)25 (31.3)Male (%)66 (82.5)61 (76.3)37 (46.3)22 (27.5)Figure 7
Proportion results of general data of patients in the two groups.∗ indicates statistical significance compared with the male group, P<0.05.


## 3.4. Comparison of Coronary Angiography between the Two Groups

The results of coronary angiography in the two groups of patients are shown in Table2 and Figure 8. The proportion of single-vessel LM and the proportion of LAD in the female group were lower than those in the male group, and the difference between the two groups had statistical significance (P<0.05). There was no statistical significance between female double-vessel disease and multivessel disease and male double-vessel disease and multivessel disease (P>0.05).Table 2
Comparison of coronary artery disease between the two groups.
ClassificationLMLADLCXRCADouble vessel diseaseMultivessel diseaseFemale (%)6 (7.5)34 (42.5)30 (37.5)26 (32.5)32 (40.0)36 (45.0)Male (%)9 (11.3)65 (81.3)45 (56.3)55 (68.8)51 (63.8)42 (52.5)Figure 8
Proportion of coronary artery lesions in the two groups.∗ indicates statistically significant difference from the male group, P<0.05.


## 4. Discussion

The annual incidence of acute coronary syndrome is about 0.3% of the living population, of which 30% are STEMI and 70% are NSTEMI or unstable angina [11]. Compared with patients with STEMI, patients with NSTEMI are more difficult to diagnose, are more complex to treat, and have a worse prognosis. Acute NSTEMI is a problem of ACS, and its pathogenesis is very complex. The most important reason is that coronary atherosclerosis leads to plaque formation, followed by narrowing of the vessel, sudden plaque rupture, or thrombus shedding, which in turn causes acute embolism of the coronary artery, resulting in the impact of coronary blood flow [12]. If blood flow is completely blocked, it leads to STEMI; if it is not completely blocked, it leads to NSTEMI. In this case, there are two possibilities: the thrombus leads to incomplete occlusion of the lumen, with a small amount of blood flow; the blood vessel has been completely blocked, but collateral circulation is formed in the distal coronary artery, which plays a certain compensatory role [13].The age of onset of NSTEMI shows a tendency to be younger, and the distribution changes of risk factors shows a tendency to cluster. In recent years, with the improvement of medical level and the rapid development of coronary intervention technology, the mortality rate of male CHD has been decreasing year by year, but the mortality rate of female CHD has been increasing year by year [14]. It is still controversial whether gender is an independent predictor of AMI prognosis, especially for patients with acute NSTEMI. Due to the physiological particularity of women, the age of onset of acute myocardial infarction in women is later than that in men. Age is an independent risk factor for myocardial infarction, and the incidence of acute myocardial infarction gradually increases with age. For women, acute myocardial infarction in young women is extremely rare due to high estrogen levels before menopause, which play a strong antiatherosclerotic role. Estrogen secretion is significantly reduced in postmenopausal women; deficiency of estrogen leads to cholesterol degradation and excretion disorder and increased plasma cholesterol concentration, thus accelerating atherosclerotic lesions. The risk of myocardial infarction in postmenopausal women increases significantly with decreasing estrogen levels [15]. Data have shown that the age of onset in female AMI patients is 10 years later than that in men [16]. The results of this study also found that the mean age of female NSTEMI patients (62.5±3.16) was significantly higher compared with the mean age of male NSTEMI patients (58.2±3.45).The results of this study showed that compared with male patients, female NSTEMI patients had a significantly higher proportion of diabetes and hyperlipidemia, while the proportion of smoking was significantly lower. This result is also consistent with the results of a report in the literature [17]. Single-vessel disease is the most common coronary vascular disease in patients with premature CHD, and the pathogenesis is mostly the rupture of the original stable plaque. This condition is mostly related to the enhancement of coronary shear stress, which is mostly caused by high physical load or emotional stress in young people. Domestic studies have shown that with the increase of age, the number of coronary artery lesions in patients with acute coronary syndrome will also increase [18]. However, there is still no uniform conclusion on whether there is a difference in the number of coronary lesions between genders. Foreign studies have shown that women have a lower number of multivessel disease, left main disease, and vascular disease compared with men [19]. However, recent studies have also shown no significant difference in the number of diseased vessels between male and female patients [20]. The results of this study suggested that the proportion of single-vessel LM and the proportion of LAD were lower in the female group than in the male group.At present, for the diagnostic methods of myocardial infarction, coronary angiography is often used as a diagnostic gold standard. Although ultrasound imaging technology and MRI technology have been gradually applied in clinical practice in recent years, due to the high cost and complex operation, there are misdiagnosis, missed diagnosis, and other phenomena, which are always not as widely used as coronary angiography technology. In clinical practice, the professional theory and experience of imaging physicians are often used to segment the coronary angiography images. However, due to the complex vessel morphology, uneven distribution of contrast agent, and noise in the imaging process [21], the angiographic images may be blurred, which makes the physicians spend a lot of time and effort to identify and segment the coronary angiography images.With the rise of intelligent algorithms in recent years, more and more algorithms have been applied in the medical field to process medical image maps. Atlason et al. [22] used the CNN algorithm to extract blood vessels from the original brain CT angiography images, and the experimental results demonstrated that the CNN algorithm had higher effectiveness than other algorithms in cerebrovascular segmentation. In this study, the CNN algorithm was used to segment the coronary angiogram, and the results revealed that the Acc, Se, Sp, and Dice of the MF algorithm (85.71%, 88.65%, 87.14%, and 83.43%) were significantly lower than those of the CNN algorithm (90.32%, 93.39%, 91.25%, and 89.75%) when the CNN algorithm was compared with the MF algorithm. The results show that the CNN algorithm used in this study has a better segmentation effect on coronary angiograms.


## 5. Conclusion

The CNN algorithm was used to segment coronary angiography images, and the accurate extraction of blood vessels in coronary angiography images was realized. There were gender differences in coronary artery diseases in patients with acute coronary syndrome.NSTEMI is more likely to occur in female patients, especially in elderly patients with diabetes and hyperlipidemia. It provides a certain basis for solving the extraction of blood vessels of coronary arteries in clinical practice, but there are still some shortcomings. The samples are collected in medical institutions, and the sample size is small. In order to obtain more detailed results, multicenter studies with large sample size are needed. In the future, the factors not conforming in the coronary artery CTA examination can be improved. The ECG with irregular heart rate and fast heart rate can be reconstructed in time to improve the image quality.



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*Source: 6447472-2022-02-07.xml*