# Hemodynamic Analysis of Pipeline Embolization Device Stent for Treatment of Giant Intracranial Aneurysm under Unsupervised Learning Algorithm

Authors: Haibin Gao; Wei You; Jian Lv; Youxiang Li
Journal: Journal of Healthcare Engineering
 (2022)

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## Abstract

To treat large intracranial aneurysms, pipeline embolization device (PED) stent with unsupervised learning algorithms was utilized. Unsupervised learning model algorithm was used to screen aneurysm health big data, find aneurysm blood flow and PED stent positioning characteristic parameters, and guide PED stent treatment of intracranial aneurysms. The research objects were 100 patients with intracranial large aneurysm admitted to X Hospital of X Province from June 2020 to June 2021, who were enrolled into two groups. One group used the prototype transfer generative adversarial network (PTGAN) model to measure mean blood flow and mean vascular pressure and guide the placement of PED stents (PTGAN group). The other group did not use the model to place PED (control group). The PTGAN model can learn feature information from horizontal and vertical directions, with smooth edges and prominent features, which can effectively extract the main morphological and texture features of aneurysms. Compared with the convolutional neural network (CNN) model, the accuracy of the PTGAN model increased by 8.449% (87.452%–79.003%), and the precision increased by 8.347% (91.23%–82.883%). The recall rate increased by 7.011% (87.231%–80.22%), and theF1 score increased by 8.09% (89.73%–81.64%). After the adoption of the PTGAN model, the average blood flow inside the aneurysm body was 0.22 (m/s). After the adoption of the CNN model, the average blood flow inside the aneurysm body was 0.21 (m/s), and the difference was 0.01 (m/s), which was considerable (p<0.05). Through this research, it was found that the PTGAN model was better than the CNN model in terms of accuracy, precision, recall, and F1 score values. The PTGAN model was better than the CNN model in detecting the average blood flow rate and average blood pressure after treatment, and the blood flowed smoothly. Postoperative complications and postoperative relief were also better than those of the control group. In summary, based on the unsupervised learning algorithm, the PED stent had a good adoption effect in the treatment of intracranial aneurysms and was suitable for subsequent treatment.


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## Body

## 1. Introduction

Cerebrovascular diseases are a kind of diseases threatening human life. With the continuous improvement of people’s living standard and diet style, the incidence of cerebrovascular diseases is increasing year by year. As cerebrovascular diseases are characterized by high mortality and disability rates, they have become the focus of neurosurgery circles around the world [1]. Intracranial aneurysm, as one of the most common cerebrovascular diseases, is the main cause of spontaneous subarachnoid hemorrhage (SAH). Chancellor et al. summarized nearly 6,000 cases of spontaneous subarachnoid hemorrhage in 24 medical centers in the UK and the US [2]. The analysis results showed that the cause of subarachnoid hemorrhage was aneurysm rupture in about 8 cases per 100,000 people every year. With the enlargement of the arterial tumor, the probability of aneurysm rupture will increase exponentially, so the active treatment of intracranial aneurysm is particularly important [3]. However, some aneurysms, such as large or giant aneurysms, wide-necked aneurysms, fusiform aneurysms, and other complex aneurysms, have always been a difficult and challenging problem in clinical treatment [4]. Due to the considerable mass effect of large or giant aneurysms, the surgical space is narrow, and separation and exposure are difficult. In addition, the aneurysm neck is wide, the tumor is prone to rupture and hemorrhage, and the presence of important perforating vessels adjacent to or even on the tumor makes surgical treatment very difficult, resulting in high mortality, disability, and risk, which are the difficulties in the treatment of this aneurysm [5]. With the emergence of compliant balloon, spring coil, intracranial stent, etc., more and more neurosurgery centers have adopted interventional treatment for such lesions and achieved good clinical results [6].It is important to study hemodynamic factors for the formation, progression, and treatment of vascular diseases [7]. Blood circulation dynamics of blood vessels is a hot topic in biomedical biomechanics research [8]. Although hemodynamics has been studied, there is still a big gap between the treatment of cerebrovascular disease and decision support. The first step of hemodynamic analysis is establishing a geometric model of the simulated object, which is used to analyze appropriate blood flow and stent sites [9]. When blood circulation dynamics is used as an auxiliary decision-making method for cardiovascular system and cerebrovascular diseases, computational fluid dynamics must be closely combined with medical image processing technology to establish the actual vascular geometry model in anatomy [10]. The establishment of accurate arterial geometric model is a bottleneck problem that affects the accuracy of hemodynamic analysis [11]. When patients use vital signs, healthy big data can obtain real human hemodynamic parameters, such as velocity, differential pressure, wall intercept stress, and particle motion. It is of great significance for cardiovascular system optimization, preoperative prediction, and clinical intervention evaluation [12].


## 2. Methods

### 2.1. Research Objects

The research subjects were 100 patients with intracranial large aneurysms admitted to X Hospital in X Province from June 2020 to June 2021, which were then enrolled into two groups of 50 people. In one group, the PTGAN model was used to detect the mean blood flow and the mean vascular pressure, and the pipeline embolization device (PED) was used to direct blood flow (PTGAN group). The other group did not use the model to place PED (control group).Inclusion criteria were as follows: (i) patients with intracranial large aneurysm with clinical symptoms or risk of hemorrhage; (ii) patients with large intracranial aneurysm with subarachnoid hemorrhage; (iii) patients aged 25 years or older; and (iv) patients without other brain diseases.Exclusion criteria were as follows: (i) patients with drug allergy; (ii) patients with other arterial diseases; (iii) patients under 25 years of age; (iv) patients with poor compliance; (iv) patients with other brain diseases; and (v) patients that cannot be followed up.In this study, 100 patients with intracranial aneurysms met the above inclusion criteria and exclusion criteria. This study had been approved by the Medical Ethics Committee of the hospital, and the families of patients included in the study had all signed the informed consent form.


### 2.2. Medical Cases and Image Classification Method Based on Artificial Intelligence Unsupervised Big Data

For the classification methods of medical and health cases and images under big data, the performance of the model is not ideal due to the large difference of data distribution types. An unsupervised image classification method based on prototype transfer generative adversarial network (PTGAN) was proposed to realize the classification of unlabeled medical and health data and image data in the target area. Training tests of unsupervised network models were performed on open medical health datasets of patients with large aneurysms. The medical images of patients with large aneurysms were unclear and fuzzy due to different equipment or medical conditions, resulting in loss of effect. In this model, the classified models were placed in the new dataset, and the most suitable arterial images were selected as reference. The PTGAN structural framework is shown in Figure1.Figure 1
PTGAN structural framework.The PTGAN method consists of two main modules of the target domain bias generation network (target), namely, target biased generative adversarial network (TBGAN) and prototype transfer algorithm (PTA). The target domain deviation uses the domain separator loss and the target to generate a hostile network. The prototype migration algorithm module maps the extracted sample function to the cross-domain feature space by cross-domain loss. The label information is used in the source domain to construct the category prototype, and the feature points of the prototype are further used to automatically classify the features of the target area to predict the noncharacteristic samples in the target domain [13].


### 2.3. An Unsupervised Image Model Algorithm Based on Prototype Transfer to Generate Adversarial Network

The unsupervised image model algorithm based on the prototype migration generation confrontation network does not require a new model but migrates the original model to a new dataset to improve the recognition accuracy of large aneurysm images. First, to define the source domain, an image with a more obvious characteristic lesion is required as a reference. PTGAN contains an extractorf to extract features.(1)his=fxis;θf,hjt=fxjt;θf,where θf is the digital parameter in the extracted features. PTGAN extracts and reconstructs his and hjt to generate xˆis and xˆjt applied to the new database.(2)xˆis=ghis;θg,xˆjt=ghjt;θg,where θg is the digital parameter of the new database. At the same time, the generator is used to stylize the structure of the new database model. The new database result G = f ∗ g is similar to the model training database result, so the generator G loss function LG is the following equation.(3)ℒG=1Ns∑i=0Nslog1−DGxis.When the loss function is close to 1, it means that the model has a good effect. The antiloss equation is as follows.(4)ℒD=1Ns∑i=0NslogDGxis+1Nt∑j=0Ntlog1−DGxjt.The purpose of the antiloss is improving the distinguishing ability of the domain discriminator, that is, it is judged that the reconstructed image from the source domain sample is 0, and the reconstructed image from the target domain sample is judged to be 1. The use of loss function and counter-loss function can improve the domain migration ability of the generator through counter-training, which makes the application of the original data model fast, convenient, and accurate. There is also a target domain bias loss function at the end of the algorithm, and the equation is as follows.(5)ℒTB=1Nt∑j=1NtGxjt−xjt2.The target domain bias loss function can ensure that the new data model is not too biased and maintain a maximum bias bottom lineGxjt. When the bias is too large, the algorithm will recalculate until the data are reasonable.


### 2.4. Dataset and Evaluation Criteria

The model dataset came from the health and medical data of patients with craniocerebral aneurysm. The reference dataset contained many characteristic images of cranial aneurysms, including 1,000 image samples of benign and malignant cranial aneurysms, taken at 40x, 100x, 200x, and 400x magnifications. Of which, 70% of the samples were used as the training set, and the remaining 30% were used as the test set. To reflect the versatility of the method, when new patient samples were tested, it was ensured that the patient samples in the training set will not participate in the model test.Accuracy, precision, recall, andF1 score were used as performance evaluation criteria. The ROC equation is shown below:(6)accuracy=TP+TNTP+TN+FP+FN,precision=TPTP+FP,recall=TPTP+FN,F1score=2∗precision∗recallprecision+recall,where TP represents the positive sample correctly predicted by the model, TN represents the negative sample correctly predicted by the model, FP represents the positive sample incorrectly predicted by the model, and FN represents the negative sample incorrectly predicted by the model. Precision is the accuracy of the proportion of all samples predicted correctly by the model. In the predicted positive samples, the proportion of true positive samples is also called the proportion of the positive samples correctly estimated by the recall model of the accuracy. This index measures and reflects the coverage of the actual positive samples. The F1 score shows the average value of accuracy and recall.


### 2.5. Algorithm Experiment Environment

To realize the functional application of each module in PTGAN, PyTorch was used as the basic framework to extract data and calculate the results on NVIDIA GeForce 2080 Ti GPU (Ubuntu 16.04 system). Specifically, ResNet50 X116 was used as the basic structure of the feature extractor factory, the batch size was set toB = 8, the learning rate was set to 0.001, and it decayed as the training progressed. The model optimizer used the stochastic gradient descent (SGD) optimizer to train the model. The patient’s aneurysm image dataset was used as the target domain dataset for training.


### 2.6. Hemodynamic Parameter Calculation

The data collected in this study were from electronic health records of the hospital and physical examination reports of patients, which can detect the patients’ past medical history, and their aneurysm images were used in the dataset of this study.Middle cerebral artery aneurysm stenosis can alter basal ganglia perfusion. Data on changes in basal ganglia perfusion can be used to determine therapeutic strategies. The PTGAN model was used to label the arterial tumor and the parent artery separately. Then, the aneurysm was divided into four areas of interest by using the box section function, which were aneurysm wall (the entire aneurysm wall), aneurysmal neck, aneurysm interior, and aneurysm longitudinal section through the inflow and outflow tract. Mean wall shear stress (WSS) and mean pressure were calculated, as well as mean blood flow velocity and mean pressure of the other three regions of interest.


### 2.7. Treatments

Before surgery, patients with mild aneurysms should take 300 mg aspirin and 75 mg clopidogrel, 1–3 times a day according to their own conditions. Patients were required to undergo thrombolysis tests and drug allergy tests, and the dosage was adjusted individually based on the results. Patients with severe aneurysms should be given 50 mg nimodipine by micropump twice daily. Before surgery, 300 mg aspirin and 75 mg clopidogrel should be used. Meanwhile, medication for hypertension and hyperglycemia should be given according to the patient’s own situation.Under general anesthesia, the trachea was surgically inserted into the right thigh artery, and the sheath was inserted. The optimal surgical angle of PTGAN group was determined according to the results of the algorithm, and the optimal surgical angle of control group was directly determined. At least two suitable mounting bracket attachment points were selected. Aneurysm and aorta were rigorously measured for appropriate PED. The PED diameter must be the same as the maximum inner diameter of the artery or blood vessel, and the length must completely cover the neck of the aneurysm, at least 6 mm across both ends of the neck of the aneurysm. In addition, it was necessary to find the right stent to deliver the catheter to the target location. The stent slowly attracted the microcatheter and released it by pressing on the stent. The stent completely covered the external aneurysm neck and was fixed.All patients were sheathed with 4100U low molecular weight heparin sodium, once every six days for three consecutive days. After surgery, 100 mg aspirin and 75 mg clopidogrel were administered once a day for six months. Routine head CT scan was performed to exclude postoperative complications such as hemorrhagic and ischemic stroke. Patients with subarachnoid hemorrhage during surgery maintained the preoperative dose of nimodipine intravenous micropump for seven days. Six months of postoperative observation were implemented, and clinical symptoms were recorded. Angiography was performed six months after surgery to evaluate aneurysm occlusion and arterial occlusion. Criteria include Raymond grading (grade 1: the aneurysm was completely occluded; grade 2: the aneurysm neck was filled with contrast agent, and the tumor was not visible; and grade 3: the tumor was filled with contrast agent), whether the parent artery was narrowed or occluded, and whether the PED covered branch was occluded.


### 2.8. Statistical Methods

SPSS 20.0 was used for statistical analysis. The measurement data of normal distribution were expressed as mean ± standard deviation. One-way ANOVA was used for comparison between groups. For general data, independent sampleT test was used, and paired sample T test was used to compare the degree of aneurysm occlusion at different time points in the control group. p<0.05 was statistically considerable.


## 2.1. Research Objects

The research subjects were 100 patients with intracranial large aneurysms admitted to X Hospital in X Province from June 2020 to June 2021, which were then enrolled into two groups of 50 people. In one group, the PTGAN model was used to detect the mean blood flow and the mean vascular pressure, and the pipeline embolization device (PED) was used to direct blood flow (PTGAN group). The other group did not use the model to place PED (control group).Inclusion criteria were as follows: (i) patients with intracranial large aneurysm with clinical symptoms or risk of hemorrhage; (ii) patients with large intracranial aneurysm with subarachnoid hemorrhage; (iii) patients aged 25 years or older; and (iv) patients without other brain diseases.Exclusion criteria were as follows: (i) patients with drug allergy; (ii) patients with other arterial diseases; (iii) patients under 25 years of age; (iv) patients with poor compliance; (iv) patients with other brain diseases; and (v) patients that cannot be followed up.In this study, 100 patients with intracranial aneurysms met the above inclusion criteria and exclusion criteria. This study had been approved by the Medical Ethics Committee of the hospital, and the families of patients included in the study had all signed the informed consent form.


## 2.2. Medical Cases and Image Classification Method Based on Artificial Intelligence Unsupervised Big Data

For the classification methods of medical and health cases and images under big data, the performance of the model is not ideal due to the large difference of data distribution types. An unsupervised image classification method based on prototype transfer generative adversarial network (PTGAN) was proposed to realize the classification of unlabeled medical and health data and image data in the target area. Training tests of unsupervised network models were performed on open medical health datasets of patients with large aneurysms. The medical images of patients with large aneurysms were unclear and fuzzy due to different equipment or medical conditions, resulting in loss of effect. In this model, the classified models were placed in the new dataset, and the most suitable arterial images were selected as reference. The PTGAN structural framework is shown in Figure1.Figure 1
PTGAN structural framework.The PTGAN method consists of two main modules of the target domain bias generation network (target), namely, target biased generative adversarial network (TBGAN) and prototype transfer algorithm (PTA). The target domain deviation uses the domain separator loss and the target to generate a hostile network. The prototype migration algorithm module maps the extracted sample function to the cross-domain feature space by cross-domain loss. The label information is used in the source domain to construct the category prototype, and the feature points of the prototype are further used to automatically classify the features of the target area to predict the noncharacteristic samples in the target domain [13].


## 2.3. An Unsupervised Image Model Algorithm Based on Prototype Transfer to Generate Adversarial Network

The unsupervised image model algorithm based on the prototype migration generation confrontation network does not require a new model but migrates the original model to a new dataset to improve the recognition accuracy of large aneurysm images. First, to define the source domain, an image with a more obvious characteristic lesion is required as a reference. PTGAN contains an extractorf to extract features.(1)his=fxis;θf,hjt=fxjt;θf,where θf is the digital parameter in the extracted features. PTGAN extracts and reconstructs his and hjt to generate xˆis and xˆjt applied to the new database.(2)xˆis=ghis;θg,xˆjt=ghjt;θg,where θg is the digital parameter of the new database. At the same time, the generator is used to stylize the structure of the new database model. The new database result G = f ∗ g is similar to the model training database result, so the generator G loss function LG is the following equation.(3)ℒG=1Ns∑i=0Nslog1−DGxis.When the loss function is close to 1, it means that the model has a good effect. The antiloss equation is as follows.(4)ℒD=1Ns∑i=0NslogDGxis+1Nt∑j=0Ntlog1−DGxjt.The purpose of the antiloss is improving the distinguishing ability of the domain discriminator, that is, it is judged that the reconstructed image from the source domain sample is 0, and the reconstructed image from the target domain sample is judged to be 1. The use of loss function and counter-loss function can improve the domain migration ability of the generator through counter-training, which makes the application of the original data model fast, convenient, and accurate. There is also a target domain bias loss function at the end of the algorithm, and the equation is as follows.(5)ℒTB=1Nt∑j=1NtGxjt−xjt2.The target domain bias loss function can ensure that the new data model is not too biased and maintain a maximum bias bottom lineGxjt. When the bias is too large, the algorithm will recalculate until the data are reasonable.


## 2.4. Dataset and Evaluation Criteria

The model dataset came from the health and medical data of patients with craniocerebral aneurysm. The reference dataset contained many characteristic images of cranial aneurysms, including 1,000 image samples of benign and malignant cranial aneurysms, taken at 40x, 100x, 200x, and 400x magnifications. Of which, 70% of the samples were used as the training set, and the remaining 30% were used as the test set. To reflect the versatility of the method, when new patient samples were tested, it was ensured that the patient samples in the training set will not participate in the model test.Accuracy, precision, recall, andF1 score were used as performance evaluation criteria. The ROC equation is shown below:(6)accuracy=TP+TNTP+TN+FP+FN,precision=TPTP+FP,recall=TPTP+FN,F1score=2∗precision∗recallprecision+recall,where TP represents the positive sample correctly predicted by the model, TN represents the negative sample correctly predicted by the model, FP represents the positive sample incorrectly predicted by the model, and FN represents the negative sample incorrectly predicted by the model. Precision is the accuracy of the proportion of all samples predicted correctly by the model. In the predicted positive samples, the proportion of true positive samples is also called the proportion of the positive samples correctly estimated by the recall model of the accuracy. This index measures and reflects the coverage of the actual positive samples. The F1 score shows the average value of accuracy and recall.


## 2.5. Algorithm Experiment Environment

To realize the functional application of each module in PTGAN, PyTorch was used as the basic framework to extract data and calculate the results on NVIDIA GeForce 2080 Ti GPU (Ubuntu 16.04 system). Specifically, ResNet50 X116 was used as the basic structure of the feature extractor factory, the batch size was set toB = 8, the learning rate was set to 0.001, and it decayed as the training progressed. The model optimizer used the stochastic gradient descent (SGD) optimizer to train the model. The patient’s aneurysm image dataset was used as the target domain dataset for training.


## 2.6. Hemodynamic Parameter Calculation

The data collected in this study were from electronic health records of the hospital and physical examination reports of patients, which can detect the patients’ past medical history, and their aneurysm images were used in the dataset of this study.Middle cerebral artery aneurysm stenosis can alter basal ganglia perfusion. Data on changes in basal ganglia perfusion can be used to determine therapeutic strategies. The PTGAN model was used to label the arterial tumor and the parent artery separately. Then, the aneurysm was divided into four areas of interest by using the box section function, which were aneurysm wall (the entire aneurysm wall), aneurysmal neck, aneurysm interior, and aneurysm longitudinal section through the inflow and outflow tract. Mean wall shear stress (WSS) and mean pressure were calculated, as well as mean blood flow velocity and mean pressure of the other three regions of interest.


## 2.7. Treatments

Before surgery, patients with mild aneurysms should take 300 mg aspirin and 75 mg clopidogrel, 1–3 times a day according to their own conditions. Patients were required to undergo thrombolysis tests and drug allergy tests, and the dosage was adjusted individually based on the results. Patients with severe aneurysms should be given 50 mg nimodipine by micropump twice daily. Before surgery, 300 mg aspirin and 75 mg clopidogrel should be used. Meanwhile, medication for hypertension and hyperglycemia should be given according to the patient’s own situation.Under general anesthesia, the trachea was surgically inserted into the right thigh artery, and the sheath was inserted. The optimal surgical angle of PTGAN group was determined according to the results of the algorithm, and the optimal surgical angle of control group was directly determined. At least two suitable mounting bracket attachment points were selected. Aneurysm and aorta were rigorously measured for appropriate PED. The PED diameter must be the same as the maximum inner diameter of the artery or blood vessel, and the length must completely cover the neck of the aneurysm, at least 6 mm across both ends of the neck of the aneurysm. In addition, it was necessary to find the right stent to deliver the catheter to the target location. The stent slowly attracted the microcatheter and released it by pressing on the stent. The stent completely covered the external aneurysm neck and was fixed.All patients were sheathed with 4100U low molecular weight heparin sodium, once every six days for three consecutive days. After surgery, 100 mg aspirin and 75 mg clopidogrel were administered once a day for six months. Routine head CT scan was performed to exclude postoperative complications such as hemorrhagic and ischemic stroke. Patients with subarachnoid hemorrhage during surgery maintained the preoperative dose of nimodipine intravenous micropump for seven days. Six months of postoperative observation were implemented, and clinical symptoms were recorded. Angiography was performed six months after surgery to evaluate aneurysm occlusion and arterial occlusion. Criteria include Raymond grading (grade 1: the aneurysm was completely occluded; grade 2: the aneurysm neck was filled with contrast agent, and the tumor was not visible; and grade 3: the tumor was filled with contrast agent), whether the parent artery was narrowed or occluded, and whether the PED covered branch was occluded.


## 2.8. Statistical Methods

SPSS 20.0 was used for statistical analysis. The measurement data of normal distribution were expressed as mean ± standard deviation. One-way ANOVA was used for comparison between groups. For general data, independent sampleT test was used, and paired sample T test was used to compare the degree of aneurysm occlusion at different time points in the control group. p<0.05 was statistically considerable.


## 3. Results

### 3.1. Feature Map Visualization

The PTGAN method used deep learning technology to automatically learn based on network loss, so the feature map output by the first convolutional layer of the image was visualized to show the performance of network feature extraction. Convolutional neural networks (CNNs) were employed to process images for comparison.Figure2 shows that the PTGAN model can learn feature information from the horizontal and vertical directions. It can effectively extract the main morphological and texture features of aneurysms, indicating that PTGAN had excellent performance feature extraction under an unsupervised framework.Figure 2
Aneurysm 3D visual image results. (a) Original image. (b) CNN model processing. (c) PTGAN model processing.
(a)(b)(c)


### 3.2. Validation of Unsupervised Algorithm Model in Data Classification Calculation Results

To verify the unsupervised algorithm model in the data classification calculation results, accuracy, precision, recall, andF1 score were calculated (Figures 3–6). Compared with the CNN model, the accuracy of the PTGAN model increased by 8.449% (87.452%–79.003%), and the precision increased by 8.347% (91.23%–82.883%). The recall rate increased by 7.011% (87.231%–80.22%), and the F1 score increased by 8.09% (89.73%–81.64%). The results showed that the PTGAN model was better than the CNN model in terms of accuracy, precision, recall, and F1 score. The PTGAN model played an important role in classification and recognition performance. Figure 3 presents comparison of the recognition and classification accuracy of the two groups of models. Figure 4 shows the comparison of the recognition and classification precision of the two groups of models. Figure 5 displays comparison of the recognition and classification recall rate of the two groups of models. Figure 6 shows comparison of the recognition and classification F1 scores of the two groups of models.Figure 3
Comparison of the recognition and classification accuracy of the two groups of models (∗indicated that there was a statistical difference in data between groups, p<0.05).Figure 4
Comparison of the recognition and classification precision of the two groups of models (∗indicated that there was a statistical difference in data between groups, p<0.05).Figure 5
Comparison of the recognition and classification recall rate of the two groups of models (∗indicated that there was a statistical difference in data between groups, p<0.05).Figure 6
Comparison of the recognition and classificationF1 scores of the two groups of models (∗indicated that there was a statistical difference in data between groups, p<0.05).


### 3.3. Hemodynamic Parameters

The mean blood flow velocity and mean pressure in the aneurysm, aneurysmal neck, and the longitudinal section of the aneurysm passing through the inflow and outflow tract were measured in the three regions of interest, as illustrated in Figures7–9. The mean internal blood flow of aneurysm was 0.22 (m/s) after adoption of the PTGAN model and 0.21 (m/s) after adoption of the CNN model, and the difference was 0.01 (m/s), which was considerable (p<0.05). The mean internal blood pressure of aneurysm was 963.42 Pa after adoption of the PTGAN model and 952.77 Pa after adoption of the CNN model, and the difference was 11.65 Pa, which was considerable (p<0.05). It indicated that the PTGAN model had better effect than the CNN model, and blood flow was smooth. Figure 7 presents comparison of the results of the average blood flow in each part of the two groups after treatment. Figure 8 displays comparison of the results of the average blood pressure in each part of the aneurysm between the two groups after treatment. Figure 9 shows comparison of the wall shear stress distribution (WSS) results of each part of the two groups after treatment.Figure 7
Comparison of the results of the average blood flow in each part of the two groups after treatment (∗indicated that there was a statistical difference in data between groups, p<0.05).Figure 8
Comparison of the results of the average blood pressure in each part of the aneurysm between the two groups after treatment (∗indicated that there was a statistical difference in data between groups, p<0.05).Figure 9
Comparison of the wall shear stress distribution (WSS) results of each part of the two groups after treatment (∗indicated that there was a statistical difference in data between groups, p<0.05).


### 3.4. Comparison of Surgical Complications and Symptom Improvement between the Two Groups

Postoperative complications and postoperative relief are very important for the prognosis of patients. The follow-up results are shown in Figures10 and 11. 43 patients in the PTGAN group had symptom relief, and 380 patients in the control group had symptom relief. In contrast, there were five more patients in the PTGAN group with symptom relief, and the difference was considerable (p<0.05). A comprehensive comparison showed that there were 41 patients with postoperative complications in the PTGAN group and 50 patients with postoperative complications in the control group. In contrast, there were nine fewer patients in the PTGAN group suffering from postoperative complications, and the difference was considerable (p<0.05). The results showed that the PTGAN group had a relatively better treatment effect. Figure 10 presents comparison of the results of symptom improvement between the two groups after treatment. Figure 11 shows the comparison of the results of complications after treatment between the two groups.Figure 10
Comparison of the results of symptom improvement between the two groups after treatment (∗indicated that there was a statistical difference in data between groups, p<0.05).Figure 11
Comparison of the results of complications after treatment between the two groups (∗indicated that there was a statistical difference in data between groups, p<0.05).


### 3.5. Follow-Up of Patients with Aneurysm Occlusion after Treatment

The patients’ follow-up aneurysm occlusion after treatment is shown in Figures12 and 13. There were 49 patients in the PTGAN group with complete or almost occluded aneurysms (Raymond grade 1), and 47 patients in the control group had complete or almost occluded aneurysms. There were fewer patients in the PTGAN group with complete or almost occluded aneurysms, and the difference was considerable (p<0.05). In the PTGAN group, there was one patient with incomplete aneurysm occlusion (Raymond grade 2), and there were three patients in the control group with complete or almost occlusion of aneurysms. The difference was considerable (p<0.05). The results showed that the prognosis of the PTGAN group was relatively better, and the patients recovered better. Figure 12 presents comparison of the results of complete occlusion of aneurysm in follow-up after six months of treatment between the two groups. Figure 13 displays comparison of the results of incomplete aneurysm occlusion in the follow-up after treatment between the two groups.Figure 12
Comparison of the results of complete occlusion of aneurysm in follow-up after six months of treatment between the two groups (∗indicated that there was a statistical difference in data between groups, p<0.05).Figure 13
Comparison of the results of incomplete aneurysm occlusion in the follow-up after treatment between the two groups (∗indicated that there was a statistical difference in data between groups, p<0.05).


## 3.1. Feature Map Visualization

The PTGAN method used deep learning technology to automatically learn based on network loss, so the feature map output by the first convolutional layer of the image was visualized to show the performance of network feature extraction. Convolutional neural networks (CNNs) were employed to process images for comparison.Figure2 shows that the PTGAN model can learn feature information from the horizontal and vertical directions. It can effectively extract the main morphological and texture features of aneurysms, indicating that PTGAN had excellent performance feature extraction under an unsupervised framework.Figure 2
Aneurysm 3D visual image results. (a) Original image. (b) CNN model processing. (c) PTGAN model processing.
(a)(b)(c)


## 3.2. Validation of Unsupervised Algorithm Model in Data Classification Calculation Results

To verify the unsupervised algorithm model in the data classification calculation results, accuracy, precision, recall, andF1 score were calculated (Figures 3–6). Compared with the CNN model, the accuracy of the PTGAN model increased by 8.449% (87.452%–79.003%), and the precision increased by 8.347% (91.23%–82.883%). The recall rate increased by 7.011% (87.231%–80.22%), and the F1 score increased by 8.09% (89.73%–81.64%). The results showed that the PTGAN model was better than the CNN model in terms of accuracy, precision, recall, and F1 score. The PTGAN model played an important role in classification and recognition performance. Figure 3 presents comparison of the recognition and classification accuracy of the two groups of models. Figure 4 shows the comparison of the recognition and classification precision of the two groups of models. Figure 5 displays comparison of the recognition and classification recall rate of the two groups of models. Figure 6 shows comparison of the recognition and classification F1 scores of the two groups of models.Figure 3
Comparison of the recognition and classification accuracy of the two groups of models (∗indicated that there was a statistical difference in data between groups, p<0.05).Figure 4
Comparison of the recognition and classification precision of the two groups of models (∗indicated that there was a statistical difference in data between groups, p<0.05).Figure 5
Comparison of the recognition and classification recall rate of the two groups of models (∗indicated that there was a statistical difference in data between groups, p<0.05).Figure 6
Comparison of the recognition and classificationF1 scores of the two groups of models (∗indicated that there was a statistical difference in data between groups, p<0.05).


## 3.3. Hemodynamic Parameters

The mean blood flow velocity and mean pressure in the aneurysm, aneurysmal neck, and the longitudinal section of the aneurysm passing through the inflow and outflow tract were measured in the three regions of interest, as illustrated in Figures7–9. The mean internal blood flow of aneurysm was 0.22 (m/s) after adoption of the PTGAN model and 0.21 (m/s) after adoption of the CNN model, and the difference was 0.01 (m/s), which was considerable (p<0.05). The mean internal blood pressure of aneurysm was 963.42 Pa after adoption of the PTGAN model and 952.77 Pa after adoption of the CNN model, and the difference was 11.65 Pa, which was considerable (p<0.05). It indicated that the PTGAN model had better effect than the CNN model, and blood flow was smooth. Figure 7 presents comparison of the results of the average blood flow in each part of the two groups after treatment. Figure 8 displays comparison of the results of the average blood pressure in each part of the aneurysm between the two groups after treatment. Figure 9 shows comparison of the wall shear stress distribution (WSS) results of each part of the two groups after treatment.Figure 7
Comparison of the results of the average blood flow in each part of the two groups after treatment (∗indicated that there was a statistical difference in data between groups, p<0.05).Figure 8
Comparison of the results of the average blood pressure in each part of the aneurysm between the two groups after treatment (∗indicated that there was a statistical difference in data between groups, p<0.05).Figure 9
Comparison of the wall shear stress distribution (WSS) results of each part of the two groups after treatment (∗indicated that there was a statistical difference in data between groups, p<0.05).


## 3.4. Comparison of Surgical Complications and Symptom Improvement between the Two Groups

Postoperative complications and postoperative relief are very important for the prognosis of patients. The follow-up results are shown in Figures10 and 11. 43 patients in the PTGAN group had symptom relief, and 380 patients in the control group had symptom relief. In contrast, there were five more patients in the PTGAN group with symptom relief, and the difference was considerable (p<0.05). A comprehensive comparison showed that there were 41 patients with postoperative complications in the PTGAN group and 50 patients with postoperative complications in the control group. In contrast, there were nine fewer patients in the PTGAN group suffering from postoperative complications, and the difference was considerable (p<0.05). The results showed that the PTGAN group had a relatively better treatment effect. Figure 10 presents comparison of the results of symptom improvement between the two groups after treatment. Figure 11 shows the comparison of the results of complications after treatment between the two groups.Figure 10
Comparison of the results of symptom improvement between the two groups after treatment (∗indicated that there was a statistical difference in data between groups, p<0.05).Figure 11
Comparison of the results of complications after treatment between the two groups (∗indicated that there was a statistical difference in data between groups, p<0.05).


## 3.5. Follow-Up of Patients with Aneurysm Occlusion after Treatment

The patients’ follow-up aneurysm occlusion after treatment is shown in Figures12 and 13. There were 49 patients in the PTGAN group with complete or almost occluded aneurysms (Raymond grade 1), and 47 patients in the control group had complete or almost occluded aneurysms. There were fewer patients in the PTGAN group with complete or almost occluded aneurysms, and the difference was considerable (p<0.05). In the PTGAN group, there was one patient with incomplete aneurysm occlusion (Raymond grade 2), and there were three patients in the control group with complete or almost occlusion of aneurysms. The difference was considerable (p<0.05). The results showed that the prognosis of the PTGAN group was relatively better, and the patients recovered better. Figure 12 presents comparison of the results of complete occlusion of aneurysm in follow-up after six months of treatment between the two groups. Figure 13 displays comparison of the results of incomplete aneurysm occlusion in the follow-up after treatment between the two groups.Figure 12
Comparison of the results of complete occlusion of aneurysm in follow-up after six months of treatment between the two groups (∗indicated that there was a statistical difference in data between groups, p<0.05).Figure 13
Comparison of the results of incomplete aneurysm occlusion in the follow-up after treatment between the two groups (∗indicated that there was a statistical difference in data between groups, p<0.05).


## 4. Discussion

Hemodynamic factors are generally considered to play an important role in aneurysm treatment, and the regulation of hemodynamic factors is one of the purposes of arterial flow therapy when aneurysm ruptures [14, 15]. The indwelling state of aneurysm stent can greatly change the hemodynamics of aneurysm. In recent years, the blood flow guidance device improves the blood flow guidance ability by providing accurate mesh structure to the guide along the normal vascular anatomical path of aneurysms [16]. Aneurysms have been confirmed in animal and clinical trials, which can not only completely cure aneurysms but also treat early or late rupture and aneurysm bleeding [17]. In recent years, numerical fluid dynamics simulation technology has been widely used to study intracranial arterial flow to emphasize the importance of analyzing dynamic changes of arterial blood circulation [18, 19]. This method is of great interest to researchers for it can assess important hemodynamic parameters and measure blood flow in actual preoperative planning due to its relatively low cost and small wound characteristics [20, 21]. In this research, the PTGAN model was used to screen medical and health big data to detect mean arterial blood flow and mean blood pressure. For hemodynamic analysis, the accuracy of blood flow parameters was high, and the blood vessels and blood were simplified and approximate, which affected the accuracy of hemodynamic analysis. Good results were obtained by guiding the placement of PED stent [22, 23]. The patient had fewer complications, and the re-examination at six months showed that aneurysm occlusion was good, providing theoretical basis for aneurysm treatment. The prospect of unsupervised learning guided by artificial intelligence is very good [24–27]. It will save social resources, bring more convenience to people, and eventually become an effective tool for diagnosis and treatment of large aneurysm.


## 5. Conclusion

In this research, PED stents with unsupervised learning algorithms were used to treat large intracranial aneurysms. Unsupervised learning model algorithm was used to screen aneurysm health big data, find aneurysm blood flow and PED stent positioning characteristic parameters, and guide PED stent treatment of intracranial aneurysms. Through this study, it was found that the PTGAN model was superior to the CNN model in terms of accuracy, precision, recall rate, andF1 score. The PTGAN model was better than the CNN model in detecting mean blood flow velocity and mean blood pressure after treatment, and blood flow was smooth. Moreover, postoperative complications and postoperative relief were also better than those of the control group. It is concluded that the adoption effect of PED stent in the treatment of intracranial aneurysm based on unsupervised learning algorithm is good and suitable for subsequent treatment.



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*Source: 8509195-2022-01-04.xml*