# Deep Learning and Microscopic Imaging in the Nursing Process of Neurosurgery Operation

Authors: Wenchun Jiang
Journal: Journal of Healthcare Engineering
 (2022)

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## Abstract

Neurosurgery is mainly for the treatment of head trauma, cerebrovascular disease, brain tumors, and spinal cord disorders. These operations are difficult and risky, so disability and mortality are high. To reduce the risk of surgery, reduce postoperative complications, and improve the treatment effect of patients, this article applies deep learning and microscopic imaging to the nursing process of neurosurgery. Through deep learning and microscopic imaging, doctors can learn about patients during surgery. The specific situation of the trauma site, after which surgery is performed according to the situation, effectively reduces the casualties, reduces the loss of patients, and provides a reference for the research of neurosurgery nursing. Research results prove that deep learning and microscopic imaging can play an important role in the nursing process of neurosurgery. Compared with conventional treatment methods, microscopic imaging treatment can effectively improve the treatment effect, and the operation time for patients is less than that of conventional treatment. About 20% and the incidence of postoperative complications is lower than 30%, which can effectively reduce the cost to patients and improve the quality of treatment.


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## Body

## 1. Introduction

With the continuous development of microneurosurgery technology, higher requirements are put forward for intraoperative neuroelectrophysiological monitoring technology and anesthesiologists. Neurosurgery mainly involves the center of life and the nerve nucleus. The risk of surgery is higher than in other surgeries, and the disability and mortality are higher. Therefore, this type of surgery has always been performed jointly by the surgical group, the neuroelectrophysiological monitoring group, and the anesthesia group. At the same time, surgeons and anesthesiologists have gradually realized the importance of neuroelectrophysiological monitoring in surgical operations. Surgical treatment includes abdominal drainage, large craniotomy with a cortical syringe, small craniotomy with small craniotomy, and neuroendoscopic surgery. Among various surgical treatments, due to the limitations of local medical technology and conditions, different regions also adopt different surgical methods.Computer vision research how to make the machine “see,” let the machine have eyes like a human, see the image, and automatically extract, analyze, and understand the useful information in the image, which is the foundation of the world of machine knowledge. In recent years, based on deep learning algorithms, especially research based on Convolutional Neural Networks (CNN), the field of vision of computers has experienced many progressive disturbances. Among them, the fast-developing target detection and recognition technologies are mainly concentrated in public datasets such as ImageNet and COCO. It has achieved groundbreaking results. The image recognition model based on the ensemble neural network has been able to distinguish thousands of targets, and the accuracy of the recognition even exceeds the level of standard people. In terms of face recognition, the last algorithm achieved 99,83% accuracy on the unlimited face recognition data set (Labeled Faces in the Wild, LWF), which is almost the same as the result of human naked eye recognition.For neurosurgery care, there are many studies on the home expert page. Liu and Zhu investigated the impact of the clinical application of predictive care during the perioperative period on reducing the mental complications of patients in the intensive care unit (ICU) after neurosurgery, including 129 patients undergoing neurosurgery and receiving intensive care. These patients were divided into two groups: the experimental group (n = 68) received predictive care before and after the operation; the control group (n = 61) general care. The clinical data such as the length of stay in the ICU, the duration of patients’ psychiatric symptoms, the form and incidence of adverse events, and patient satisfaction were recorded, and the differences between the two groups were analyzed. The duration of mental symptoms and ICU stay in the experimental group were significantly shorter than those in the control group (P < 0.05) [1]. Aylward et al. examined the potential utility of structural MRI measurements as a measurement of the results of this type of test. In total, 211 precursor individuals and 60 controls were scanned at baseline and 2-year follow-up. Participants in the prodromal phase were divided into several groups according to their closeness to the estimated onset of the diagnosable clinical disease: distant (estimated time to onset > 15 years), moderate (9–15 years), and near (<9 years). The atrophy rates of the striatum, whole brain, and white matter (especially the frontal lobe) of all prodromal groups were higher than those of the control group [2]. Ferro et al. retrospectively registered cases of acute CVT treated by decompression surgery (craniotomy or hematoma removal) in 22 centers and systematically reviewed all published cases of decompression surgery for CVT. The main outcome is the modified Rankin scale (mRS) score at the last follow-up, which is divided into favorable (mRS scores 0–4) and unfavorable outcome (mRS score, 5 points or death). Secondary outcomes were complete recovery (mRS score 0–1), independence (mRS score 0–2), severe dependence (mRS score 4–5), and death at the last available follow-up, including 69 patients and 38 are on the registration form. Forty-five patients underwent decompressive craniectomy, 7 underwent hematoma removal, and 17 underwent two interventions. At the last follow-up (median, 12 months), only 12 people (17.4%) had adverse results [3]. Isaac believed that in neurosurgery, craniofacial osteoma is a benign tumor at the base of the skull, often involving the sinuses. The frontal sinus is the most commonly affected site, followed by the ethmoid sinus, maxillary sinus, and sphenoid sinus. The growth rate is very slow, and it may take many years for bone tumors to appear clinically. The origin of these tumors is due to embryonic tissue dysplasia, trauma, or infection. The tumor is hard, lobulated, ivory, and often mixed with coarse particles. Bones are dense or cancellous, with vascular or connective tissue components. Complications of osteoma growth are sinus ostium obstruction, expansion to adjacent bones and intracranial cavity, and anatomical displacement. The treatment of uncomplicated sinus osteoma is controversial because of the serious potential risks involved in surgery. When undergoing surgery, these tumors can be successfully managed through endoscopy, open or combined techniques [4].Wu and Feng first reviewed the development history of artificial neural networks and related theories and introduced four characteristics of artificial neural networks: nonlinear, nonrestrictive, nonquantitative, and nonconvex. Then, it analyzes its application in information, medicine, economy, control, transportation, psychology, and so on. Finally, the future development trend of the artificial neural network is prospected and summarized [5]. These studies have a certain reference effect for this article, but due to various factors, the results of the studies are difficult to reproduce in practice, so they do not have universal effects.The innovation of this article is to analyze the shortcomings of the original neurosurgery nursing methods, understand the shortcomings of the existing methods, combine deep learning and microscopic imaging with neurosurgical surgical nursing so that the neurosurgery can understand the patient’s injury and disease very intuitively during the operation, and provide a reference for the surgical resection. In the postoperative care process, microscopic imaging can understand the patient after the operation. The recovery details can reduce the incidence of postoperative complications.


## 2. Neurosurgery Nursing Analysis Methods

### 2.1. Deep Learning and Microscopic Imaging

Deep learning comes from artificial neural networks. In the early days, it was mainly represented by multilevel adventure activities. It is optimized using the backpropagation algorithm. However, due to the problem of tilt loss or tilt explosion during training, there is no research on neural networks [6]. An important discovery in recent years is that due to the rapid improvement of large-scale review collections and the rapid improvement of large-scale computer capabilities, the popularity and practicability of deep learning have been greatly improved, and the complexity of deep learning models has also increased. Segmentation and other computer vision work, speech recognition and automatic translation, document classification, sentiment analysis, question answering systems, and other language processing operations have increased accuracy. The neural network has strong features and expression extraction ability. It can transform the initial high-dimensional information into high-level functions that are conducive to learning goals through a large amount of data education [7]. Cooperative neural networks in grid topology data processing and repeated neural networks in sequential data processing are the more popular neural network types that are current research hotspots.CNN is a neural network specially used for processing grid data. CNN usually contains three elements: conference operation, nonlinear activation, and concentration. The overall level is composed of a set of core conference courses of the same scale, and each core conference is responsible for outputting different types of conference characteristics [8]. Taking an image as an example, each neuron on the CNN only connects and responds to a local area of the image, which is called local perception. A cluster core slides and scans each area in the image to form a corresponding feature map. Different systolic nuclei produce different feature maps so that the parameters can be shared in the entire neural network [9]. The local perception and weight distribution characteristics in the ranking function significantly reduce the number of parameters and storage requirements of the model, thereby reducing the complexity of the model. The local perception feature and weight distribution of the collective neural network are shown in Figure 1. Each neuron is connected to only a few image patches. In the image and feature maps, different color features represent different types of features derived from different connected cores [10].Figure 1
Local perception and weight sharing of convolution operation in CNN.The parameters of the convolutional layer include the size of the convolution kernel, the stride, and the padding, which together determine the size of the output feature map of the convolutional layer and are the hyperparameters of the convolutional neural network. The convolution kernel size can be specified as an arbitrary value smaller than the input image size. The larger the convolution kernel, the more complex the input features that can be extracted.The convolution step size defines the distance between the positions of the convolution kernel when it scans the feature map twice. When the convolution step size is 1, the convolution kernel will scan the elements of the feature map one by one. When the step size isn, the next time the scan skips n-1 pixels.It can be seen from the cross-correlation calculation of the convolution kernel that the size of the feature map will gradually decrease with the stacking of the convolution layers. After that, a 12 × 12 feature map will be output. To this end, padding is a method of artificially increasing the size of the feature map before it passes through the convolution kernel to offset the effect of size shrinkage in the computation. Common padding methods are padding by 0 and repeating boundary values.Forward Network. Each neuron in the network accepts the input of the previous stage and outputs it to the next stage. There is no feedback in the network, which can be represented by a directed acyclic graph. This kind of network realizes the transformation of the signal from the input space to the output space, and its information processing capability comes from multiple composites of simple nonlinear functions. The network structure is simple and easy to implement. A backpropagation network is a typical forward network.Feedback Network. There is feedback between neurons in the network, which can be represented by an undirected complete graph. The information processing of this kind of neural network is the transformation of state, which can be dealt with by dynamical system theory. The stability of the system is closely related to the associative memory function. Hopfield networks and Boltzmann machines belong to this type.In artificial neural networks, neuron processing units can represent different objects, such as features, letters, concepts, or some meaningful abstract patterns. The types of processing units in the network are divided into three categories: input units, output units, and hidden units. The input unit accepts signals and data from the outside world; the output unit realizes the output of the system processing results; the hidden unit is the unit between the input and output units and cannot be observed by the outside of the system. The connection weight between neurons reflects the connection strength between units, and the representation and processing of information are reflected in the connection relationship of network processing units. An artificial neural network is a nonprogrammed, adaptive, brain-style information processing. Its essence is to obtain a parallel and distributed information processing function through the transformation and dynamic behavior of the network and to imitate human beings with different degrees and levels. Information processing function of the brain nervous system is an interdisciplinary subject involving neuroscience, thinking science, artificial intelligence, computer science, and other fields. The artificial neural network is a parallel distributed system. It adopts a completely different mechanism from traditional artificial intelligence and information processing technology. It overcomes the shortcomings of traditional artificial intelligence based on logical symbols in processing intuitive and unstructured information and has features of self-organization and real-time learning.The nonlinear activation function is an indispensable part of the convolutional neural network. Usually, the nonlinear activation function is used after the convolution operation to introduce nonlinearity and improve the expressive ability of the model [11, 12]. In the early deep learning models, the sigmoid function and the hyperbolic tangent (tanh) function are the most widely used nonlinear activation functions. The mathematical form of the sigmoid function is as follows:(1)σx=11+e−x,tanx=ex−e−xex+e−x.σxis the input normalization.However, the sigmoid function and tanh function tend to be saturated in most of their domains. This wide saturation easily causes the problem of gradient disappearance when the number of network layers is deepened, making it difficult for deep neural networks to be effectively trained [13]. Rectified Linear Unit (ReLU) is currently one of the most commonly used activation functions. ReLU not only effectively alleviates the problem of gradient disappearance but also introduces sparsity and speeds up the calculation. Its function form is as follows:(2)ReLUx=x,ifx>0,0,ifx≤0.x is the hard saturation.The special hierarchical structure and characteristics of convolutional neural networks are perfectly suitable for image processing tasks. The unique advantage of CNN lies in its powerful feature extraction capabilities. Nowadays, CNN has basically replaced traditional HOG, SIFT, and other artificial features and has become the mainstream means of image feature extraction [14]. CNN learns the input-to-output mapping from the training data and automatically extracts image features. CNN’s shallow network can extract detailed features such as the edge and color of the image, and the deep network extracts more discriminative high-level abstract semantic features by combining low-level features. To avoid the complicated process of explicit manual feature extraction, it is often used as a feature extractor and is widely used in various fields [15].Get image result:(3)∇Iz=F−zBz∇α2+α2∇Fz+1−α2∇Bz.Assuming that the foreground and background images have local smooth characteristics, the gradient domain of the image is as follows:(4)α∗=argminα∫∫z∈Ω‖∇α2−1Fz−Bz,where Ω represents the entire unknown area in the three-part graph. The color distribution of all pixels is approximately on the same line segment. Under this assumption, the value of α in the small window w can be obtained by the following formula:(5)αi=∑cacIic+b,∀i∈w,where c represents the three channels of RGB, and ac and b are constants in the same window. To get ac and b, you can use the following formula to calculate(6)Jα,a,b=∑i∈I∑i∈wjαi−∑cajc−bj2.The minimization of the cost function can be transformed into a linear least squares problem related only toα:(7)Jα=αTLα,where L is an N × N matrix, called matting Laplacian matrix. Therefore, the problem is transformed into finding the value of α that minimizes the cost function; that is, following the quadratic error minimization problem, the result can be obtained using the linear system solver:(8)α=arg,minαTLα,s.t.ai=1or0,∀i∈λΩ.The gray value of an image can be expressed as a random variable in the interval [0, 1]. The probability density function is one of the most basic descriptions of random variables, which can be obtained as follows:(9)ds=prrdaPaa=prrda1=Prrdr.Thereby,(10)s=Tr=∫0rPrsda,whereTr=n!/r!n−r! is the cumulative distribution function, and the digitized formula for the uniform distribution of the histogram is as follows:(11)sk=Trk=∑i=0kPrj=∑j=0knjn.Assign thesk calculated in the formula to each quantization level according to a fixed interval:(12)lk=skL−1,defined as(13)Zn=12∑m=−∞∞sgnxm−sgnxm−1wn−m.Whenn is less than 0,(14)sgnn=1,n≥0,−1,n≤0.The basic idea of prescribing is to transform the histogram of the input image to obtain a histogram of a specific shape for specific enhancement.Compared with Sigmoid and tanh, ReLU can converge quickly in SGD. Allegedly, this is because of its linear, unsaturated form. Sigmoid and tanh involve a lot of expensive operations (such as exponentials), and ReLU can be implemented more simply and effectively alleviate the problem of gradient disappearance. It can also perform well without unsupervised pretraining and provide the sparse representation capability of neural networks.Neural network is a parallel computing method with the ability to process data at high speed, which makes it useful for solving time-varying matrix problems in real time. The scope of the existing recurrent neural network is zero to infinity, and the periodic time-varying problem is a kind of periodic time running problem, and its action time is one cycle time. Periodic time-varying matrix inversion quadratic programming problem with periodicity is a kind of general periodic time-varying matrix problem. Periodic time-varying matrix inversion includes periodic time-varying square matrix inversion and periodic time-varying matrix generalized inversion. The redundant manipulator trajectory planning problem is a kind of practical manifestation of the periodic time-varying problem. For such periodic time-varying problems, this article proposes a class of repetitive neural networks. Compared with the general infinite interval neural computation, this kind of network is mainly aimed at the computation of periodic time-varying matrices.


### 2.2. Neurosurgery

Deep learning and microscopic imaging technology provide new ideas and solutions for medical diagnosis and treatment. This technology can perfectly observe tissues and organs, avoiding the ethical problems caused by organ transplantation [16]. At the same time, due to the development of auxiliary medical data processing software, the patient’s clinical data are transformed into more intuitive image data. With the growth, deep learning and microscopic imaging technologies are increasingly used in clinics, and organ transplantation has also been rapidly developed, such as vascular surgery, tumor surgery, plastic surgery, liver surgery, and neurosurgery.Intraoperative neuroelectrophysiological monitoring can use these parameters to change the rule of neuroelectrophysiological signal changes to find lesions and lesions in neurosurgery, work as many nerves as possible, and reduce disability and mortality. Neurosurgery generally takes a long time and puts forward higher requirements for anesthesia [17]. The ideal neurosurgery operation attempts to induce a rapid and stable process, maintain the process of inhibition and analgesia without bad energy in the nervous system, side effects after drug withdrawal, waking up quickly, and no other psychiatric symptoms.Neurosurgery learning has the characteristics of complexity, three-dimensional accuracy, precision, high requirements, and high difficulty. Therefore, the learning cycle of this topic is longer than that of other subjects [18]. At the same time, there are higher requirements for anatomical ownership, surgical difficulty, and lower level of doctors. It is difficult to complete a more complex operation independently. Second, skull anatomy is more complex and sensitive than other systems. The medical school does not have a fresh body to teach students and clinicians. This increases the difficulty of neurosurgery and the invisible development of new doctors [19].Two-dimensional and three-dimensional image processing is performed on the original data, and two digital processing methods are used for the first time: after filtering, image preprocessing is performed using felt image segmentation and felt composite. The computer system can then convert the digital model into a physically visible model and convert the data model into a model that can be identified by microscopic imaging. Finally, we use microscopic imaging technology to quickly establish a simulated disease model, which contains the patient’s brain tissue [20]. Therefore, the process includes the collection of raw data, inputting the data into relevant software for two-dimensional and three-dimensional processing, and then modeling the processed three-dimensional data.Because neurosurgery involves important life centers and nerve nuclei, the risk of surgery is relatively high, the disability rate and mortality of surgery are high, and the safety of surgery has always been the focus of the surgical team. With the development of deep learning and microscopic imaging technology, neurosurgery and surgical teams have gradually realized the importance of deep learning and microscopic imaging in surgical care [21]. Apply deep learning and microscopic imaging technology to monitor the function of the nervous system under dangerous conditions, understand the changes of electrophysiological signals in the process of neurotransmitters, reduce nerve damage, improve the effect of surgical treatment, and help surgeons make comprehensive and timely judgments on the integrity anesthetize the nerve function of the patient. Deep learning and microscopic imaging can monitor neurophysiological changes, prevent intraoperative nerve damage, explain the clinical significance of signal changes, and guide surgeons to decide on surgical strategies [22].The correct body position is not only conducive to the smooth completion of the operation, but also conducive to the observation and nursing of the patient. The side prone position has the following advantages over the side lying position. (1) This position avoids pressure on the abdomen and has little impact on the patient’s breathing and circulation; (2) It is not easy to fall off the tracheal intubation and pressure ulcers on the face; (3) It is easy for the operator to operate, and it has a good angle of view. Fatigue, and there is a blind spot in the microscope viewing area and the projection light source in the lateral position.


### 2.3. Postoperative Care

Surgical site infections are infections that occur in the surgical incision, organ, or cavity during surgery. We counted the cases in a hospital in this city and obtained the statistical data with the consent of the patients. SSI is a common postoperative complication belonging to third-party nosocomial infections, accounting for 14% to 16% of all nosocomial infections, and 35% to 40% of surgical nosocomial infections. Once SSI appears, it will prolong the patient’s hospital stay, increase the patient’s hospitalization costs, and even cause the patient’s death. At the same time, it will consume a lot of medical resources, seriously affect the quality of medical care, and bring a serious burden to patients and hospitals.Neurosurgery nurses’ SSI prevention measures knowledge scores are positively correlated with their behaviors, indicating that the higher the nurse’s prevention knowledge level, the greater the consistency of the clinical behavior of prevention and control [23, 24]. In the face of severe SSI, high incidence, managers should start with prevention, control, management, etc., pay attention to SSI prevention, strengthen nurse knowledge training, improve prevention knowledge, and at the same time, strengthen supervision and management to improve nurses’ compliance with SSI prevention and treatment measures. Prevent and control the occurrence of infection at the surgical site.In actual work, the factors that affect the nurses’ ability to implement surgical site infection prevention and control measures are not just the nurse’s personal cognition and weak awareness of prevention and control, the attention of the management department, the completeness of the facilities, the supervision and management system, and the training and education. Effectiveness, etc., will affect the nurses’ implementation of infection prevention and control measures at the surgical site to a certain extent. In addition to training nurses, this study also applied the Ebbinghaus forgetting curve theory to regularly assess the nurses’ awareness and implementation of infection prevention and control measures, to discover problems in actual work in a timely manner and carry out continuous quality improvement. At the same time, give full play to the supervision and guidance of the intervention team and head nurses on the prevention and control of infection at the surgical site of nurses, and promote the effective implementation of prevention and control measures for SSI infection by nurses. It can be seen that SSI infection prevention and control management involves overall systemic management. Through training, nurses’ awareness of surgical site infections can be improved, helping nurses to establish health beliefs, and allowing nurses to fundamentally realize the importance of surgical site infection prevention and control. It is necessary to change health behaviors and improve nurses’ compliance with surgical site infection prevention and control measures; in addition, it is necessary to provide necessary logistical support to ensure nurses’ access to infection prevention and control facilities; and at the same time enhance the management of surgical site infections pay attention to prevention and control work, strengthen the supervision and management of clinical sensory control work, and improve the compliance of nurses’ behaviors in the prevention and control of infection at the surgical site under systematic management.


### 2.4. Microscopic Optical Imaging

Microscopic optical imaging, also commonly referred to as “optical microscopic imaging,” or “optical microscopy,” refers to the ability to obtain magnification of a tiny sample by passing visible light through or reflected from a sample through one or more lenses using image technologies. The obtained image can be directly observed with the eyes through the eyepiece, or recorded with a photosensitive plate or a digital image detector such as CCD and CMOS, and can also be displayed and analyzed on a computer.Ordinary optical microscopy using bright-field illumination usually has three limitations: first, it can only image dark samples (transmission type) or strongly reflective samples (reflection type); second, the optical diffraction limit limits the highest resolution of this technique is about 200 nm; third, out-of-focus information reduces the image contrast. Fluorescence microscopy, based on the excitation and fluorescence emission of fluorescent molecules in the sample (exogenous or endogenous), can overcome the limitations of imaging transparent samples, but the problems of limited resolution and out-of-focus interference still require additional measures to be resolved.


## 2.1. Deep Learning and Microscopic Imaging

Deep learning comes from artificial neural networks. In the early days, it was mainly represented by multilevel adventure activities. It is optimized using the backpropagation algorithm. However, due to the problem of tilt loss or tilt explosion during training, there is no research on neural networks [6]. An important discovery in recent years is that due to the rapid improvement of large-scale review collections and the rapid improvement of large-scale computer capabilities, the popularity and practicability of deep learning have been greatly improved, and the complexity of deep learning models has also increased. Segmentation and other computer vision work, speech recognition and automatic translation, document classification, sentiment analysis, question answering systems, and other language processing operations have increased accuracy. The neural network has strong features and expression extraction ability. It can transform the initial high-dimensional information into high-level functions that are conducive to learning goals through a large amount of data education [7]. Cooperative neural networks in grid topology data processing and repeated neural networks in sequential data processing are the more popular neural network types that are current research hotspots.CNN is a neural network specially used for processing grid data. CNN usually contains three elements: conference operation, nonlinear activation, and concentration. The overall level is composed of a set of core conference courses of the same scale, and each core conference is responsible for outputting different types of conference characteristics [8]. Taking an image as an example, each neuron on the CNN only connects and responds to a local area of the image, which is called local perception. A cluster core slides and scans each area in the image to form a corresponding feature map. Different systolic nuclei produce different feature maps so that the parameters can be shared in the entire neural network [9]. The local perception and weight distribution characteristics in the ranking function significantly reduce the number of parameters and storage requirements of the model, thereby reducing the complexity of the model. The local perception feature and weight distribution of the collective neural network are shown in Figure 1. Each neuron is connected to only a few image patches. In the image and feature maps, different color features represent different types of features derived from different connected cores [10].Figure 1
Local perception and weight sharing of convolution operation in CNN.The parameters of the convolutional layer include the size of the convolution kernel, the stride, and the padding, which together determine the size of the output feature map of the convolutional layer and are the hyperparameters of the convolutional neural network. The convolution kernel size can be specified as an arbitrary value smaller than the input image size. The larger the convolution kernel, the more complex the input features that can be extracted.The convolution step size defines the distance between the positions of the convolution kernel when it scans the feature map twice. When the convolution step size is 1, the convolution kernel will scan the elements of the feature map one by one. When the step size isn, the next time the scan skips n-1 pixels.It can be seen from the cross-correlation calculation of the convolution kernel that the size of the feature map will gradually decrease with the stacking of the convolution layers. After that, a 12 × 12 feature map will be output. To this end, padding is a method of artificially increasing the size of the feature map before it passes through the convolution kernel to offset the effect of size shrinkage in the computation. Common padding methods are padding by 0 and repeating boundary values.Forward Network. Each neuron in the network accepts the input of the previous stage and outputs it to the next stage. There is no feedback in the network, which can be represented by a directed acyclic graph. This kind of network realizes the transformation of the signal from the input space to the output space, and its information processing capability comes from multiple composites of simple nonlinear functions. The network structure is simple and easy to implement. A backpropagation network is a typical forward network.Feedback Network. There is feedback between neurons in the network, which can be represented by an undirected complete graph. The information processing of this kind of neural network is the transformation of state, which can be dealt with by dynamical system theory. The stability of the system is closely related to the associative memory function. Hopfield networks and Boltzmann machines belong to this type.In artificial neural networks, neuron processing units can represent different objects, such as features, letters, concepts, or some meaningful abstract patterns. The types of processing units in the network are divided into three categories: input units, output units, and hidden units. The input unit accepts signals and data from the outside world; the output unit realizes the output of the system processing results; the hidden unit is the unit between the input and output units and cannot be observed by the outside of the system. The connection weight between neurons reflects the connection strength between units, and the representation and processing of information are reflected in the connection relationship of network processing units. An artificial neural network is a nonprogrammed, adaptive, brain-style information processing. Its essence is to obtain a parallel and distributed information processing function through the transformation and dynamic behavior of the network and to imitate human beings with different degrees and levels. Information processing function of the brain nervous system is an interdisciplinary subject involving neuroscience, thinking science, artificial intelligence, computer science, and other fields. The artificial neural network is a parallel distributed system. It adopts a completely different mechanism from traditional artificial intelligence and information processing technology. It overcomes the shortcomings of traditional artificial intelligence based on logical symbols in processing intuitive and unstructured information and has features of self-organization and real-time learning.The nonlinear activation function is an indispensable part of the convolutional neural network. Usually, the nonlinear activation function is used after the convolution operation to introduce nonlinearity and improve the expressive ability of the model [11, 12]. In the early deep learning models, the sigmoid function and the hyperbolic tangent (tanh) function are the most widely used nonlinear activation functions. The mathematical form of the sigmoid function is as follows:(1)σx=11+e−x,tanx=ex−e−xex+e−x.σxis the input normalization.However, the sigmoid function and tanh function tend to be saturated in most of their domains. This wide saturation easily causes the problem of gradient disappearance when the number of network layers is deepened, making it difficult for deep neural networks to be effectively trained [13]. Rectified Linear Unit (ReLU) is currently one of the most commonly used activation functions. ReLU not only effectively alleviates the problem of gradient disappearance but also introduces sparsity and speeds up the calculation. Its function form is as follows:(2)ReLUx=x,ifx>0,0,ifx≤0.x is the hard saturation.The special hierarchical structure and characteristics of convolutional neural networks are perfectly suitable for image processing tasks. The unique advantage of CNN lies in its powerful feature extraction capabilities. Nowadays, CNN has basically replaced traditional HOG, SIFT, and other artificial features and has become the mainstream means of image feature extraction [14]. CNN learns the input-to-output mapping from the training data and automatically extracts image features. CNN’s shallow network can extract detailed features such as the edge and color of the image, and the deep network extracts more discriminative high-level abstract semantic features by combining low-level features. To avoid the complicated process of explicit manual feature extraction, it is often used as a feature extractor and is widely used in various fields [15].Get image result:(3)∇Iz=F−zBz∇α2+α2∇Fz+1−α2∇Bz.Assuming that the foreground and background images have local smooth characteristics, the gradient domain of the image is as follows:(4)α∗=argminα∫∫z∈Ω‖∇α2−1Fz−Bz,where Ω represents the entire unknown area in the three-part graph. The color distribution of all pixels is approximately on the same line segment. Under this assumption, the value of α in the small window w can be obtained by the following formula:(5)αi=∑cacIic+b,∀i∈w,where c represents the three channels of RGB, and ac and b are constants in the same window. To get ac and b, you can use the following formula to calculate(6)Jα,a,b=∑i∈I∑i∈wjαi−∑cajc−bj2.The minimization of the cost function can be transformed into a linear least squares problem related only toα:(7)Jα=αTLα,where L is an N × N matrix, called matting Laplacian matrix. Therefore, the problem is transformed into finding the value of α that minimizes the cost function; that is, following the quadratic error minimization problem, the result can be obtained using the linear system solver:(8)α=arg,minαTLα,s.t.ai=1or0,∀i∈λΩ.The gray value of an image can be expressed as a random variable in the interval [0, 1]. The probability density function is one of the most basic descriptions of random variables, which can be obtained as follows:(9)ds=prrdaPaa=prrda1=Prrdr.Thereby,(10)s=Tr=∫0rPrsda,whereTr=n!/r!n−r! is the cumulative distribution function, and the digitized formula for the uniform distribution of the histogram is as follows:(11)sk=Trk=∑i=0kPrj=∑j=0knjn.Assign thesk calculated in the formula to each quantization level according to a fixed interval:(12)lk=skL−1,defined as(13)Zn=12∑m=−∞∞sgnxm−sgnxm−1wn−m.Whenn is less than 0,(14)sgnn=1,n≥0,−1,n≤0.The basic idea of prescribing is to transform the histogram of the input image to obtain a histogram of a specific shape for specific enhancement.Compared with Sigmoid and tanh, ReLU can converge quickly in SGD. Allegedly, this is because of its linear, unsaturated form. Sigmoid and tanh involve a lot of expensive operations (such as exponentials), and ReLU can be implemented more simply and effectively alleviate the problem of gradient disappearance. It can also perform well without unsupervised pretraining and provide the sparse representation capability of neural networks.Neural network is a parallel computing method with the ability to process data at high speed, which makes it useful for solving time-varying matrix problems in real time. The scope of the existing recurrent neural network is zero to infinity, and the periodic time-varying problem is a kind of periodic time running problem, and its action time is one cycle time. Periodic time-varying matrix inversion quadratic programming problem with periodicity is a kind of general periodic time-varying matrix problem. Periodic time-varying matrix inversion includes periodic time-varying square matrix inversion and periodic time-varying matrix generalized inversion. The redundant manipulator trajectory planning problem is a kind of practical manifestation of the periodic time-varying problem. For such periodic time-varying problems, this article proposes a class of repetitive neural networks. Compared with the general infinite interval neural computation, this kind of network is mainly aimed at the computation of periodic time-varying matrices.


## 2.2. Neurosurgery

Deep learning and microscopic imaging technology provide new ideas and solutions for medical diagnosis and treatment. This technology can perfectly observe tissues and organs, avoiding the ethical problems caused by organ transplantation [16]. At the same time, due to the development of auxiliary medical data processing software, the patient’s clinical data are transformed into more intuitive image data. With the growth, deep learning and microscopic imaging technologies are increasingly used in clinics, and organ transplantation has also been rapidly developed, such as vascular surgery, tumor surgery, plastic surgery, liver surgery, and neurosurgery.Intraoperative neuroelectrophysiological monitoring can use these parameters to change the rule of neuroelectrophysiological signal changes to find lesions and lesions in neurosurgery, work as many nerves as possible, and reduce disability and mortality. Neurosurgery generally takes a long time and puts forward higher requirements for anesthesia [17]. The ideal neurosurgery operation attempts to induce a rapid and stable process, maintain the process of inhibition and analgesia without bad energy in the nervous system, side effects after drug withdrawal, waking up quickly, and no other psychiatric symptoms.Neurosurgery learning has the characteristics of complexity, three-dimensional accuracy, precision, high requirements, and high difficulty. Therefore, the learning cycle of this topic is longer than that of other subjects [18]. At the same time, there are higher requirements for anatomical ownership, surgical difficulty, and lower level of doctors. It is difficult to complete a more complex operation independently. Second, skull anatomy is more complex and sensitive than other systems. The medical school does not have a fresh body to teach students and clinicians. This increases the difficulty of neurosurgery and the invisible development of new doctors [19].Two-dimensional and three-dimensional image processing is performed on the original data, and two digital processing methods are used for the first time: after filtering, image preprocessing is performed using felt image segmentation and felt composite. The computer system can then convert the digital model into a physically visible model and convert the data model into a model that can be identified by microscopic imaging. Finally, we use microscopic imaging technology to quickly establish a simulated disease model, which contains the patient’s brain tissue [20]. Therefore, the process includes the collection of raw data, inputting the data into relevant software for two-dimensional and three-dimensional processing, and then modeling the processed three-dimensional data.Because neurosurgery involves important life centers and nerve nuclei, the risk of surgery is relatively high, the disability rate and mortality of surgery are high, and the safety of surgery has always been the focus of the surgical team. With the development of deep learning and microscopic imaging technology, neurosurgery and surgical teams have gradually realized the importance of deep learning and microscopic imaging in surgical care [21]. Apply deep learning and microscopic imaging technology to monitor the function of the nervous system under dangerous conditions, understand the changes of electrophysiological signals in the process of neurotransmitters, reduce nerve damage, improve the effect of surgical treatment, and help surgeons make comprehensive and timely judgments on the integrity anesthetize the nerve function of the patient. Deep learning and microscopic imaging can monitor neurophysiological changes, prevent intraoperative nerve damage, explain the clinical significance of signal changes, and guide surgeons to decide on surgical strategies [22].The correct body position is not only conducive to the smooth completion of the operation, but also conducive to the observation and nursing of the patient. The side prone position has the following advantages over the side lying position. (1) This position avoids pressure on the abdomen and has little impact on the patient’s breathing and circulation; (2) It is not easy to fall off the tracheal intubation and pressure ulcers on the face; (3) It is easy for the operator to operate, and it has a good angle of view. Fatigue, and there is a blind spot in the microscope viewing area and the projection light source in the lateral position.


## 2.3. Postoperative Care

Surgical site infections are infections that occur in the surgical incision, organ, or cavity during surgery. We counted the cases in a hospital in this city and obtained the statistical data with the consent of the patients. SSI is a common postoperative complication belonging to third-party nosocomial infections, accounting for 14% to 16% of all nosocomial infections, and 35% to 40% of surgical nosocomial infections. Once SSI appears, it will prolong the patient’s hospital stay, increase the patient’s hospitalization costs, and even cause the patient’s death. At the same time, it will consume a lot of medical resources, seriously affect the quality of medical care, and bring a serious burden to patients and hospitals.Neurosurgery nurses’ SSI prevention measures knowledge scores are positively correlated with their behaviors, indicating that the higher the nurse’s prevention knowledge level, the greater the consistency of the clinical behavior of prevention and control [23, 24]. In the face of severe SSI, high incidence, managers should start with prevention, control, management, etc., pay attention to SSI prevention, strengthen nurse knowledge training, improve prevention knowledge, and at the same time, strengthen supervision and management to improve nurses’ compliance with SSI prevention and treatment measures. Prevent and control the occurrence of infection at the surgical site.In actual work, the factors that affect the nurses’ ability to implement surgical site infection prevention and control measures are not just the nurse’s personal cognition and weak awareness of prevention and control, the attention of the management department, the completeness of the facilities, the supervision and management system, and the training and education. Effectiveness, etc., will affect the nurses’ implementation of infection prevention and control measures at the surgical site to a certain extent. In addition to training nurses, this study also applied the Ebbinghaus forgetting curve theory to regularly assess the nurses’ awareness and implementation of infection prevention and control measures, to discover problems in actual work in a timely manner and carry out continuous quality improvement. At the same time, give full play to the supervision and guidance of the intervention team and head nurses on the prevention and control of infection at the surgical site of nurses, and promote the effective implementation of prevention and control measures for SSI infection by nurses. It can be seen that SSI infection prevention and control management involves overall systemic management. Through training, nurses’ awareness of surgical site infections can be improved, helping nurses to establish health beliefs, and allowing nurses to fundamentally realize the importance of surgical site infection prevention and control. It is necessary to change health behaviors and improve nurses’ compliance with surgical site infection prevention and control measures; in addition, it is necessary to provide necessary logistical support to ensure nurses’ access to infection prevention and control facilities; and at the same time enhance the management of surgical site infections pay attention to prevention and control work, strengthen the supervision and management of clinical sensory control work, and improve the compliance of nurses’ behaviors in the prevention and control of infection at the surgical site under systematic management.


## 2.4. Microscopic Optical Imaging

Microscopic optical imaging, also commonly referred to as “optical microscopic imaging,” or “optical microscopy,” refers to the ability to obtain magnification of a tiny sample by passing visible light through or reflected from a sample through one or more lenses using image technologies. The obtained image can be directly observed with the eyes through the eyepiece, or recorded with a photosensitive plate or a digital image detector such as CCD and CMOS, and can also be displayed and analyzed on a computer.Ordinary optical microscopy using bright-field illumination usually has three limitations: first, it can only image dark samples (transmission type) or strongly reflective samples (reflection type); second, the optical diffraction limit limits the highest resolution of this technique is about 200 nm; third, out-of-focus information reduces the image contrast. Fluorescence microscopy, based on the excitation and fluorescence emission of fluorescent molecules in the sample (exogenous or endogenous), can overcome the limitations of imaging transparent samples, but the problems of limited resolution and out-of-focus interference still require additional measures to be resolved.


## 3. Experiments and Results

### 3.1. Patient Information

After approval by the hospital ethics committee, 40 patients who underwent elective surgery in the Department of Neurosurgery of the First Hospital of the city were selected as the test subjects. The information needed for the patient study was collected without affecting the patient’s treatment.


#### 3.1.1. Inclusion Criteria

(1)
Those have no abnormal EEG before operation;(2)
Those have no history of serious heart, lung, liver, kidney, metabolic diseases, and epilepsy before surgery;(3)
Those have no history of allergy to sedative drugs before surgery


#### 3.1.2. Exclusion Criteria

(1)
Those have a history of mental illness, epilepsy or taking anti-epilepsy or psychoactive drugs before surgery;(2)
Those have experienced drowsiness, coma, and disturbance of consciousness before the operation, and cannot complete the examination with instructions;(3)
Patients with chronic hypoxia, anemia, etc. before surgery.The patients are grouped into experiments. The general information of the patients is shown in Table1:Table 1
Basic information of patients.
GenerallyMicroscopic imaging groupRoutine experimental groupGenderMale129Female118Age<60 years old1118>60 years old57Course of diseaseMedian course of disease (months)1512The results oft-test for general data of the two groups of patients are shown in Table 2:Table 2
Comparison of patient data between the two groups.
GroupAgeWeight (kg)Height (cm)ASA ratingMicroscopic imaging group43.3 ± 13.964.0 ± 10.7167.1 ± 7.18/12Routine experimental group43.5 ± 13.870.8 ± 12.0165.6 ± 6.77/13P0.9450.7410.0560.713


### 3.2. Comparison of Postoperative Patients

After 12 hours, the three groups were reexamined with head CT, calculated and compared the remaining rate of intraventricular hematoma, and the results are as follows: Among them, the remaining rate of hematoma in the routine operation group: 10 cases <20%, 16 cases of hematoma remaining between 20% and 50%, 4 cases remaining hematoma > 50%; microsurgery group: 4 cases <20%, 10 cases of hematoma remaining between 20% and 50%, 8 cases of hematoma remaining > 50%. The remaining rate of hematoma in the ventricular drainage group: 2 cases <20%, 12 cases of hematoma remaining at 2050%, and 24 cases of hematoma remaining >50%; after comparison, the difference is statistically significant, see Table3 for details.Table 3
Postoperative hematoma in each group.
<20%20%∼50%>50%Routine surgery group10 (33.3)16 (53.3)4 (13.3)Microsurgery group4 (18.2)10 (45.4)8 (36.4)Drainage group2 (5.3)12 (31.6)24 (63.2)χ219.875P0.001The grading of the ADL scale for six months after the operation is shown in Table4.Table 4
ADL scale grading for the first half of the year after surgery.
IIIIIIIVVRoutine surgery group14 (46.7)7 (23.3)5 (16.7)3 (10.0)1 (3.3)Microsurgery group9 (41.0)4 (18.2)4 (18.2)3 (13.6)2 (9.1)Drainage group8 (21.1)8 (21.1)10 (26.3)7 (18.3)5 (13.2)H24.884P<0.05Quantitative aspects: The PSMS score, IADL score, and the total ADL score of the neuroendoscopy group were lower than those of the puncture drainage group and the microsurgery group at half a year after surgery. The differences were statistically significant (P < 0.05), see Table 5 for details.Table 5
Comparison of ADL scale scores six months after surgery.
PSMSIADLTotal scoreRoutine surgery group6.57 ± 0.7317.23 ± 4.8323.80 ± 4.85Microsurgery group8.91 ± 1.6320.09 ± 3.9329.00 ± 4.76Drainage group12.61 ± 3.3325.08 ± 4.6437.68 ± 5.60F56.73825.96262.825P<0.001<0.001<0.001A common emergency and critical illness in neurosurgery, the clinical emergency of ventricular hemorrhage, is reflected in the rapid onset, rapid progress, severe disease, and high mortality and disability rate. To compare the difference between the method in this article and the traditional treatment method, we distinguished the effects of different groups of patients before and after treatment, as shown in Figure2 before treatment and as shown in Figure 3 after treatment.Figure 2
Different groups of patients before treatment. (a) Conventional treatment group. (b) Microscopic imaging group treatment effect.
(a)(b)Figure 3
Comparison of effects treatment. (a) Routine surgery quality effect. (b) Microscopic imaging group treatment effect.
(a)(b)After treatment, the method used in this article can effectively reduce intracranial hypertension and prevent the emergence of acute obstructive hydrocephalus. In traditional treatment methods, a large amount of blood accumulates in the ventricular system after ventricular hemorrhage due to various causes. The production of cerebrospinal fluid is basically not affected, but the circulation is blocked. Therefore, the cerebrospinal fluid gradually accumulates in the ventricular system over time, which further causes acute obstruction. The appearance of hydrocephalus causes the expansion of the ventricular system and increased intracranial pressure. Further development will oppress important tissue structures such as the brainstem, and in severe cases, it may even lead to the occurrence of cerebral herniation.To understand the role of nursing in the operation, we made relevant mentions of the two groups of nursing work and then compared their roles in the operation process, as shown in Figure4:Figure 4
Preoperative nursing work.The treatment effect of different groups after nursing work is shown in Figure5:Figure 5
Comparison of treatment effects of different groups.The average extubation time of the microscopic imaging group was (2.67 ± 0.55) days, and the average length of hospitalization was (14.20 ± 3.18) days; the average extubation time of the routine quality group was (4.36 ± 0.73) days, and the average length of hospitalization was (26.59 ± 4.98) days; In the drainage group, the average extubation time was (6.05 ± 1.45) days, and the average length of hospital stay was (29.58 ± 7.54) days; After statistical analysis, the difference was statistically significant, as shown in Table6:Table 6
Average drainage tube extubation time and average length of hospital stay.
Extubation time of drainage tubeLength of hospital stayRoutine surgery group2.67 ± 0.5514.20 ± 3.18Microsurgery group4.36 ± 0.7326.59 ± 4.98Drainage group6.05 ± 1.4529.58 ± 7.54F85.90162.737P<0.001<0.001The experimental results of the patient group in the state and the patient group in the intraoperative anesthesia state are shown in Table7:Table 7
Experimental results of patients under anesthesia.
Explosive suppressionNo explosive suppressionRoutine surgery group419Microsurgery group146Drainage group87P0.004—The hemodynamic indexes of the patient group in the state and the patient group in the intraoperative anesthesia state are shown in Table8:Table 8
Hemodynamic indicators.
GroupTimeMAPHRSpO2Routine surgery groupT191.7 ± 12.375.8 ± 13.998.9 ± 1.3T289.6 ± 11.377.9 ± 16.398.6 ± 1.9T389.6 ± 12.481.3 ± 14.997.9 ± 2.6T488.2 ± 12.479.1 ± 15.098.1 ± 1.9Drainage groupT185.8 ± 8.469.1 ± 9.299.8 ± 0.8T282.9 ± 10.569.8 ± 8.999.8 ± 0.6T383.5 ± 10.871.2 ± 7.899.8 ± 0.6T481.6 ± 9.970.7 ± 8.199.7 ± 0.6For the effect of postoperative care, complications are an important sign. In the microsurgery group, there was 1 case of intracranial infection, 0 cases of rebleeding, and 3 cases of hydrocephalus, accounting for 13.3% of the group; group: traditional operation group of 3 cases of intracranial infection, 3 cases of complicated hydrocephalus, 2 patients developed silent aphasia after operation, accounting for 36.4% of the group. Drainage group: 7 cases of intracranial infection, 3 cases of rebleeding, and 14 cases of complicated hydrocephalus, accounting for 63.2% of this group. After comparison, the difference was not statistically significant. The details are shown in Figure6:Figure 6
Patient complications.We have performed statistics on the imaging after the occurrence of complications in different groups of patients, and the differences in the treatment effects of different groups can be seen very intuitively through the images, as shown in Figure7:Figure 7
Imaging of complications in different groups. (a) Complications in the routine group. (b) Complication imaging in drainage group. (c) Complication imaging in the microscopy group.
(a)(b)(c)In this study, the preventive measures of neurosurgery nurses have been done.In the nursing process of neurosurgery patients, the risk factors for nursing care are mainly related to factors such as accidental extubation, pressure ulcers, aspiration, and burns. In the daily management process of many patients, due to the neglect of relevant management elements, there are loopholes in patient management, thus causing patient management risks. For example, the factors that cause accidental extubation of most patients in nursing are mainly related to the patient’s own condition and the occurrence of agitation. In view of the relatively common risks in neurosurgery patient management, preventive measures should be taken in the implementation of patient management. First, after the patient is admitted to the hospital, it is necessary to conduct a timely assessment of the patient, analyze the patient’s own condition, and the severity of the patient’s illness, to classify the patient. Secondly, in the process of patient management, it is necessary to combine health education with patient management for the first time, so that patients can clearly understand the significance of health education in patient management and actively learn health education. Finally, in the process of nursing staff management, the vocational skills training of nursing staff should be strengthened to ensure that scientific nursing can be carried out for patient care during vocational skills training. And it can be combined with the implementation requirements of patient care work to exclude risk factors in the process of patient care, to improve the quality of patient care.


## 3.1. Patient Information

After approval by the hospital ethics committee, 40 patients who underwent elective surgery in the Department of Neurosurgery of the First Hospital of the city were selected as the test subjects. The information needed for the patient study was collected without affecting the patient’s treatment.


### 3.1.1. Inclusion Criteria

(1)
Those have no abnormal EEG before operation;(2)
Those have no history of serious heart, lung, liver, kidney, metabolic diseases, and epilepsy before surgery;(3)
Those have no history of allergy to sedative drugs before surgery


### 3.1.2. Exclusion Criteria

(1)
Those have a history of mental illness, epilepsy or taking anti-epilepsy or psychoactive drugs before surgery;(2)
Those have experienced drowsiness, coma, and disturbance of consciousness before the operation, and cannot complete the examination with instructions;(3)
Patients with chronic hypoxia, anemia, etc. before surgery.The patients are grouped into experiments. The general information of the patients is shown in Table1:Table 1
Basic information of patients.
GenerallyMicroscopic imaging groupRoutine experimental groupGenderMale129Female118Age<60 years old1118>60 years old57Course of diseaseMedian course of disease (months)1512The results oft-test for general data of the two groups of patients are shown in Table 2:Table 2
Comparison of patient data between the two groups.
GroupAgeWeight (kg)Height (cm)ASA ratingMicroscopic imaging group43.3 ± 13.964.0 ± 10.7167.1 ± 7.18/12Routine experimental group43.5 ± 13.870.8 ± 12.0165.6 ± 6.77/13P0.9450.7410.0560.713


## 3.1.1. Inclusion Criteria

(1)
Those have no abnormal EEG before operation;(2)
Those have no history of serious heart, lung, liver, kidney, metabolic diseases, and epilepsy before surgery;(3)
Those have no history of allergy to sedative drugs before surgery


## 3.1.2. Exclusion Criteria

(1)
Those have a history of mental illness, epilepsy or taking anti-epilepsy or psychoactive drugs before surgery;(2)
Those have experienced drowsiness, coma, and disturbance of consciousness before the operation, and cannot complete the examination with instructions;(3)
Patients with chronic hypoxia, anemia, etc. before surgery.The patients are grouped into experiments. The general information of the patients is shown in Table1:Table 1
Basic information of patients.
GenerallyMicroscopic imaging groupRoutine experimental groupGenderMale129Female118Age<60 years old1118>60 years old57Course of diseaseMedian course of disease (months)1512The results oft-test for general data of the two groups of patients are shown in Table 2:Table 2
Comparison of patient data between the two groups.
GroupAgeWeight (kg)Height (cm)ASA ratingMicroscopic imaging group43.3 ± 13.964.0 ± 10.7167.1 ± 7.18/12Routine experimental group43.5 ± 13.870.8 ± 12.0165.6 ± 6.77/13P0.9450.7410.0560.713


## 3.2. Comparison of Postoperative Patients

After 12 hours, the three groups were reexamined with head CT, calculated and compared the remaining rate of intraventricular hematoma, and the results are as follows: Among them, the remaining rate of hematoma in the routine operation group: 10 cases <20%, 16 cases of hematoma remaining between 20% and 50%, 4 cases remaining hematoma > 50%; microsurgery group: 4 cases <20%, 10 cases of hematoma remaining between 20% and 50%, 8 cases of hematoma remaining > 50%. The remaining rate of hematoma in the ventricular drainage group: 2 cases <20%, 12 cases of hematoma remaining at 2050%, and 24 cases of hematoma remaining >50%; after comparison, the difference is statistically significant, see Table3 for details.Table 3
Postoperative hematoma in each group.
<20%20%∼50%>50%Routine surgery group10 (33.3)16 (53.3)4 (13.3)Microsurgery group4 (18.2)10 (45.4)8 (36.4)Drainage group2 (5.3)12 (31.6)24 (63.2)χ219.875P0.001The grading of the ADL scale for six months after the operation is shown in Table4.Table 4
ADL scale grading for the first half of the year after surgery.
IIIIIIIVVRoutine surgery group14 (46.7)7 (23.3)5 (16.7)3 (10.0)1 (3.3)Microsurgery group9 (41.0)4 (18.2)4 (18.2)3 (13.6)2 (9.1)Drainage group8 (21.1)8 (21.1)10 (26.3)7 (18.3)5 (13.2)H24.884P<0.05Quantitative aspects: The PSMS score, IADL score, and the total ADL score of the neuroendoscopy group were lower than those of the puncture drainage group and the microsurgery group at half a year after surgery. The differences were statistically significant (P < 0.05), see Table 5 for details.Table 5
Comparison of ADL scale scores six months after surgery.
PSMSIADLTotal scoreRoutine surgery group6.57 ± 0.7317.23 ± 4.8323.80 ± 4.85Microsurgery group8.91 ± 1.6320.09 ± 3.9329.00 ± 4.76Drainage group12.61 ± 3.3325.08 ± 4.6437.68 ± 5.60F56.73825.96262.825P<0.001<0.001<0.001A common emergency and critical illness in neurosurgery, the clinical emergency of ventricular hemorrhage, is reflected in the rapid onset, rapid progress, severe disease, and high mortality and disability rate. To compare the difference between the method in this article and the traditional treatment method, we distinguished the effects of different groups of patients before and after treatment, as shown in Figure2 before treatment and as shown in Figure 3 after treatment.Figure 2
Different groups of patients before treatment. (a) Conventional treatment group. (b) Microscopic imaging group treatment effect.
(a)(b)Figure 3
Comparison of effects treatment. (a) Routine surgery quality effect. (b) Microscopic imaging group treatment effect.
(a)(b)After treatment, the method used in this article can effectively reduce intracranial hypertension and prevent the emergence of acute obstructive hydrocephalus. In traditional treatment methods, a large amount of blood accumulates in the ventricular system after ventricular hemorrhage due to various causes. The production of cerebrospinal fluid is basically not affected, but the circulation is blocked. Therefore, the cerebrospinal fluid gradually accumulates in the ventricular system over time, which further causes acute obstruction. The appearance of hydrocephalus causes the expansion of the ventricular system and increased intracranial pressure. Further development will oppress important tissue structures such as the brainstem, and in severe cases, it may even lead to the occurrence of cerebral herniation.To understand the role of nursing in the operation, we made relevant mentions of the two groups of nursing work and then compared their roles in the operation process, as shown in Figure4:Figure 4
Preoperative nursing work.The treatment effect of different groups after nursing work is shown in Figure5:Figure 5
Comparison of treatment effects of different groups.The average extubation time of the microscopic imaging group was (2.67 ± 0.55) days, and the average length of hospitalization was (14.20 ± 3.18) days; the average extubation time of the routine quality group was (4.36 ± 0.73) days, and the average length of hospitalization was (26.59 ± 4.98) days; In the drainage group, the average extubation time was (6.05 ± 1.45) days, and the average length of hospital stay was (29.58 ± 7.54) days; After statistical analysis, the difference was statistically significant, as shown in Table6:Table 6
Average drainage tube extubation time and average length of hospital stay.
Extubation time of drainage tubeLength of hospital stayRoutine surgery group2.67 ± 0.5514.20 ± 3.18Microsurgery group4.36 ± 0.7326.59 ± 4.98Drainage group6.05 ± 1.4529.58 ± 7.54F85.90162.737P<0.001<0.001The experimental results of the patient group in the state and the patient group in the intraoperative anesthesia state are shown in Table7:Table 7
Experimental results of patients under anesthesia.
Explosive suppressionNo explosive suppressionRoutine surgery group419Microsurgery group146Drainage group87P0.004—The hemodynamic indexes of the patient group in the state and the patient group in the intraoperative anesthesia state are shown in Table8:Table 8
Hemodynamic indicators.
GroupTimeMAPHRSpO2Routine surgery groupT191.7 ± 12.375.8 ± 13.998.9 ± 1.3T289.6 ± 11.377.9 ± 16.398.6 ± 1.9T389.6 ± 12.481.3 ± 14.997.9 ± 2.6T488.2 ± 12.479.1 ± 15.098.1 ± 1.9Drainage groupT185.8 ± 8.469.1 ± 9.299.8 ± 0.8T282.9 ± 10.569.8 ± 8.999.8 ± 0.6T383.5 ± 10.871.2 ± 7.899.8 ± 0.6T481.6 ± 9.970.7 ± 8.199.7 ± 0.6For the effect of postoperative care, complications are an important sign. In the microsurgery group, there was 1 case of intracranial infection, 0 cases of rebleeding, and 3 cases of hydrocephalus, accounting for 13.3% of the group; group: traditional operation group of 3 cases of intracranial infection, 3 cases of complicated hydrocephalus, 2 patients developed silent aphasia after operation, accounting for 36.4% of the group. Drainage group: 7 cases of intracranial infection, 3 cases of rebleeding, and 14 cases of complicated hydrocephalus, accounting for 63.2% of this group. After comparison, the difference was not statistically significant. The details are shown in Figure6:Figure 6
Patient complications.We have performed statistics on the imaging after the occurrence of complications in different groups of patients, and the differences in the treatment effects of different groups can be seen very intuitively through the images, as shown in Figure7:Figure 7
Imaging of complications in different groups. (a) Complications in the routine group. (b) Complication imaging in drainage group. (c) Complication imaging in the microscopy group.
(a)(b)(c)In this study, the preventive measures of neurosurgery nurses have been done.In the nursing process of neurosurgery patients, the risk factors for nursing care are mainly related to factors such as accidental extubation, pressure ulcers, aspiration, and burns. In the daily management process of many patients, due to the neglect of relevant management elements, there are loopholes in patient management, thus causing patient management risks. For example, the factors that cause accidental extubation of most patients in nursing are mainly related to the patient’s own condition and the occurrence of agitation. In view of the relatively common risks in neurosurgery patient management, preventive measures should be taken in the implementation of patient management. First, after the patient is admitted to the hospital, it is necessary to conduct a timely assessment of the patient, analyze the patient’s own condition, and the severity of the patient’s illness, to classify the patient. Secondly, in the process of patient management, it is necessary to combine health education with patient management for the first time, so that patients can clearly understand the significance of health education in patient management and actively learn health education. Finally, in the process of nursing staff management, the vocational skills training of nursing staff should be strengthened to ensure that scientific nursing can be carried out for patient care during vocational skills training. And it can be combined with the implementation requirements of patient care work to exclude risk factors in the process of patient care, to improve the quality of patient care.


## 4. Discussion

### 4.1. Remaining Amount of Hematoma 12 Hours after Surgery

The microscopic imaging group had the least remaining hematoma, followed by the drainage surgery group. It is sufficient to say that the obvious microimaging has a high hematoma clearance rate in intraventricular hemorrhage, and the residual hematoma in postoperative reexamination is less than that of traditional ventricular drainage. The reason for this result may be that the entire operation in the treatment of intraventricular hemorrhage by microscopic imaging is performed under direct vision, which can remove most of the hematoma seen in the field of view. Other fields of view are not as clear as endoscopy, which can remove the hematoma. The effect is not as good as microscopic imaging, and the extraventricular drainage has the most hematoma remaining among the three. The main reason is that the ventricle puncture increases the risk of rebleeding under blindness. On the other hand, it mainly relies on the drainage tube to remove the hematoma. However, the drainage tube often has poor position or clogged the tube head, which limits its drainage effect. Even if urokinase is injected, it cannot be compared with the effect of microscopic imaging in removing hematoma.


### 4.2. Postoperative Complications

Although surgical skills are becoming increasingly proficient, and there have been great advances in machinery, surgical operations have made considerable progress and development, but due to the difficulty of some operations, high risk, and some underlying diseases of the patients themselves, some surgical operations are still more or more frequent. There are few complications. In this study, 1 case of microscopic imaging intracranial infection, 0 cases of rebleeding, 3 cases complicated with hydrocephalus, all complications accounted for 13.3% of the group; 3 cases of intracranial infection in the drainage group, 3 cases complicated with hydrocephalus cases, 2 patients developed silent aphasia after surgery, accounting for 36.4% of this group. In the conventional treatment group, there were 7 cases of intracranial infection, 3 cases of rebleeding, and 14 cases of complicated hydrocephalus, accounting for 63.2% of the group. It may be because the occurrence of postoperative complications is affected by many factors. From the perspective of the operation itself, it is affected by the surgical instruments and the level of operation of the surgeon. At the same time, postoperative treatment and nursing, the basic physical condition of the patient, and the primary disease can all be affected. Complications occurred, but overall, neuroendoscopic complications were the lowest compared to the other two groups. It is worth mentioning that the microsurgery in this article, the removal of intraventricular hematoma through the longitudinal fissure corpus callosum approach, can also remove the hematoma better under the illumination system of the microscope. Compared with neuroendoscopy and ventricular drainage, this surgical procedure is more traumatic, and it also stretches the brain tissue more severely. When the cingulate gyrus and other structures are damaged, there will be neurological disorders such as silence, which leads to its complications compared to the intraneural There are more in the mirror group, but lower than the ventricular drainage group because the complications after ventricular hemorrhage such as intracranial infection and hydrocephalus are mainly determined by whether it can actively and effectively remove the intraventricular hematoma, relieve the space-occupying effect, and recover Cerebrospinal fluid circulation, microsurgery can do this, but ventricular drainage cannot.


### 4.3. Length of Hospital Stay

Voluntary patients in a hospital in this city were treated with microscopic imaging technology, and the treatment time was recorded.In terms of length of hospital stay, the average length of stay in the microscopic imaging group (14.20 ± 3.18 days) was shorter than the average length of stay in the drainage group (26.59 ± 4.98 days) and the average length of stay in the conventional treatment group (29.58 ± 7.54 days). After statistical analysis, the results show that the difference between the microscopic imaging group and the conventional treatment group and the drainage group is statistically significant, indicating that compared with the conventional treatment group and drainage, the surgical method of microscopic imaging can reduce the length of hospital stay, thereby reducing the economic burden of patients to improve the quality of life of patients and save medical resources. However, the results show that the difference between the conventional treatment group and the drainage group is not statistically significant, indicating that the length of hospital stay between the conventional treatment group and the drainage operation is basically the same. The reason may be that conventional surgery requires a relatively long recovery period due to severe trauma. Drainage is due to poor surgical results, and both of them have relatively more complications, which prolongs the hospital stay.


## 4.1. Remaining Amount of Hematoma 12 Hours after Surgery

The microscopic imaging group had the least remaining hematoma, followed by the drainage surgery group. It is sufficient to say that the obvious microimaging has a high hematoma clearance rate in intraventricular hemorrhage, and the residual hematoma in postoperative reexamination is less than that of traditional ventricular drainage. The reason for this result may be that the entire operation in the treatment of intraventricular hemorrhage by microscopic imaging is performed under direct vision, which can remove most of the hematoma seen in the field of view. Other fields of view are not as clear as endoscopy, which can remove the hematoma. The effect is not as good as microscopic imaging, and the extraventricular drainage has the most hematoma remaining among the three. The main reason is that the ventricle puncture increases the risk of rebleeding under blindness. On the other hand, it mainly relies on the drainage tube to remove the hematoma. However, the drainage tube often has poor position or clogged the tube head, which limits its drainage effect. Even if urokinase is injected, it cannot be compared with the effect of microscopic imaging in removing hematoma.


## 4.2. Postoperative Complications

Although surgical skills are becoming increasingly proficient, and there have been great advances in machinery, surgical operations have made considerable progress and development, but due to the difficulty of some operations, high risk, and some underlying diseases of the patients themselves, some surgical operations are still more or more frequent. There are few complications. In this study, 1 case of microscopic imaging intracranial infection, 0 cases of rebleeding, 3 cases complicated with hydrocephalus, all complications accounted for 13.3% of the group; 3 cases of intracranial infection in the drainage group, 3 cases complicated with hydrocephalus cases, 2 patients developed silent aphasia after surgery, accounting for 36.4% of this group. In the conventional treatment group, there were 7 cases of intracranial infection, 3 cases of rebleeding, and 14 cases of complicated hydrocephalus, accounting for 63.2% of the group. It may be because the occurrence of postoperative complications is affected by many factors. From the perspective of the operation itself, it is affected by the surgical instruments and the level of operation of the surgeon. At the same time, postoperative treatment and nursing, the basic physical condition of the patient, and the primary disease can all be affected. Complications occurred, but overall, neuroendoscopic complications were the lowest compared to the other two groups. It is worth mentioning that the microsurgery in this article, the removal of intraventricular hematoma through the longitudinal fissure corpus callosum approach, can also remove the hematoma better under the illumination system of the microscope. Compared with neuroendoscopy and ventricular drainage, this surgical procedure is more traumatic, and it also stretches the brain tissue more severely. When the cingulate gyrus and other structures are damaged, there will be neurological disorders such as silence, which leads to its complications compared to the intraneural There are more in the mirror group, but lower than the ventricular drainage group because the complications after ventricular hemorrhage such as intracranial infection and hydrocephalus are mainly determined by whether it can actively and effectively remove the intraventricular hematoma, relieve the space-occupying effect, and recover Cerebrospinal fluid circulation, microsurgery can do this, but ventricular drainage cannot.


## 4.3. Length of Hospital Stay

Voluntary patients in a hospital in this city were treated with microscopic imaging technology, and the treatment time was recorded.In terms of length of hospital stay, the average length of stay in the microscopic imaging group (14.20 ± 3.18 days) was shorter than the average length of stay in the drainage group (26.59 ± 4.98 days) and the average length of stay in the conventional treatment group (29.58 ± 7.54 days). After statistical analysis, the results show that the difference between the microscopic imaging group and the conventional treatment group and the drainage group is statistically significant, indicating that compared with the conventional treatment group and drainage, the surgical method of microscopic imaging can reduce the length of hospital stay, thereby reducing the economic burden of patients to improve the quality of life of patients and save medical resources. However, the results show that the difference between the conventional treatment group and the drainage group is not statistically significant, indicating that the length of hospital stay between the conventional treatment group and the drainage operation is basically the same. The reason may be that conventional surgery requires a relatively long recovery period due to severe trauma. Drainage is due to poor surgical results, and both of them have relatively more complications, which prolongs the hospital stay.


## 5. Conclusion

Neurosurgery has a higher rate of death and disability, and rapid diagnosis and correct treatment will determine the prognosis of the patient. Early diagnosis relies on cranial CT, and DSA is still the gold standard in excluding secondary ventricular hemorrhage such as aneurysm rupture, and the use of MRA and CTA is increasing. Traditional ventricle puncture combined with intraventricular fibrinolysis has achieved certain results, but still cannot achieve satisfactory results. At the same time, some side effects of fibrinolysis also limit its clinical application. It is necessary to develop a new type of fibrinolysis without side effects. The efficacy of deep learning and microscopic imaging in neurosurgical treatment is definite, but it has not been widely used in primary hospitals in China, and the requirements for surgeons are high, so it is still far popular. The research in this article has provided some help for the popularization of deep learning and microscopic imaging in neurosurgery treatment, but there are some shortcomings. The length of the study cycle is related to the case quality of the neurosurgical care studied. In terms of research samples, due to the limitations of research time and ability, the research in this article is limited to this city Some patients in the First Hospital may have some cases without statistics, which will have a certain impact on the results of the research. In future research, we will increase the research sample and try to avoid research errors.



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*Source: 5719897-2022-04-18.xml*