RETRACTED

This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:

(1)Discrepancies in scope(2)Discrepancies in the description of the research reported(3)Discrepancies between the availability of data and the research described(4)Inappropriate citations(5)Incoherent, meaningless and/or irrelevant content included in the article(6)Peer-review manipulation

The presence of these indicators undermines our confidence in the integrity of the article’s content and we cannot, therefore, vouch for its reliability. Please note that this notice is intended solely to alert readers that the content of this article is unreliable. We have not investigated whether authors were aware of or involved in the systematic manipulation of the publication process.

Wiley and Hindawi regrets that the usual quality checks did not identify these issues before publication and have since put additional measures in place to safeguard research integrity.

We wish to credit our own Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation.

The corresponding author, as the representative of all authors, has been given the opportunity to register their agreement or disagreement to this retraction. We have kept a record of any response received.

Journal of Healthcare Engineering
Volume 2021 (2021), Article ID 5571862, 11 pages
https://doi.org/10.1155/2021/5571862

Research Article

Augmented Reality Research of Measuring X-Ray Dental Film Alveolar Bone Based on Computer Image Analysis System

Chunfeng Wang,¹ Caigang Peng,¹ Yepo Hou,¹ and Minmin Chen²

¹Department of Stomatology, Hunan University of Medicine, Huaihua 418000, Hunan, China
²Department of Endodontic, Xiangya Stomatological Hospital Xiangya School of Stomatology, Hunan Key Laboratory of Oral Health Research, Central South University, Changsha 410008, Hunan, China

Correspondence should be addressed to Minmin Chen; chenminmin@stu.cpu.edu.cn

Received 6 January 2021; Revised 15 February 2021; Accepted 1 March 2021; Published 18 March 2021

Academic Editor: Zhihan Lv

Copyright © 2021 Chunfeng Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

The important application of computer imaging technology in the medical field is a necessary auxiliary method for clinical diagnosis and treatment. At present, many people are affected by various factors and have various problems caused by the dental cellular bone. Traditional treatment methods are complex and long, which can cause damage to body tissues. Based on this problem, this paper takes the augmented reality measurement of X-ray dental film as the research object. Based on the in-depth measurement algorithm of the computer image analysis system, two three-dimensional reconstruction methods based on the center of gravity and the matching of the front and side positions are proposed. These two methods only need two X-rays of the front and side of the dental film, the three-dimensional parameters are obtained through calculation and analysis of each spine in the X-ray film, and these parameters are used to fit the dental alveolar bone model. The experimental results prove that the computer-based image analysis system has a great effect on the measurement of X-ray dental film alveolar bone. The positive correlation coefficient reaches 0.87. Compared with the cerebral infarction caused by other methods, the proportion of people with dental film alveolar bone injury is about 15%; after treatment, the functional recovery rate reaches more than 80%. Studies have found that there is a great difference in the age of the population that needs to be treated for dental slices and alveolar bone. The grade of patients is generally under 20 and over 60. This shows that the measurement of X-ray dental film alveolar bone based on computer image analysis system can play an important role in protecting people's oral health.

1. Introduction

With continued progress and development of computer technology, virtual reality technology [1], graphics technology, and image processing technology, computer imaging technology has emerged. In the field of image processing, computer image analysis technology is one of the current research points of access and is widely used in many areas such as geological research, finite data analysis, and medicine. Computer imaging technology has changed the traditional way of displaying things and improved people’s ability of analyzing and understanding complex things. It has important research significance. The medical field is the earliest application of computer image analysis [2]. With the continuous advancement of medical imaging technology, the three-dimensional visualization of medical images plays an increasingly important role in measuring the augmented reality of X-ray dental film alveolar bone and has gradually become an important auxiliary method for medical diagnosis and treatment. Through the computer’s image analysis system, the doctor can more sensitively observe the shape, size, position, and deformation of the cellular bone, which is suitable for the doctor to make an accurate, scientific, and logical diagnosis and treatment, which can improve the efficiency of work [3].

Traditional medical treatment methods are relatively intuitionistic and are closely related to the doctor’s experience. Insufficient experience can easily lead to misdiagnosis and serious life threatening [4]. The family members of patients have difficulty in understanding, which increases the difficulty of communication between doctors and patients, and to a certain extent restricts the effective diagnosis and treatment of diseases. People are eagerly seeking a technology that can visually display the effect of lesions to assist medical diagnosis. The rapid development of computer technology, computer vision, and computer graphics has enabled the medical image computer image analysis system to move from vision to reality [5, 6].

Ying Liang believes that computer imaging plays an extremely important role in hospital internships, which can truly improve the comprehensive medical skills of doctors in all aspects. Because the traditional medical teaching method is passed on by word of mouth, all aspects of knowledge are extremely complicated, and medical students are more familiar with it. Difficulties lead to high costs, and computer images can be displayed in a very three-dimensional and intuitive way when teaching, reducing the difficulty of learning, improving the learning ability of medical students, and comprehensively improving the skills of doctors [7]; Zhao Ruohan believes that computers have achieved good application effects in various fields and they can also improve the basis for diagnosis and treatment in medical images. He designed a medical image-assisted analysis system based on computer images [8]. Through relevant examinations of patients, comparing the results of other diagnosis and treatment methods, it is concluded that the results of the analysis system are correct. He believes that the image analysis system can provide a good assistance to doctors in diagnosis and treatment and provide a new idea for teaching [9]; Guo Anmin uses computer X-ray photography to count the internal dental films of more than one hundred patients and clearly obtains the situation in the population. For different angles and exposures, the X-ray imaging time is different, and the images can be clearly understood after imaging. Compared with the traditional diagnosis and treatment methods, it reduces the time and money required for the patient’s teeth and is suitable for use in various hospitals [10]. These studies have a certain reference basis for this article, but, due to insufficient samples of these studies, too much emphasis on theories, and unreasonable practical programs, the research has too many variables and the conclusions are unconvincing.

This research provides a new idea for the clinical treatment of alveolar bone defects. Based on the computer image analysis system to measure the alveolar bone of the X-ray dental film, it can fully demonstrate the clinical alveolar bone resorption and reduce the alveolar bone caused by the doctor’s empirical simulation of the three-dimensional structure diagnosis of defect. Using the computer image analysis system, the alveolar bone repair bracket that matches the shape of the patient's defect area and the pore structure is adjustable and can be quickly formed in a short time to achieve personalized customization [11]. The material used in the study is a natural material that is similar in composition to alveolar bone and biodegradable and promotes bone regeneration without the need for secondary surgery to remove it, reducing the risk of secondary infections in patients. In addition, the design of a bone defect scaffold with a matching shape and good structural strength can not only stably increase the accumulation height of the material for promoting bone regeneration in the vertical direction [12], but also solve the problem that the material cannot accumulate in the horizontal alveolar bone resorption and realize the effective filling of the bone defect to promote the alveolar bone regeneration.

2. Computer Image Analysis System Measures the Alveolar Bone Method of X-Ray Dental Film

2.1. Computer Imaging

In computer imaging technology, linear filtering and nonlinear filtering are used. Linear filtering methods include high and low gradient filtering and medium sector filtering [6]. The principle of this method is simple and easy to apply, but the edges or details of the image will be blurred during processing [13]. Commonly used nonlinear filtering methods are median filtering, wavelet transform filtering, and filtering based on diffusion equation. Median filtering has a good suppression effect on impulse noise and can better retain the edge information of the image, but it is a window-based filtering method that requires all pixels to be sorted, so it is time-consuming and the algorithm execution efficiency is low [14]. Wavelet transform filtering is to first transform the original image into the wavelet domain, then discard signals of certain scales, and finally makes thorough inverse transform to remove image noise. Among them, the choice of threshold, wavelet basis, and wavelet decomposition scale will directly affect the filtering effect of the image [15]. For images, it is difficult to choose an appropriate value. Filtering based on the diffusion equation takes the original image as the initial value and selects the effective diffusion coefficient according to the image feature information to control the diffusion behavior of the diffusion equation. This not only smoothens the image, but also preserves the feature information of the image [16].

In the formula, s is the two-dimensional coordinates of the pixels in the image, is the filtered image, is the average value of the pixels in the filter window, and is the adaptive filter coefficient obtained according to the local noise statistics. The filter is based on the isotropic diffusion equation, and the diffusion coefficients in the four directions are all .

Medical image analysis technology uses the lighting model to directly display volume data without the need to construct intermediate geometric prototypes and has greater accuracy. Since the three-dimensional data field is not fully fragmented, the amount of the calculation is large, the real-time performance is low, but completeness is better [17]. The image can be cut, displayed, and measured based on voxel values. In the implementation process, it is necessary to use an optical model to explain how the three-dimensional discrete data field generates, reflects, blocks, and scatters light [18], so the reasonable selection of the optical model is an important factor in determining the effect of image rendering [19].

When is approaching 0,

As the model’s lighting conditions change, the image will also change with the discovery

From this, we can see

When is approaching zero, use the following differential equation to illustrate the change of light intensity:

From each pixel of the screen image, a ray of light passes through the volume data field and intersects it, samples this ray according to a reasonable sampling interval to obtain a series of sampling points, and then performs processing based on the eight data points closest to this sampling point. The color value and opacity value of the sampling point are obtained by interpolation operation [20]; finally, the color value and opacity of all the sampling points on the light are accumulated in a front-to-back or back-to-front manner, and the emitted light can be obtained, the color of the screen pixels of the light [21].

2.2. X-Ray Dental Film Alveolar Bone Measurement

Alveolar bone, as the main structure of periodontal support tissue, plays an important role in the occurrence, development, and eruption of teeth and daily chewing. For dental implant patients, sufficient bone mass in the implant area is a prerequisite for implant placement and it is also a key factor to ensure successful implantation. Therefore, repairing of alveolar bone defects caused by severe periodontal disease has become an important part of dental implant surgery [22]. At present, the commonly used clinical methods to repair the defect of the alveolar bone in the implantation area include autologous bone transplantation, traction osteogenesis, and guided tissue regeneration. Autologous bone transplantation is the gold standard for alveolar bone regeneration, but there are problems such as limited sources and poor remodeling. At the same time, studies have shown that the complication rate of using combined mandibular grafts is 33%–70%. In the treatment of alveolar bone traction osteogenesis, complications caused by infection, accidental fractures, soft tissue problems, traction disk tilt, nerve damage, and postoperative recurrence reached 70% [23]. In addition, the placement of the intraoral retractor can cause discomfort to the patient. In guided tissue regeneration, the membrane material is used as a barrier to establish an environment for implant placement and new bone regeneration. However, accidental exposure of the membrane may cause secondary infections, leading to failure of alveolar bone regeneration.

At present, although many scholars have made various attempts to prepare personalized scaffolds according to the shape of the patient’s bone defect, they have not yet prepared a scaffold that can truly replicate the patient’s specific alveolar bone defect [24]. Measuring X-ray dental film alveolar bone is based on a computer image analysis system as a new digital molding technology, based on the principle of discrete/stacking, using biological materials or biological units (cells, proteins, etc.) as raw materials to construct a specific external shape and complex internal structure and functional three-dimensional structure. Compared with the traditional molding process, it uses computer-aided technology to design personalized implants and realizes the control of the pore structure and prepares a three-dimensional structure with almost the same macrostructure and defect structure, which provides for personalized defect alveolar bone repair new solution ideas and exploration directions [25].

The algorithm simulates the projection of light through a preset light source, determines the extent to which the light reaches each voxel, and is emitted or absorbed by the voxel. Then, calculate the meaning of the object that can be seen from the current observation point relative to the observation plane position and simulate the light projection from the observation point [26]. Determine the amount of emitted light, the color, and the amount of absorption from the scalar data by using a colormap containing alpha values.

Periodontal disease is one of the two main types of human oral diseases, with an incidence rate of 80–90% in the population. Mild periodontal disease will have symptoms such as bad mouth odor, bleeding gums, and inflammation. As the disease deepens, periodontal pockets will form chewing weakness or pain and even tooth displacement and loss will occur including a serious pathological change, namely, alveolar bone resorption [27]. Alveolar bone is the protruding part of the upper and lower jaw bone surrounding the tooth root. It plays an important role in the occurrence, development, and eruption of the tooth body and daily chewing. Alveolar bone is a part of human bone tissue, mainly composed of inorganic and organic components. Among them, calcium phosphate-based inorganic components accounted for 2/3 of the dry weight of the bone matrix and collagen fibers composed of cellulose, adhesives, and mucopolysaccharide-based organic components accounted for 1/3. Human bone tissue has the ability of self-repairing to a certain extent, but the repair of serious bone defects caused by trauma, congenital malformations, bone lesions, etc. requires surgical diagnosis and treatment [28].

Although the existing repair methods for alveolar bone defects such as autologous bone grafting, distraction osteogenesis, and membrane-guided tissue regeneration have been widely used clinically, they all have certain limitations. In recent years, with the expansion of tissue engineering in the field of stomatology, the construction of bone tissue engineering scaffolds is expected to become a new technology for the restoration of alveolar bone defects.

2.3. Augmented Reality Technology

The continuous development of augmented reality technology has deepened the connotation of virtual practice. In essence, the practice of augmented reality technology is still transforming things in objective reality into digital image symbols through photoelectric and other means and then expressing them in virtual space to come out [29]. But different from the usual virtual practice is that the practice in augmented reality makes the practice subject completely immersed in a virtual environment that can obtain real feelings, and, through sophisticated sensing technology, people can obtain vision, hearing, touch, and smell that are the same as reality. And the comprehensive feeling of kinesiology and then the motion tracker of each part of the human body will feedback the subject's feedback into the virtual space [30] and cyclically interact.

Augmented reality technology has brought about changes in human working life, entertainment and leisure, and practices. It has also brought about social problems such as excessive indulgence, health effects, violence, and value orientation and has spawned the evolution of philosophy. The traditional philosophy of technology tends to study the various social effects of technology, but at the same time the development of technology will also have an impact on the philosophy system. Affected by augmented reality technology, branches of philosophical fields such as artificial intelligence philosophy, virtual reality philosophy, and artificial life philosophy continue to appear [31, 32]. The philosophical issues generated by augmented reality technology involve many fields such as practice theory, media theory, epistemology, ethics, and sociology. Its unique technical characteristics and practical methods have produced different depths of effectiveness in multiple disciplines.

The characteristics of augmented reality technology make philosophers start to pay more attention to some traditional philosophical theories, such as the study of ontology and thinking about the nature of the world. Some brand-new philosophical ideas have emerged at the historic moment. Aiming at the technical characteristics of augmented reality that can construct a brand-new virtual environment, some scholars have put forward the idea that “computing” is the essence of the world [33]. For example, the American philosopher Steinhart proposed digital metaphysics; Zhai Zhenming, a philosophy professor at Sun Yat-sen University in my country, proposed a new ethics “world-making ethics” and so on. Facing the continuous innovation of technology, philosophical thinking cannot stop but should lead to and correct the new technology [34].

Since the advent of the Internet, virtual practice and augmented reality technology have attracted much attention [35]. Through virtual practice and augmented reality technology, mankind has broken through material production practice, social practice, and scientific practice, expanded the scope of practice, increased the objects of knowledge, and improved the practical ability. The development of augmented reality technology has added new vitality to virtual practice. If virtual practice is a practice that creates possibilities, then augmented reality practice is a practice that transforms any possibility into reality. In the virtual environment, any condition becomes controllable. Human beings are no longer restricted by physiological, natural, and social factors and get rid of the shackles of material conditions. The digital symbols with bits as the unit abstract the original concrete things. In reality, the function of things is separated from the material carrier of the thing, and its function can be used alone anytime and anywhere without being restricted by the material carrier. This is no exception for humans. Augmented reality pulls human senses and consciousness out of the body, realizing truly beyond the limits of time and space.

Ian White, the responsible editor of the manufacturing section of the “Engineering” website once said: “Factory planning, automation, assembly, maintenance, and training can all benefit from augmented reality.” As technology continues to advance, augmented reality has become a practical tool for the manufacturing industry. Goldman Sachs Global Investment predicted in a report that, by 2025, the revenue of augmented reality technology in the industrial sector will reach approximately US$4.7 billion. This is undoubtedly an important contribution to advanced reality technology in the industrial sector. The use value is the best confirmation. In addition to limiting costs and completing construction, the increased reality can also improve the impact of industrial planning and the effectiveness of training skills. Increased reality has become the most likely one to bring about the fourth industrial revolution. One of the most advanced technologies is that the age of industry 4.0 came to us [36].

3. Computer Image Analysis System to Measure the Alveolar Bone Experiment of X-Ray Dental Film

3.1. Research Objects

Taking patients with dental film alveolar bone problems in a city hospital as the experimental subjects, collect their clinical data (gender, age at diagnosis or diagnosis, clinical symptoms and signs at admission, auxiliary examinations, etc.) and follow up consultations and outpatient visits. Perform retrospective analysis on the collected data and draw conclusions. The process is shown in Figure 1.

Figure 1: Experimental inspection items.

3.2. Establish a Model Evaluation Index System

Definite conclusions can be drawn through actual observation of objects. Generally speaking, the evaluation index system includes three levels of evaluation indexes: they are the relationship between gradual decomposition and refinement. Among them, the first-level evaluation indicators and the second-level evaluation indicators are relatively abstract and cannot be used as a direct basis for evaluation. The third-level evaluation indicators should be specific, measurable, and behavior-oriented and can be used as a direct basis for teaching evaluation.

Full methods of quantitative and qualitative analysis: quantitative analysis is the analysis of the data of the problem, using the intuition and clear substance of mathematics to reflect the existence of the problem. The quality of the collection, reading, and organisation of relevant domestic and foreign research libraries systematically summarise the relevant theoretical results. The evaluation criteria of green supply chain performance are complex and diverse [37], including not only financial standards but also other nonfinancial standards. Some standards cannot be directly analyzed in a quantitative way but can only be evaluated by qualitative analysis. Green supply system the performance evaluation standard system of the company is constructed using a model that combines quantitative and qualitative analysis methods [38]. At the same time, it provides formulas for standard calculation and evaluation standards.

3.3. Determine the Evaluation Weight

The index weight is a numerical index indicating the importance and function of the index. In the indicator system of the evaluation plan, the weight of each indicator is different. Even if the indicator level is the same, the weight is different. Index weight is also called weight and is usually represented by a. It is a number greater than zero but less than 1, and the sum of the weights of all first-level indicators must be equal to 1; that is, satisfy conditions 0 < a < 1 and ∑a − 1.

3.4. Statistics

All data analysis in this article uses SPSS19.0, statistical test uses two-sided test, significance is defined as 0.05, and is considered significant. The statistical results are displayed as mean ± standard deviation (x ± SD). When the test data follows a normal distribution, the double T test is used for comparison within the group, and the independent sample T test is used for comparison between the groups. If the regular distribution is insufficient, two independent samples and two related samples will be used for inspection. The calculation formula is as follows:

4. Computer Image Analysis System to Measure X-Ray Dental Film Alveolar Bone Experiment

4.1. Distribution of Patients

We conducted a survey on patients in 6 plus dental hospitals in this city. Through on-site interviews with doctors and access to relevant diagnostic data, we conducted relevant statistics on patients. In order to facilitate comparison, we grouped patients by age and gender to make the data more clearly; the specific statistical results are shown in Table 1.

Table 1: Patient distribution.

0–12
13–24
25–36
37–46
47–60
Over 60
Male
Female

First hospital
28
24
26
14
17
27
76
60

Second hospital
33
29
23
21
16
31
94
59

Third hospital
38
25
16
17
13
28
83
54

Fourth hospital
35
24
16
14
11
25
69
56

Fifth hospital
27
29
17
15
13
33
74
60

Sixth hospital
27
25
15
12
15
24
67
51

From Figure 2, we can see that, in terms of patient’s age, the majority of patients are under 24 years old and over 60 years old and this proportion is more than 60%. This is because the teeth of teenagers and the elderly are not as strong as others. In the first grade, the teeth and alveolar bone are easily damaged. On the whole, the alveolar bone of male dental slices is more vulnerable to injury than that of females. The ratio of male to female patients in these hospitals is about 6.5 : 3.5. We also made relevant statistics on the treatment plans adopted by patients (3), as shown in Table 2.

Figure 2: Patient’s age and sex distribution.

Figure 3: Number of different treatment methods.

Table 2: Distribution of treatment methods.

Cone beam
3D model
CT image
Surface tomography
Image comparison
Computer image

First hospital
31
36
31
35
23
21

Second hospital
36
28
24
28
22
32

Third hospital
42
27
44
28
24
34

Fourth hospital
49
44
34
32
29
43

Fifth hospital
29
37
43
45
35
25

Sixth hospital
27
35
39
42
27
24

According to Table 3, we can see that patients currently have a variety of options for treatment of alveolar bone injury and it is not the first choice for patients to measure the alveolar bone based on computer image analysis. Through the survey, it is found that the preferred treatment method for patients is CBCT, which accounts for 30% of the surveyed population, and computer image analysis accounts for 12%. This shows that the method of measuring the alveolar bone of X-ray dental film based on computer image analysis needs to be strengthened.

Table 3: Treatment efficiency.

Cone beam
3D model
CT image
Surface tomography
Image comparison
Computer image

0–12
4.36
3.83
3.79
3.82
4.06
4.15

13–24
4.84
4.34
4.82
4.94
4.25
5.22

25–36
4.87
5.19
5.12
5.36
5.07
5.8

37–46
5.22
5.88
5.62
5.79
5.88
6.45

47–60
5.88
5.88
5.99
6.21
6.29
6.06

Over 60
6.44
6.9
6.41
6.87
6.91
7.7

4.2. Measurement Effect

Through the comparison of patients before and after treatment, we found a treatment plan with better treatment effect, and the treatment effect was digitized through the model to facilitate comparison. The specific value pairs are shown in Table 3.

From Figure 4, we can see that, in most cases, the method of measuring dental alveolar bone based on computer images is better than other methods. The efficiency optimization is about 17% and only 0–12. In the treatment plan of the age, the score is slightly lower than that of the CBCT plan, but it is also higher than other treatment plans, which shows that the effect of measuring infants and young children based on computer imaging is strengthened. We separate the parts in the measurement and calculate each effect separately. For the pros and cons of each technology, the specific data is shown in Table 4.

Figure 4: Patient treatment effect.

Table 4: Image analysis effect.

Cone beam
3D model
CT image
Surface tomography
Image comparison
Computer image

Clarity
0.211
0.242
0.207
0.235
0.251
0.292

Check efficiency
0.194
0.215
0.213
0.211
0.203
0.226

Treatment speed
0.195
0.233
0.187
0.228
0.197
0.253

Damage to teeth
0.243
0.189
0.205
0.188
0.215
0.294

False negative rate
0.231
0.215
0.237
0.224
0.241
0.107

Misdiagnosis rate
0.213
0.188
0.225
0.186
0.187
0.136

From Figure 5, we can see that, in the comparison of various indicators and parameters, the methods based on computer imaging measurement are leading other solutions, with a leading margin of 18%. In the case of errors and underreporting, based on computer imaging, the error of measurement is much smaller than other solutions, about 30% lower, which shows that the method based on computer image measurement can play an important role in dental diagnosis and treatment.

Figure 5: Various indicators.

4.3. Changes in Treatment

We have made statistics on the range of changes in the measurement methods of dental indentation bone in recent years and digitized the data through the model to make the data clear. From it, we can see the changes in the measurement technology over time. The specific data are shown in Table 5.

Table 5: Treatment changes.

2010
2011
2012
2013
2014
2015
2016
2017
2018
2019

Cone beam
2.01
2.22
2.2
2.47
2.24
2.06
2.19
1.92
1.97
2.41

3D model
2
1.8
2.23
1.8
2.5
2.31
2.16
2.02
2.1
2.36

CT image
1.91
2.3
1.87
2.41
2.09
1.89
2.2
2.09
2.48
2.05

Surface tomography
1.9
1.98
1.86
1.84
2.32
2.39
2.43
2.37
2.2
2.17

Image comparison
1.89
1.85
2.24
2.05
2.13
2.39
2.2
1.94
1.8
2.28

Computer image
1.46
1.38
1.59
1.77
2.09
1.87
2.44
2.47
2.51
2.54

From Figure 6, we can see that various treatment methods are basically showing an upward trend, especially the method based on computer imaging measurement. From 2010 to 2019, the parameters have nearly doubled, which shows that more and more many people have paid attention to the important role of computer-based imaging measurement in the measurement of dental indentation. We have also conducted related investigations on the evaluation of patients and doctors. The specific data are shown in Table 6.

Figure 6: Trends in treatment methods.

Table 6: Satisfaction survey.

Very dissatisfied
Not satisfied
General
Favorable
Basically satisfied
Very satisfied

Cone beam
3
12
25
33
18
9

3D model
5
7
32
21
24
11

CT image
7
5
39
19
22
8

Surface tomography
11
17
24
22
19
7

Image comparison
9
13
35
19
17
7

Computer image
2
5
23
32
21
16

From Figure 7 we can see that, for patient satisfaction, other treatment methods are not as high as measured based on computer image analysis [39]. This is because, based on computer image analysis, computer intelligent analysis can be performed without disturbing the patient. It can be obtained through related images, pictures for diagnosis and treatment, to improve patient’s satisfaction.

Figure 7: Trends in treatment methods.

5. Conclusions

The computer image analysis system measures the alveolar bone of X-ray dental film with digital image processing and computer graphics as the research background and reconstructs the three-dimensional shape of the oral cavity after filtering and segmentation according to the two-dimensional tomogram. It is the current medical visualization, one of the main research topics. Three-dimensional reconstruction can make up for the defect of a certain degree of information loss in two-dimensional planar images. Imaging the same object from different angles is conducive to data analysis and better recovery of three-dimensional information of the object, which plays a huge role in clinical diagnosis. A more intuitive understanding of the structure of the diseased tissue in the clinic, reducing the diagnosis deviation caused by the three-dimensional structure simulated by the doctor empirically, is conducive for improving the success rate of the operation; it can be used to guide radiotherapy to reduce the damage to the normal human tissue to a certain extent and make plastic surgery. And the result of prosthesis repair is more ideal, which promotes medical education, research, and even clinical diagnosis into a new era.

In short, dental alveolar bone injury is a common disease with a high prevalence rate. Some patients are prone to recurring attacks after being cured and eventually develop into major diseases. We should have a deep understanding of this common disease, strengthen health education, carry out active, reasonable, and useful treatments, and try our best to prevent dental alveolar bone damage.

Due to the unclear force value of the alveolar bone during the chewing process, it is impossible to determine the required mechanical properties of the alveolar bone in the specific defect area; the biological activity of the alveolar bone repair scaffold is still in the stage of in vitro experiments, and a large number of animal experiments are required. The later clinical application lays the foundation; the relationship between the degradation rate of the alveolar bone repair scaffold after implantation in the alveolar bone defect area and the rate of new alveolar bone needs further study; due to the small structural volume of the defect alveolar bone, in the case of ensuring the mechanical properties, porosity, and pore structure of the scaffold, it is necessary to further find a suitable printing formula to improve the image accuracy; the use of tissue engineering to repair alveolar bone defects is in its infancy, and a large number of experiments are still needed to transform it from basic research to clinical application.

Data Availability

No data were used to support this study.

Disclosure

Chunfeng Wang and Caigang Peng are the co-first authors.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Chunfeng Wang and Caigang Peng contributed equally to this work.

Acknowledgments

This study was supported by the Research Foundation of Education Bureau of Hunan Province, China (Grant no. 16C1148), the Research Foundation of Health Bureau of Hunan Province, China (Grant no. B2014181), and the Research Foundation of Administration of Traditional Chinese Medicine of Hunan Province, China (Grant no. 2017148).

References

Z. Yan and Z. Lv, “The influence of immersive virtual reality systems on online social application,” Applied Sciences, vol. 10, no. 15, Article ID 5058, 2020.

F. Ji, R. Wu, and H. He, “Sub-pixel positioning technology for image measurement of large-scale external objects,” Computer and Digital Engineering, vol. 45, no. 8, pp. 1647–1651, 2017.

G. Ma, Y. Zhou, Y. Li et al., “Computed tomography imaging measurement of Chinese lower thoracic spine transcortical bone channel nail placement,” Chinese Journal of Experimental Surgery, vol. 35, no. 7, pp. 1348–1350, 2018.

Y. Yu, J. Liu, and X. Han, “Analysis of 18F-deoxyglucose positron emission computed tomography/X-ray computed tomography multiple image modes of renal lymphoma,” Chinese Journal of Clinical Practical Medicine, vol. 11, no. 2, pp. 11–14, 2020.

J. Zhao, Q. Dong, and X. Zhou, “Imaging analysis of the relationship between liver tumors and portal vein in children based on computer-assisted surgery system,” Journal of Clinical Pediatric Surgery, vol. 16, no. 6, pp. 546–551, 2017.

M. Elhoseny and K. Shankar, “Optimal bilateral filter and convolutional neural network based denoising method of medical image measurements,” Measurement, vol. 143, pp. 125–135, 2019.

S. Ding and W. Jia, “DWI manifestations and DCE-MRI quantitative analysis of the activity evolution of brain parenchymal tuberculosis after treatment,” Chinese Journal of Medical Computer Imaging, vol. 26, no. 4, pp. 301–305, 2020.

K. Geetha, V. Anitha, M. Elhoseny, S. Kathiresan, P. Shamsolmoali, and M. M. Selim, “An evolutionary lion optimization algorithm‐based image compression technique for biomedical applications,” Expert Systems, vol. 38, no. 2, 2020, In press.

X. Zhang, D. Mu, J. Luan et al., “Study of body mass index and CT angiography data assisted breast reconstruction with superficial abdominal artery flap,” Chinese Journal of Plastic Surgery, vol. 36, no. 11, pp. 1210–1214, 2020.

S. Yang and Y. Su, “Application of close-range photogrammetry combined with unsupervised image cluster classification method in road paving pothole judgment,” Pavement Engineering, vol. 17, no. 3, pp. 59–66, 2019.

Z. Che, W. Meng, X. Peng et al., “Comparative study of multi-slice spiral CT volume scanning in facial nerve canal imaging measurement of congenital external and middle ear malformations,” Chinese Journal of Anatomy and Clinic, vol. 21, no. 1, pp. 1–5, 2016.

Y. Tang, W. Feng, W. Feng, J. Chen, D. Bao, and L. Li, “Compressive properties of rubber-modified recycled aggregate concrete subjected to elevated temperatures,” Construction and Building Materials, vol. 268, Article ID 121181, 2021.

M. Miao, X. Lin, Z. Cui et al., “CT imaging on development of the fetal spinal canal of T12 vertebrae,” Chinese Journal of Anatomy and Clinics, vol. 22, no. 3, pp. 177–180, 2017.

H. Zhu, B. Ye, W. Duan et al., “Preoperative three-dimensional image measurement combined with laser navigator-assisted puncture for transforaminal approach percutaneous endoscopic discectomy,” Journal of Spine Surgery, vol. 17, no. 1, pp. 17–23, 2019.

X. Yuan, D. Li, D. Mohapatra, and M. Elhoseny, “Automatic removal of complex shadows from indoor videos using transfer learning and dynamic thresholding,” Computers & Electrical Engineering, vol. 70, pp. 813–825, 2018.

T. Song, “One-dimensional line pattern detection system based on image and laser interference technology,” China Metrology, vol. 7, no. 437, pp. 96–98, 2017.

Z. Lu, H. Lu, X. Lin et al., “A new method for measuring the internal deviation of pedicle screws using X-ray imaging data,” Journal of Spine Surgery, vol. 17, no. 4, pp. 262–266, 2019.

M. Shi, M. Narayanasamy, C. Yang et al., “3D interpenetrating assembly of partially oxidized MXene confined Mn-Fe bimetallic oxide for superior energy storage in ionic liquid,” Electrochimica Acta, vol. 334, Article ID 135546, 2020.

S. Liu, R. Cheng, and Liming, “Application of improved canny operator in the evaluation of industrial image clarity,” Journal of Chongqing Normal University: Natural Science Edition, vol. 34, no. 2, pp. 86–90, 2017.

Y. Yang, L. Wei, and G. Wang, “3D imaging model to measure the best passage of S1 sacroiliac joint screw and its clinical application,” Chinese Journal of Traumatology and Orthopedics, vol. 21, no. 2, pp. 138–143, 2019.

S. Kamiya, S. Iwano, H. Umakoshi et al., “Computer-aided volumetric measurement of partial solid lung cancer based on CT: solid component size prediction prognosis,” International Journal of Medical Radiology, vol. 41, no. 4, pp. 113-114, 2018.

R. E. Jung, P. Grohmann, I. Sailer et al., “Evaluation of a one-piece ceramic implant used for single-tooth replacement and three-unit fixed partial dentures: a prospective cohort clinical trial,” Clinical Oral Implants Research, vol. 27, no. 7, pp. 751–761, 2016.

S. S. Francisco, A. D. J. Soares, and R. D. Murrer, “Evaluation of elementary education teachers’ knowledge on avulsion and tooth replantation,” RSBO, vol. 12, no. 1, p. 32, 2016.

A. A. Kamenskikh, T. N. Ustjugova, and A. G. Kuchumov, “Comparative analysis of mechanical behavior of the tooth pair contacting with different mouthguard configurations,” IOP Conference Series: Materials Science and Engineering, vol. 511, no. 1, pp. 3–12, 2019.

P. Toti, S. Marconcini, G. Enrica et al., “The influence of prosthesis design on the outcomes of tooth-implants immediately placed and loaded by means of one-piece titanium restoration,” Journal of Oral Implantology, vol. 44, no. 2, pp. 87–93, 2018.

S. Schnutenhaus and R. G. Luthardt, “Ceramic implants and potential advantages of a one-piece design,” Implantologie, vol. 25, no. 2, pp. 163–173, 2017.

D. Lekhadia, G. Hegde, and K. Sindhuja, “A modified three-piece base arch for en masse retraction and intrusion in a class II division 1 subdivision case,” International Journal of Orthodontic Rehabilitation, vol. 8, no. 2, p. 81, 2017.

Z. C. Muñoz, A. Diaz, E. E. Gómez, and Y. R. Gómez, “Soft tissues and bone healing response in impacted third molar osteotomies,” Revista Odontológica Mexicana, vol. 21, no. 1, pp. e29–e32, 2017.

S. A. Basheer, R. J. Govind, A. Daniel et al., “Comparative study of piezoelectric and rotary osteotomy technique for third molar impaction,” Journal of Contemporary Dental Practice, vol. 18, no. 1, pp. 60–64, 2017.

Z. Lv, “Virtual reality in the context of internet of things,” Neural Computing and Applications, vol. 32, pp. 9593–9602, 2020.

J. Hartlev, T. Klit Pedersen, and S. E. Nørholt, “Cone beam computed tomography evaluation of tooth injury after segmental Le Fort I osteotomy,” International Journal of Oral and Maxillofacial Surgery, vol. 48, no. 1, pp. 84–89, 2019.

Z. Lv, L. Qiao, S. Verma, and Kavita, “AI-enabled IoT-edge data analytics for connected living,” ACM Transactions on Internet Technology (TOIT), 2020.

B. Wang, X. Sun, P. y. Feng, C. x. Yan, and X. j. Jia, “Solution and verification of cutter position for machining split equal-base circle bevel gear,” Mathematical Problems in Engineering, vol. 2019, no. 3, pp. 1–14, 2019.

B. M. Andreasi, M. A. Lopez, L. Confalone et al., “Clinical outcome of a two-piece implant system with an internal hexagonal connection: a prospective study,” Journal of Biological Regulators & Homeostatic Agents, vol. 30, no. 1, pp. 7–12, 2016.

Z. Lv, L. Qiao, and I. You, “6G-enabled network in box for internet of connected vehicles,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–8, 2020.

S. Jeschke, C. Brecher, H. Song, and D. Rawat, Industrial Internet of Things: Cybermanufacturing Systems, Springer, Cham, Switzerland, 2017.

S.-B. Tsai, Y.-M. Wei, K.-Y. Chen et al., “Evaluating green suppliers from green environmental perspective,” Environment and Planning B-Planning & Design, vol. 43, no. 5, pp. 941–959, 2016.

S.-B. Tsai and K. Wang, “Using a novel method to evaluate the performance of human resources in green logistics enterprises,” Ecological Chemistry and Engineering S, vol. 26, no. 4, pp. 629–640, 2019.

H. Kim, “Investigating the mediating role of social networking service usage on the big five personality traits and on the job satisfaction of Korean workers,” Journal of Organizational and End User Computing, vol. 31, no. 1, pp. 110–123, 2019.